KELAS ANALISIS BIBLIOMETRIK | 25 FEBRUARI 2026

Okay, Putri, how much is this? Huh? The key. Oh, it means Mother forgot to go out. Oh my God, I forgot. Sorry, B. Check, check. Good afternoon. Assalamualaikum, everyone. Waalaikumsalam. Waalaikumsalam warahmatullahi wabarakatuh. Alright, thank you, everyone. Can you hear my voice clearly? Please allow me. Yes, it's clear. I apologize for the 5-minute delay. I suddenly felt unwell. So I had to take a moment to close my eyes, okay? My wife woke me up just now, it was quite sudden, okay? Alright, thank you. Please allow me, everyone, for the 5-minute delay, okay? Uh, everyone, please allow me to introduce myself. I am Ibas from Tumbuh Pedia. Uh, this afternoon, we will be learning together in the skilled bibliometric analysis class for research and scientific writing publications with our extraordinary speaker, Ibu Fitri Marisa, PhD. She is a young lecturer with a Scopus Age Index of 6, with 18 papers indexed in reputable journals. Today, Wednesday, February 25, 2026, from 1:00 PM Western Indonesian Time until completion. Uh, please allow me to greet you. Assalamualaikum. Good afternoon, Bu Fitri, Bu Risa. Good afternoon, Pak Ibas. Assalamualaikum. Waalaikumsalam war. Good afternoon, everyone. Alhamdulillah, Pak. Alhamdulillah. Yes. Are you well, Pak Ibas? Yes. Alhamdulillah, Bu. Uh, I was feeling a bit unwell earlier, Bu. For 3 days now, this, uh, what do you call it? Exhaustion. You have to, Pak. [laughs] Oh, yes, happy fasting for those of you who are fasting. Happy fasting also for Bu Risa. Yes. Alright. Uh, yes. Alright, thank you, Bu Risa. Uh, please allow me to greet the participants briefly, okay, Bu, and convey a few things about this afternoon's class. Ladies and gentlemen, for those of you who have joined, please allow us to convey something. You can change your Zoom profile with the format: Name UND City of Residence or your Institution. And next, the Tumbuh Pedia team has sent, uh, what do you call it? A virtual background that has been sent to the WA group, okay? Or if there are any difficulties in downloading it, uh, you will be assisted by the Tumbuh Pedia team to resend it in the Zoom meeting chat column this afternoon. Then, ladies and gentlemen, we will first give Ibu Risa the opportunity to present her material, and then it will be followed by a discussion and Q&A session. God willing, this class will last approximately 120 minutes from now, okay, Bu Risa, okay? Or approximately 2 hours, okay? Uh, then, ladies and gentlemen, uh, what do you call it? Attendance will be sent by the Tumbuh Pedia team at the end of the class. Therefore, hopefully, all of you can attend this class until the end and be given ease in understanding, uh, be given complete understanding because you are attending the class until the end. Then, if there are any issues with the signal or anything else on Zoom this afternoon, you can switch to the YouTube live stream, the link for which will be sent by the Tumbuh Pedia team. What about the certificates? Certificates are usually sent by the Tumbuh Pedia team after, uh, usually two or three days after the event is finished. Like that, okay? Alright, ladies and gentlemen, let us begin this afternoon's class by together reciting a prayer according to our respective religions and beliefs. Please pray. Amen. Yes. Hopefully, the prayer at the beginning of this session will provide ease and smoothness for this class, especially, and ease for all of you in understanding everything that will be conveyed by our speaker, Ibu Fitri Marisa, PhD, a young lecturer with a Scopus Edge Index of 18 indexed papers in reputable journals. To Bu Risa. Please, perhaps the class can begin. Yes. Alright, thank you, Pak Ibas. Uh, I will begin. Bismillahirrahmanirrahim. Assalamualaikum warahmatullahi wabarakatuh. Waalaikums, good afternoon. Good afternoon, everyone. Nice to meet you. I am Risa Arisa or Fitri, it's all the same, you can call me by my name. Uh, previously, perhaps let me introduce myself. I am a lecturer from Agama Malang, uh, as an academic content creator, uh, where my social media and YouTube focus on academic content creation. So, if you, uh, happen to need resources, perhaps this can be one of the resources. You can visit Fitri Marisa Channel. Well, thank you, Bu Musedia, for inviting me, always inviting me every month, Pak Ibas, to share with you all. Alright, ladies and gentlemen, uh, previously, perhaps I need to convey, uh, for a common understanding, that I have been assigned by Tumbuh Pedia to share with you all regarding, uh, how to find research gaps using bibliometric analysis. Yes, bibliometric analysis is indeed a relatively new technology for us to map trends, okay? To map research trends, where the source is from published publications in journals. Now, uh, if we have already been able to perform the data extraction process and visualization, this analysis can function in two ways. Its main function is to generate ideas. To generate ideas, to find research gaps that go through or follow trends, okay? Trends that will be presented by bibliometrics, and also, uh, can be continued to create literature review journal articles. This means that this training is a stimulus to move towards that. However, this training does not go as far as producing the journal itself, explaining the steps, because of the time limitation. So, the focus in our research in this activity is how we apply bibliometric analysis, how we find ideas, then visualize them, then from there we can formulate research gaps. So, that's perhaps, uh, what I need to convey, ladies and gentlemen. Uh, alright, without further ado, please allow me to share my screen for the material. Now, so, ladies and gentlemen, as I mentioned earlier, our activity for the next 90 to 120 minutes is theory and practice on how to find novelty and research gaps, specifically focusing on research gaps. If novelty, then later, uh, novelty will certainly stem from research gaps, right? So, we find research gaps using bibliometric techniques. Now, when we talk about bibliometrics, of course, we will encounter applications or facilities related to journals or journal data presentations. Because bibliometrics is developing rapidly, journal applications or journal database websites are also using bibliometric analysis. However, we will limit the bibliometrics we will learn today to, uh, entering the pioneers. So, we will learn how to extract data or get datasets. Then we will analyze them directly using a tool, which is VOSviewer. Now, of course, are there other tools? Yes, like BiboShiny, and others, or those already provided by journal datasets. But with VOSviewer, uh, when you enter other applications, you won't have difficulty because this is indeed the pioneer of bibliometric visualization analysis, which is VOSviewer. Then, the datasets that we will, uh, extract, I will demonstrate with three datasets, uh, on three, more precisely, three platforms. The first is Publish or Perish. This is a platform that can retrieve datasets from journal databases of various types of journals, subject to terms and conditions. We will practice this. And the second is Dimensions. This is one of the extraordinary platforms for us to get journal data, datasets, and also free journals. We can get millions of reputable journals through Dimensions. Then, we will also practice finding datasets using the Scopus database. Now, of course, Scopus is limited. So, if we want to get Scopus datasets, we must have access. Of course, this access is not free, ladies and gentlemen. Now, uh, then, ladies and gentlemen, if, uh, from this training or perhaps developing to learn other things, then perhaps you can visit my channel, Fitri Marisa Channel, on IG, YouTube, or TikTok. It has the same name, Fitri Marisa Channel. Alright, ladies and gentlemen, let's start, uh, theoretically, about bibliometrics. So, why is bibliometrics important, okay? Uh, if we, uh, imagine when we are searching for articles to find ideas, for example, we want to, of course, when we conduct research, we must be able to formulate a strong research gap, right? Now, if the usual way, for example, we search for journals on Google Scholar, we search on Scopus and others, and then we find, for example, a group of journals that have an idea that strengthens that idea, then a research gap is formed. However, with bibliometrics, it becomes that easy to formulate it. So, formulating a research gap can be done in minutes, okay? With the strength of, uh, the justification of related journals in bibliometrics. Therefore, why do we need bibliometrics? Because bibliometrics can identify research trends through analysis, collaboration, and keywords. Bibliometrics can also map the development of science over time. Now, why map the development of science over time? Besides being up-to-date, it can also be generated from various datasets, various meta-data from journal databases, such as Scopus of Science, Google Scholar, or others. And certainly, bibliometrics can detect research gaps. Through, uh, the analysis of the patterns produced, we can analyze or detect research gaps and see areas that are less explored in a field. Now, in searching for research gaps that lead to novelty, generally, conventionally, we do it like this: review articles after downloading them, then summarize them, then produce findings, then analyze them as research gaps, then proceed to novelty. But by using, uh, bibliometrics, we can use tools, namely the VOSviewer tool that I mentioned, VOSviewer, perhaps others, which are based on text mining. And one important thing here, a tool that is also no less important in the bibliometric analysis process is citation management. So, ladies and gentlemen, you must, not must, but it is highly recommended to use citation management because it will help in organizing the citations generated that are relevant to the research gap. You can use Zotero or others. So, from this analysis, a research gap will be produced. Now, of course, if we compare, this work will be very helpful if we use bibliometrics. Whatever the tool, however sophisticated the tool, ladies and gentlemen, the key to the success of bibliometric analysis is still the suitability of the keywords. So, the keywords you search for or use in the process of searching for article metadata greatly determine the results of the bibliometric analysis. Therefore, this suitability depends on our ability, our initial ability. That is, how deep is our initial knowledge of the topic? Of course, if we have a deep understanding of the topic, then the keywords will be relevant. The deeper we understand the topic, the more relevant the keywords will be, so the domino effect will also affect the visualization, and what is no less important is the completeness of the metadata. So, complete metadata, including title, author, DOI, abstract, and others, is very crucial for significant results from bibliometric analysis. Currently, the most complete for bibliometric analysis is metadata from Scopus. But that doesn't mean others can't be used. They can still be used, ladies and gentlemen. But in terms of completeness, if asked which is the most complete for metadata, Scopus provides it openly and easily. So, the tools we will use to search for metadata here are POP, Dimensions, and Scopus. This, uh, I forgot to include it here. So, there are three that we will practice. Specifically for POP or Publish or Perish, we must install this application because it needs to be installed on our laptops. It is desktop-based. So, from that application, we can then search. You can install it here, or you can just run it from the materials that have been provided to Tumbuh Pedia. The Tumbuh Pedia team will definitely provide those materials. Then you can directly install from the POP file that I have provided. For Dimensions, go directly to the Dimensions website. AI. And for the visualization tool, we will use VOSviewer. VOSviewer just needs to be downloaded and then opened without installation. I have also provided this in the material. You don't have to download it here. Just open my material file. There is the master file for VOSviewer. You can run it directly. Alright, ladies and gentlemen, let's get straight to the practice. This time, we will practice finding metadata first, and then we will learn about, uh, this, the visualization analysis. After that, how to read and analyze it. Alright, ladies and gentlemen, please allow me to open the first file. The first file I will open is POP. Before that, perhaps I need to show you. So, the material I will share, you will receive from Tumbuh Pedia, in addition to my presentation material, also master files. So, the POP master file is for Publish or Perish, to find metadata, then VOSviewer. And here are some example datasets that you can practice with. In this live session, in this training, I will demonstrate with the same file. So that the practice is synchronized for you all. I have also completed some files from the results of our practice. You can compare the results as well. Alright, ladies and gentlemen, now we will proceed. I will demonstrate. You can just observe first, and then perhaps repeat it after this training so you don't get confused, okay? So, the first step is to understand how to find a dataset, okay? The first way I will demonstrate is to find a scientific publication dataset through POP Publish or Perish. This needs to be installed. You install it. If it's successful, or perhaps one or two of you will be asked to update Java. Just follow it, next, next. It's not a problem, it's not an error. Just follow it. If it asks to update Java, update it. If it's done, then reinstall. If it's successful, then the result will be like this. This is the platform for searching, for searching journals, scientific articles, journals, proceedings, and others, through this one platform. The articles that can be produced, whose metadata can be retrieved, can be from several sources here, such as Crossref, Google Scholar, then PubMed, and others. However, for those that require login, it is recommended not to use POP because there will be more facilities if you go directly to the website. For example, Scopus. We can get data from here, but here you need to log in with your Scopus account. It will be different if we go directly to the Scopus website. So, it is recommended to go directly to the website. So, usually, for articles, for metadata that we will search for through POP, it is generally metadata generated from Google Scholar. Now, how do we do it? We will practice. But before that, what I need to convey are these parts. So, the top part contains items from a set of search results. Excuse me, Ibu. The display hasn't changed. Oh, yes. One moment, Ibu. Let me check. Has it changed, Ibu? Here I am displaying VOSviewer, displaying POP. Yes, it has, Ibu. It has. Oh, okay. Thank you. Thank you, everyone. My apologies. Alright. So, the POP display, the very top part contains a collection of our metadata search results. So, for example, if the keywords we are searching for are about, for example, gender equality, then all the resulting journals and proceedings from that search will be in one file here, okay? The details are below. The details are below. Then, this is for selecting which database we will search keywords from. Here are Crossref, Google Scholar, and others. Then, on the right side is the citation matrix. This is usually useful if you want to proceed to a literature review. If we want to create a literature review article. From this data, it will be very helpful in the grouping process. Then, the bottom part is the details of the found articles. Now, let's try to search. Here, we are focusing on searching for articles within the Google Scholar database. We just click like this. Here, options are presented for entering keywords or criteria that we will search for. Generally, what we search for is entered here in the title or keyword. It can be one or both. So, for example, if I am searching for articles or want to find trends, okay? Research trends related to mental health, okay? Then here I will put keywords, for example, here, mental health. So, mental health means I am looking for articles whose titles contain the words "mental health." Then I want to focus more, make it more specific within the keywords, okay? So, for example, let's say it's not in the title, but the keyword mentions mental health. I want that to be included in the search. Then we can put this as mental health. If there is more than one, for example, I want specific mental health also related to, uh, adolescents or adults. Then we just separate them with a semicolon. Then we add here, adult, okay? Then, or something else, okay? Add and so on and so forth. This depends on what keywords you want to add, according to those keywords. It is quite limited in Publish or Perish, but for Google Scholar cases, it will be very helpful. So, the datasets in Google Scholar will be very helpful if we use Publish or Perish. Then, for publication name, we can specify it. For example, we want to specify, I only want to search for journals, so here we write "journal." But if we don't specify, then all articles, as long as they contain the keywords, are okay. Let's remove this. Similarly, for authors. For example, we specify that there is an expert in our field who has produced hundreds or thousands of journals, then here we specify based on this author. That is also possible, or more than one author is also possible. Like that. Then, the year is the year range. For example, I want to specify the last 5 years, so 2021 to 2026. Like that. If confirmed, then we set the maximum result. If we click here, it's a maximum of 1,000. If, uh, if for searching, to maximize, we maximize it up to 1,000. But if this is just for practice, okay? Because 1,000 will take a long time to process. So, this is just for practice, ladies and gentlemen. So, here I set the maximum result to 20. If you practice for real, it's best to maximize it. After that, we click search like this. Then we wait for the results. This is what is produced. So, all articles produced with these keywords, okay? These keywords within this year range. What we need to pay attention to here are these columns, ladies and gentlemen. Usually, when we get this, we definitely need to know which article has the most citations, right? We can see this in the "Cited by" section. Click. Now, if we click "Cited by." Here, "Cited by 74" means this article has the most citations. We can see how many per year and what its ranking is. This rank means the relevance of the article to the keywords. So, it's not directly proportional if the citation count is high and the rank is high. No. Because it all depends on the relevance of the keywords, the year, and also the publisher. What else needs to be noted is that not all articles here can be obtained because it's not filtered whether they are open access or not. If we want to know if it's open access or not, we can click on one of them here. For example, we click here. Here, on the right side, there is "Open Publishing Version." There is "Open Full Text." Usually, if it's like this, this article means it's full access. We can just click, for example, for the "Open Full Text Version," we click. Then here we can directly view the PDF, ladies and gentlemen. But if there is no full version here, then it means there isn't one. It means it's not open access. So, we must have access to the journal because journals can be open access or subscription-based. What else is important here, ladies and gentlemen? Here we can see the citation metrics. This is very useful for us to have initial knowledge that the trend of articles related to this topic is in which year. We can click "Year" here. Now, it will be sorted by year. Now, here we can check. For example, let's say we compare the years 2025 to 2024. We can check. Let's confirm this first. We press Ctrl+A like this. Then we right-click. After that, we uncheck all. Let's neutralize everything first. Then we will compare how the publication in 2025 to 2024 compares, okay? 2025 and 2004. Let's group the 2025 ones first. For example, there are three here. We click the file, uh, what articles in 2025, then we check, okay? We check, then we look at the citation metrics. In the metrics, especially what we need to look at is the index. The H-index here is two, and the J-index is three, okay? Then, in the citations per year, it's 33, citations per paper are 11, okay? Then we can note this down. For literature review purposes, this is very helpful. Meaning, for literature review article purposes. Then we compare this. We change the checkmarks. We go to 2024. Now, in 2024, we check. Now, let's look. Here, there are four papers. Then for citations, it's 9, okay? Citations per year, 19, citations per paper are 9, and the H-index is 4, G-index is 4. Meaning, if we compare, for example, the research trend related to mental health, when compared between 2024 and 2025, it tends to decrease because the citations are more in 2024 than in 2025, for example. And so on. After that, we save this result. The method is to click on one of them, especially focus on the first row. Then right-click. After that, Ctrl+A, okay? Ctrl+A so it's all highlighted. Then "File Ris," meaning we are saving the metadata, we save it in a file. There are many options here, like CSV, and especially RIS. We can use CSV and RIS, it's the same. Here, for example, I will use a RIS file. After that, we can save it, okay? We save it as "example POP result," okay? Enter. Then one file will be saved in our metadata search results. So, "example POP result" means we have obtained it and we have practiced how to search for metadata using POP, and the metadata is from Google Scholar. Now, let's save this metadata first. We will search again, ladies and gentlemen, for metadata using Dimensions. Usually, among us who might be looking for metadata in Scopus, or perhaps your students, ladies and gentlemen, if you are in a lecturer position, have difficulty finding data in Scopus, where access is limited, and searching Scopus is not possible, then Dimensions is an alternative. Dimensions is an AI-based platform that can present search results from journal metadata and journals. So, first, ladies and gentlemen, you need to log in. So, I will repeat it here, okay? So, when you access Dimensions, the first page looks like this, ladies and gentlemen. Then you have to register. So, go here to login. If you have already registered, then the display will be like this. But for those who haven't, just follow along. So, follow the registration process. I will try to demonstrate it here. For example, here I go to Dimensions. Like this. Like this. Then, when you click login, okay? If, uh, in my other browser, I set it to automatic, okay? Then, when you click login, you will be asked to enter your email. Then, when you enter your email, there is "Register Now." Register for now. Enter your active email address here. Then next, so that you will be asked to open your email for confirmation. After that, from that confirmation email, you can open the login. The username is your email address. The password is the password you entered. So, the result will be this. After that, we can have more freedom or more facilities to search for metadata in Dimensions. If in POP, as I said, each has its pros and cons. POP is a single platform where we can input according to this. But the criteria will not be as many as in the platform itself, not as many as in the platform itself. Whereas Dimensions is within its own platform and it's free. If you want more, for example, to search for patents and so on, then it must be paid. But for the purpose of searching journal metadata, it's sufficient. Searching for journals is sufficient. It's not necessary to pay here. Okay. Now, here's how, ladies and gentlemen. You just go here, go to the input menu for keyword searching. Here, we can directly utilize the keyword search process using OR. We can use that. So, for example, I want to search for data about mental health, as before. So, I want to search for mental health data that is also related to adults. Then we put quotation marks "mental health," then "AND," then "adult." This means I am searching for articles that contain "mental health" and "adult." Or I can add more. I want to search for "mental health" and "adult" and also those containing "Gen Z" or, you know, Gen Z or adolescents. Then I can create a formula like this: AND adult OR generation Z. Like this. This means, if it's like this, then what I want to search for is mental health that contains or contains generation Z. Because AND means everything must be met, while OR means at least one. Then here there is a choice: full data, title, and abstract. Usually, we focus on title and abstract so it's easier, okay? More filtered. If done, then we just press enter. We will see the results. This is indeed a bit slow because it also adjusts to the search results and also to the internet connection, ladies and gentlemen. So, the result is 161,488 articles. We haven't filtered these articles yet, ladies and gentlemen. Here there is dataset, grant, patents, and so on. So, what we want to search for is to filter it. The filtering is here. We filter by publication year. We can enter the publication year. Let me try to change it, ladies and gentlemen. Okay. Now, for publication. Now, there are options here, ladies and gentlemen. For example, I only want the last 3 years. So, the last 3 years, we check this, then "Add to Search." So, it will be filtered according to that year. Now, from 161,000, it becomes 39,000-something. This is based on publication type. Usually, for publication type, we want to limit it to articles only. We don't include this, okay? So that the search results are more relevant. Add to Search again. We can also limit it from here. There are 36,000-something. Earlier it was 36,400-something. Then there is usually open access. We want to limit it to all open access or all. So, usually, so that the articles we search for can be downloaded, we choose all open access. Then "Add to Search." This publication should increase. Now, it becomes this much. This is the search process. For literature review purposes, ladies and gentlemen, you can note the filters, which can then be included in PRISMA. If done, then we can export. Let's assume this is finished. For example, because the keywords I searched for were not too specific, the results were thousands, ladies and gentlemen. But when you practice, you will search for something specific according to the category you want to search for. If done, for example, we assume it's done. Then we can do this: save or export, then export results. Here there are options: CSV, then export reference manager. If we use CSV, usually the result will be relevance per author. If we use RIS, then it will be per keyword or themes related to that trend. So, choose "Export for Reference Manager" or RIS. After that, click export. If done, there is a notification. We just click, and the result will go here. Then we just download the result. The result can then be used as material for the VOSviewer analysis process. So, we have two metadata obtained from two sources. Now, the next step is to find metadata through Scopus. Now, this is the most important thing, in my opinion. So, whether you like it or not, in my opinion, ladies and gentlemen, when you are searching for research gaps, it is best for you to use articles, uh, metadata generated from Scopus. Please allow me to open access to Scopus first, ladies and gentlemen. Please allow me, ladies and gentlemen, I am using access. But our condition is not yet, uh. Bu, excuse me, it seems your voice is still breaking. Is the signal weak or what? Oh, yes. One moment, Pak. Please allow me to try changing this, Bu. Pak Ibas, can you hear my voice now? Yes, Bu. It's clearer, Bu. Yes. Oh, okay, okay. Thank you, Pak. My apologies, the signal at the campus is like this. Alright, alright. Ladies and gentlemen, I will now open Scopus. This time, we will search for metadata generated from Scopus. Ladies and gentlemen, of course, you need to get access to be able to enter Scopus. So, we recommend, ladies and gentlemen, when you are truly looking for research gaps, it is best to have a comparison, so from Google Scholar, then from Dimensions, then from Scopus. From there, the trends will be different with the same keywords. The trends will be different, or perhaps the trends will be related, which will strengthen it. But for literature review purposes, Scopus datasets are prioritized, and others can still be included as reinforcement. So, we will try to search, okay? For metadata, for example, as before. So, here we put "mental health." Then "Add Search." Then we search for "adult." Then we add again, OR Gen, or Generation Z. Like this. After that, we click search. So, here it will produce this many, 233,000-something articles. This metadata, of course, must be filtered. We filter it first, like Dimensions, by year. We set the year range, for example, the last 5 years, as before. So, 2021 to 2026. After that, click the arrow to filter. So, it results in 104 and so on. Then there are many options here, ladies and gentlemen, subject area, whether to filter or leave it. Then document type, as before, we can make it only articles, or reviews, or book chapters, and so on. For example, I only want articles, so click "Limit To," so that the results will also change because they have been limited. Then language, don't forget, we focus only on English articles, and click again, so the results will also change. And so on, ladies and gentlemen. So, here there is keyword, whether to specify it, then country, theory, source type, author name, or perhaps we want to specify, uh, for figures, for example, 10 figures related to this theme. In Scopus, we are given those options. Then there is publication stage, affiliation, funding, and so on. There is open access, as before. So, if we choose "All Open Access," then only open access articles will be included in this search. We can "Limit To." If done, for example, let's assume it's done. The search process is finished. Then we save it. We click this "All" section. After that, we export. Here, ladies and gentlemen, you must log in. If this is a login that is different. So, usually, ladies and gentlemen, if you log in to Scopus not to download data, you are asked to log in. At the time of logging in, we must ensure that the login process is complete before we can export. So, this is different from the login process earlier, ladies and gentlemen. To access this, we must have access to download the metadata, which involves payment. Now, we export. For Scopus, it is best to export in CSV format so that it is complete and can produce all the metadata obtained from the journal. So, CSV. After that, here it is explained that we can save all documents on this page or limit it to documents from one to a certain number. These are options according to what you are searching for. Usually, "All documents on this page" is for practicality, for example. Then, we select all so that all the datasets within the journal's identity can be saved in the metadata. Then we export. This also depends on the result. Because I chose "All," and there are hundreds of thousands, this means 10 documents for CSV, because there was a limitation earlier. But in practice, it won't be this many because it's filtered. If done, then we just wait, ladies and gentlemen. If done, it will be saved in a CSV file. That CSV file is what we can then use for analysis. I have already provided the practice material using metadata from Scopus. I have also saved it as CSV, and also as RIS. You can practice. Alright. Now, let's move to the next step, which is the step of installing that metadata, reading that metadata in VOSviewer. VOSviewer looks like this, ladies and gentlemen. So, you can open it from the file. This is the VOSviewer file, which will then produce this application. This application, before I explain, consists of three layers. So, the visualization display will consist of three layers: network visualization, overlay, and density. The network will present the relevance, the visualization of relevance between themes, between the themes of the topic we are raising. The overlay will be related to the year, to the recency of the publication year. The density is the strength or how trending the research related to this is. That is in the density. Then, on the left side, we can see each cluster produced, how many items and which groups are there. It can be here. Then, the right side is for properties, for changing the display, changing colors, and so on. While this area is the workspace. So, the analysis results will be here. Alright, let's try, ladies and gentlemen, to practice. First, we will try to visualize the results from POP, meaning the results from Google Scholar. By chance, what I practiced earlier was on the topic of mental health. But what I am demonstrating here, what I am using as an example here, is on a different keyword, the keyword "gamification." So, we are not focusing on the theme, we are focusing on how to read it. Alright. First, we go to "Create." After that, because this is data from Google Scholar, then what we search for, what we select here is "Create map based on text data." Actually, it doesn't depend on whether it's Google Scholar or not, ladies and gentlemen, but it depends on the file type. Because this file that we obtained from Google Scholar from POP is a RIS file, then we choose this. So, if your file is a RIS file, you choose this one. Create map on, create a map based on text data. After that, next. After that, you choose "Read data from reference manager file." Here there is RIS. After that, click next. Choose RIS. Here there is a choice of RIS. Because our file is a RIS file, we choose RIS. Now, this is an example of POP metadata. After that, we click OK. After that, next. After that, here there is a choice. We choose which fields: title and abstract, or title, or abstract. Usually, we choose this. Then next. Extract. Then we click next. Now, this, we will discuss this. Let's just click next. After that, here there is a threshold, ladies and gentlemen. Now, what does this mean? Here it says "Minimum number of occurrences of term 10." So, this is the default recommended value. Meaning, what will be displayed in the analysis is if a keyword appears 10 times in the analysis. If it appears 10 times in the analysis, then it will be included in the visualization. Now, if we set the threshold higher, for example, 5, uh, sorry, raise it to 15. What happens? If I click next, then the result is not 15 anymore, but 8, because the filtering is stricter. So, words that do not meet the criterion of appearing 15 times will be eliminated. Now, if done, then we click next. If the RIS file recommended by VOSviewer is displayed as 60%. 60%. So, out of 8, 5 are recommended. Is it okay to use all of them? It's perfectly fine. This is just a recommendation. So, for example, we produce, we want to use all five, it's okay. Click next. Then, these are the eight terms or eight keywords that passed the selection. Now, this. Now, ladies and gentlemen, you can select whether these are related. For example, because I searched for the keyword "gamification," so the keywords I want to search for the trend of gamification in education and business. So, the keywords are gamification, education, and business. Then, here, here is the depth of our understanding of the topic. It is very important because from here we will know which ones are not used. For example, here, if I feel that "use" is not used, then I will remove it from the analysis. Then all others are used. So, I click finish. If finished, then we produce this. Okay. We will pause this for now to read, ladies and gentlemen. We will practice for Dimensions and, uh, this, for Scopus. Then Dimensions, you can practice. Now, this. Now, this. So, this, we will have a session to understand this. Now, alright. Uh, now we will try, if the data is generated from Scopus, we file. File, create. Then, because the Scopus file is CSV. Then, of course, for Dimensions, because it's RIS, it's the same as this, ladies and gentlemen. So, I will skip it. For Dimensions, it's the same as the first method, ladies and gentlemen, using Create, map on, map based on text data. But, uh, I am deliberately differentiating it. I will

Practice two different ones. Well, Scopus because Scopus's file is a CSV file. So, earlier, for VOP and dimension, it's the same, right? FIS uses the third category. Now, if Scopus's metadata uses CSV, then which category do we use? This one. If it's a Scopus file, we use this category. Now, after that, we click next. After that, we choose the second one as well. Choose Scopus. This is Scopus. After that, next. And we choose Scopus. After that, we search for the file. Well, here. Okay, next. Now, we wait for the results. Yes. Now, this is the difference, ladies and gentlemen. Now, let me enlarge it first. Oh, just a moment. Oh, it can't be done. Okay. Alright. So, when you, ladies and gentlemen, earlier, I practiced, ladies and gentlemen, please pay attention when I practiced for the bob. So, this RIS file only has the type of analysis limited to abstract and title or title abstract. Now, we are presented with many choices. Why? Because Scopus data is the most complete, so we can freely perform analysis from a certain perspective. We can visualize how co-authorship or the trend of co-authorship of this research is. We can choose co-authorship. But if we want it like before, we want to see how each term interacts, we can use cocurrence, if citation, and so on. Now, here too, we are given choices, there is the unit of analysis, there is all keyword, there is author keyword, there is index keywords. Then we just choose. Okay, generally, we just take the general one. The general one is that we want to see the research trend according to its subtopic, so we choose cocurrence. Then we can use all keywords. After that, next. After that, it's the same, right? The meaning is the same as before. So, the threshold that we maintain, for example, we use five, the same. Now, now this is the difference, ladies and gentlemen. So, if the file is from Scopus, it is not recommended to use 60%, but everything is 100%. Now, after that, we click next. Now, these are the 86 keywords that are included in that threshold, right? Now, we can confirm which ones need to be eliminated and which ones do not. Now, you, ladies and gentlemen, can check one by one. You, ladies and gentlemen, also consider the occurrence and total link strength. Now, the point is, we will discuss this later in the analysis discussion. After that, we click finish. Now, now, before that, ladies and gentlemen, the question is, for example, if we are hesitant, there are many terms here, are there any redundant ones or others, or how? Especially the redundancy, for example, the terms are actually the same but here they are different, or maybe the terms are actually this, but why do they appear differently here, affecting the significance of the visualization. Then we can repeat it by filtering keywords. For example, let's take "curricula" as an example. "Curricula" should be "curriculum" not "curricula", for example. Or maybe there are terms that are actually the same but it turns out they appear in different notes here. Then we can do the process again. Like before, we repeat. We repeat, we click next. Then, now, here when we click next, up to here. Now, we can right-click and then export selected keywords. Now, this is what is called a thesaurus. So, the function of a thesaurus is to improve keywords. That is, to improve them if there are duplicate keywords or they appear in two keywords. It's better to have one, so we can fix it in the thesaurus. Maybe it's better to look here first, for example, we can see the keywords here, ladies and gentlemen. We click keywords. Now, here we check. Now, for example, are there any here that look different, or maybe look like they can be. Now, for example, "game based learning" and "game-based learning". But actually, they are the same, right? But they become two. The visualization results will also be different. For example, or "high education" and "higher education". But actually, they are the same, but they become different. Now, we can combine them. The way to do this is to right-click anywhere in this cell. Then export selected keywords. After that, we create a file, for example, I'll create it here so it's different. Thesaurus 5. Okay. Now, we stop here for now, but we don't need to close it, ladies and gentlemen. Let's focus on opening the thesaurus file. Now, this is Thesaurus 5, we open it. Now, we open the thesaurus. Now, the result is this, right, ladies and gentlemen? Now, after that, let's fix the keywords first. The way to do this is to open the Excel file first. We open the Excel file temporarily for storage, for temporary processing, so that we can organize it more easily. Now, okay. This is Excel. Now, let's open the thesaurus first, Ctrl A, copy. After that, we move it, we copy it here, Ctrl V. Now, here. So, the purpose here is to ensure that the keywords are more filtered. Now, what needs to be removed here is the ID, then the occurrence, and the total linking. We delete. Now, like this. After that, we create one more column here. We change "keywords" to "label". Label, with a capital L. Now, we add one more column here. Replace by. Replace by. Now, for the example that is already done, it looks like this. Like this. Label, replace by. Now, let's go here. Now, here. Now, we want to remove one of them, ladies and gentlemen, from the duplicates. So, for example, "business education" and "business education", which one do we use? Right? The one we use is "business education" without "s", for example. So, here, within "business education", we add "replace by business education" like this. Then we look for others, for example, for "game base" here. "game based learning". Then "game base". Now, for example, they actually have the same meaning, "game based learning", for example. So, we make it here, "game base" is replaced by "game based learning", for example. Like that. This is just an example, ladies and gentlemen. Then we look for others, for example, "high education" and "higher education", which one is used. For example, the one used is "higher education", so "higher education" replaces "high education". Now, this is done, let's assume it's done. Then the next step is to remove the non-duplicates. Yes. So, we delete this, we delete all of them. We keep the replacements. Right? Okay. Now, we delete. Now, now we make sure it's deleted. Now, if it's done, we copy it again. We copy it again into this thesaurus file. We save it, we delete it, we replace it. After that, we save it. We save it. Now, like that, this is done for the thesaurus file 5 for filtering. After that, we go back. We go back, go back again, go back again. Now, view, future thesaurus file, optional. Now, we apply this. After that, we click Thesaurus 5. Next, next, next. Now, after that, finish. Now, so it's more filtered. So, "higher education" is no longer there, "high education". Then, whatever we have fixed in the thesaurus file is applied here. Now, like that, ladies and gentlemen. Now, we will learn how to read. Well, I'll go back to the slide. I'll go back to the slide on how to read. Well, ladies and gentlemen. Now, how to read the visualization results. Now, this is the VOSviewer result that we tried earlier. For example, this is what I'm highlighting one of the cells. Now, if we look at the VOSviewer result, it's like this. But if we highlight it, it's like this. Like this. Okay. So, if you, ladies and gentlemen, see this, the most important thing to understand here is network visualization. So, network visualization is a visualization of bibliometric analysis that provides information on the relevance of keywords related to the theme. So, these are the keywords from published research, ladies and gentlemen. So, we will know which keywords appear frequently, which means how much research related to this is relevant and to what it is relevant and how it is grouped. The grouping is within items. So, these colors represent clusters, ladies and gentlemen. So, this blue cluster, this green cluster, red, and so on. Now. Now, how many clusters are there? Ladies and gentlemen, you can see here. So, this visualization produces 8 clusters and 83 items that we have filtered, with 1219 links. So, the links are these, these links. If items were these, these. Then total link strength means the strength of the relationship between keywords is the total link strength. Now, this is calculated by VOSviewer from bibliometrics, which calculates it. Okay. If you, ladies and gentlemen, want to know who the members of cluster one are, sometimes we get confused looking at this blue, what is it? Ladies and gentlemen, you can see here, cluster, what? Cluster one has business, business management, and so on. Now, "high education" is no longer there, right? It's "higher education". Then there is also an overlapping cluster. An overlapping cluster is like this, it's in blue, but why is it purple? So, for example, if we look at semantic review, this is gamification, but why does it become blue as it gets closer? So, it's actually strong in the semantic review cluster, but it's related to gamification, generally. So, research related to gamification's semantic review is quite strong here, for example, ladies and gentlemen. Next, I will go back to the light. Okay. That was it. Now, there are three terms that are important in bibliometric analysis: occurrence, link, and total strength. Now, occurrence is the number of times an item appears in a document. Now, what is the relevance? The relevance is that occurrence indicates popularity or dominant topics. Now, there is also a link. A link is a node directly connected to several other items. Now, what is the relevance? It indicates connectivity, for example, co-authorship. If, for example, for visualization, it's co-author. If we are talking about keywords, then it's keywords. So, it is very possible, ladies and gentlemen, that we can also do author analysis. So, it depends on our choice for VOSviewer earlier, right? There are choices, there is cocurrence, there is author. Then there is total link strength, which is the total strength of the relationship or the frequency of co-occurrence. Now, this indicates how strong the connection is with other items. Now, let's break it down one by one, for example, as an example of occurrence. Let's say, for example, the occurrence in the visualization result is 254. Now, what does this mean? It means the keyword or item being analyzed. For example, the keyword, author, or institution appeared 254 times in the entire dataset being processed. For example, if the keyword gamification has an occurrence of 254, then this data appeared 254 times in the titles, abstracts, and keywords of all documents included in the analysis. For example, like that. Next, link. For example, if the link is written as 25, it means that the item has a direct connection with 25 other items in the analyzed network. For example, if the keyword gamification has a link of 25, it means this keyword co-occurs with 25 other keywords at least once. Or if we are analyzing authors, then it collaborates with 25 other authors. Now, it's strong, ladies and gentlemen. This means that the analysis results of VOSviewer, bibliometric analysis, are strong because even to reach the threshold, the requirements are not few and are difficult. Then, now, let's focus on how to analyze or how to get novelty. Okay. But before that, this is the last way to read, total link strength. For example, total link strength is 279. Now, this indicates the cumulative strength of all 250 links owned by the item. Now, this means that even though it only has 25 links, the total frequency of co-occurrence with all connections is 279 times. So, some connections are strong and appear together frequently, and some are weak. So, for example, if in total strength, let's say for gamification and education it's 45, gamification and motivation is 13, and so on, so the total link strength is 279. Well, now we will get the research gap and novelty from the visualization results. So, first, how to get a research gap. First, we can generate it by identifying the main themes or analysis clusters. Now, this can be identified from the most dominant cluster. So, for example, here, ladies and gentlemen. So, for example, here there is explainable AI, for example. The dominant cluster means it is indicated by the largest circle. Then it will be even more dominant if the link is thick. The lines are thick, of course, the links will be a certain number if we look on the left. First, we can claim a research gap if a topic is trending. Let's say this year, 2026, the trend of explainable AI is a trending topic. Therefore, because it is trending, we strengthen it with relevant articles. Ladies and gentlemen, you will certainly have the PDF files from the metadata that can be traced from the data we have generated. With that, we can claim that this research takes a topic related to explainable AI, considering that research related to this is trending and needed. Then it is strengthened by relevant articles and by the display in the visualization. And or vice versa. So, the opposite is if it is the smallest or the quietest, for example, neural network. If in this analysis, neural network is small. However, we read, for example, based on our understanding and the existing research we have read, that neural network is very, very related to explainable AI and is very relevant. But why is the position of neural network far and small here? This means that research discussing neural networks related to explainable AI is still limited, and this becomes a research opportunity for future research to work on this related topic. So, that's an example, ladies and gentlemen. Yes, it's like that. Then we can also get novelty from temporal analysis visualization. So, earlier, we moved to network visualization and then shifted to the middle one. If we look here, this is overlay visualization. Now, overlay visualization provides information on the most recent and oldest discussions of this topic. The darker it is, the longer it has been discussed. The brightest or the yellow color is the most recent discussion. We can also generate research gap claims for novelty from overlay analysis. For example, like this. So, we can use this, let's say business. Business is in green, green. Now, green towards yellow. This means the topic related to business is quite current. It means it is in the recent years. Or user experience, for example, sorry, experimental learning is clearly yellow. Now, it's yellow and far away. This also has great potential for us to claim a research gap. Let's say we want to focus on experimental learning, which is claimed to be a topic that has recently been discussed in recent years and has not been widely explored, and this can also be strengthened by density, by density here. Now, experimental learning. For example, the example is different. For example, if earlier experimental learning in its density, it would certainly be directly proportional, it would have a dull color. This indicates that it can support that the topic has not been widely explored. Now, like that. Then we can also generate, what? Claim research gaps from collaboration networks, co-authorship, or co-citation. Now, let's see if there are authors or institutions that are not connected in the main network if we use co-authorship. So, minimally, for example, like this. For example, the display, if we chose co-authorship earlier, it will display authors who are related to each other. Then our claim is, for example, the lack of collaboration between researchers from fields A and B indicates an unexplored integrative opportunity, even though it has synergistic potential in the field of AI, for example. Like that, ladies and gentlemen. Next, we can also get research gap justification from citation bars. This is quite helpful. But the condition for citation bars is that we have some kind of flagship article. So, for example, we look or read from journals before we do this analysis, we find an article whose citations are significant, very significant. But this citation bar, for example, one or two articles with this citation bar have not yet been included in the visualization analysis. Meanwhile, this article has the same environment. Let's say it's the same Scopus database, and the keywords are clearly there, but it's not included. Now, this can be claimed. So, for example, a paper by Smith, which we already have ourselves, shows a surge in citations on the topic of gamification and the field of learning, but it has not yet been included in any cluster. Now, this indicates a research direction. Was there a pause? Is it disturbing, ladies and gentlemen? No, it's just a pause. Oh, I see. Oh, okay. Okay. I'll repeat, sir. Okay. So, citation bars are for knowing, generating research gap justification from citation bars, which are papers with high citations. But these papers are not old papers, but new papers. But these papers have not yet been included in this analysis. They were not captured in this analysis. Even though they are in the same environment, even though they are in the same Scopus dataset network, for example. Then we can do an analysis process. We can claim, claim that this paper, for example, shows a surge in citations on the topic of gamification and the field of learning, but it has not yet been included in any cluster, and this indicates a new research direction, for example. So, for example, if we can claim from all these perspectives, we can combine them, ladies and gentlemen, combine them into one paragraph to form one gap. So, for example, the bibliometric analysis results show that the topic of gamification in the business context is still considered new and separate from the mainstream of research. Nevertheless, the appearance of these keywords in the overlay map with yellow color indicates increasing interest in recent years. The small connectivity between researchers from the gamification and business fields also marks a gap in interdisciplinary collaboration. Thus, research integrating gamification and business approaches can be said to be a research direction with strong novelty. Now, now, like that, to conclude, to generate a research gap claim, ladies and gentlemen. Now, perhaps that's all I can convey, ladies and gentlemen. Next, I will return the time to Mr. Ibas as the moderator. Thank you. I apologize if there were any mistakes in my words. Waalaikumsalam warahmatullahi wabarakatuh. Thank you, Mrs. Risa, for the excellent presentation. It seems many of you, ladies and gentlemen, want to ask questions, right, Mrs. Risa? Yes, please, sir. But before that, we ask for permission, as usual, Mrs. Risa. There is some information that we will convey regarding the classes available at TubuhPED, which might complement today's afternoon class. Then, after I convey some information, we will have a documentation session. Usually, participants ask for documents, right, documentation? Then, ladies and gentlemen, those who are perhaps not yet proper or ready for documentation, please prepare yourselves while waiting for me to convey the administrative information. Please help display the information. Alright. Alhamdulillah, the bibliometric class has been held this afternoon with Mrs. Risa. And God willing, next there will be a Scopus-indexed class as well, ladies and gentlemen. Perhaps there are those here who want to go further, want to focus more. There is also a Scopus-indexed class with Mrs. Risa, a class on writing papers to be Scopus-indexed with the help of AI for beginners, tomorrow, Thursday, February 26, 2026, at 13:00 Western Indonesian Time until finished. For more information, please contact the admin in the WA group, ladies and gentlemen, or the admin of the person who helped you register. Then there is the mastery set AI class for writing scientific papers using only one AI tool, mastery set AI, with Mr. Purbojet Miko, S. M.Sc. He is an associate professor, a young lecturer at Bunghatta University, an AI academic on Wednesday, March 4, 2026, at 13:00 Western Indonesian Time until finished. Next is the AI medical class. Perhaps ladies and gentlemen here have colleagues, acquaintances, relatives who are in the medical field or focus on the medical field or are studying in the medical world. There is an AI medical class, optimizing artificial intelligence or AI to improve the accuracy of medical diagnosis as an effort to minimize malpractice, with dr. Hendra Nusaputra, MKom, a practicing AI lecturer for medicine and a data scientist on Sunday, March 7, 2026, at 13:00 Western Indonesian Time until finished. Next is the AI citation class. Perhaps ladies and gentlemen here are still confused about how to cite properly and correctly. There is an AI citation class. Easy way to manage citations and automate bibliographies using Mendeley and plagiarism-free. Okay, this is with Dr. Akbar Kuntariswan, MHum, a lecturer in AI for Education and Research on Sunday, March 8, 2026, at 19:00. Oh, the time might be wrong, maybe. If it's 19:00, Muslims are still performing Tarawih. Yes, maybe there will be a revision, please check further. There is an AI novelty class, strategies for determining research gaps and research novelty by optimizing the role of AI. Yes. This class is perhaps for ladies and gentlemen here who want to focus more on novelty. There is an AI novelty class with Mr. Akbar, a lecturer in AI for Education and Research on Saturday, March 14, 2026, at 13:00 Western Indonesian Time until finished. Next is the final project class, ladies and gentlemen. Perhaps there are those here who are currently working on their final projects. There is a comprehensive discussion on writing theses and dissertations using AI legally and anti-plagiarism with Mr. Purbo Jatmik, MSC, Associate Professor, a young lecturer at Bunghatta University. He is also an AI academic enthusiast on Thursday, March 12, 2026, at 13:00 Western Indonesian Time until finished. Now, there is a private class. Perhaps ladies and gentlemen here want a private session so that the discussion is more intense and in-depth. There is a private class that we are trying to facilitate at Tumbuhia. There are two sessions of guidance, five stages of systematic literature review with Dr. Akbar Kuntarwan, MHum. For more information, ladies and gentlemen can contact the admin. Next, there is a private class in AI medical management, medical AI. There is also consultation on the utilization of AI for medicine. Perhaps those whose relatives are focusing on the medical world. There is also a private class for this. Usually, those who have clinics or hospitals. Now, many also need this, ladies and gentlemen. For more details, please inquire. Next, there is a private class for guidance on writing scientific papers according to your needs. So, your needs will be asked so that it can be directly addressed. This is with Mr. Akbar. For more information on time, price, and so on, you can chat with the admin. Chat with AI? Almost said chat with AI. Yes, next. There is a private class of three sessions guided by writing a Scopus paper until submission with Mrs. Fitri Marisa, PhD. Now, please. Usually, models like this are in high demand, Mrs. Risa. You can get more information from Mrs. Risa because for private classes at Tumbopedia, as far as I know, there was a participant who asked why I haven't got a schedule yet. It's indeed a schedule adjustment. Ladies and gentlemen, you can contact the admin for more information. If ladies and gentlemen are trying to submit to Scopus journals, Tumbopedia also tries to facilitate AI accounts for ladies and gentlemen, especially premium ones. There is set AI, then there are Proa and Promx accounts. For more information, contact the admin. Next. There is Manus AI as well. Perhaps there are those here who are already familiar with Manus AI. It's an extraordinary AI for brainstorming, in my opinion. What is it? If ladies and gentlemen usually use ChatGPT, this seems worth using Manus to try to get acquainted with. In my opinion, it's quite good. Powerful, powerful. Next. Now, this is I space. It has a nickname among the speakers at Tumbopedia as a one-stop writing service. Whatever you want to buy, I have it. Whatever you need, I have it. Now, there is an I SOP size P account. Now, the price is indeed quite high. But if ladies and gentlemen have a budget for it, please feel free to explore it. This is the level up from set AI, so to speak. Set AI, in my opinion, is mid-range. Because this is the flagship of I space. Next. Ah, ladies and gentlemen, there was a question from Mrs. Sri Harini from UNIDA. UNIDA Bogor or UNIDA Gontor? What if they don't have Scopus access, for example, if their campus hasn't subscribed? Tumbuhia tries to facilitate Scopus accounts. Please ladies and gentlemen, contact the admin for more information. Next. That's all. Alright. Thank you, ladies and gentlemen. Yes, there is a question, ma'am. I apologize for reading it, but something was missed. We haven't taken documentation yet. Please, ladies and gentlemen, let's take documentation for a moment. We are still waiting for you to turn on your cameras. This is extraordinary, still 84 participants. Fasting during Ramadan, you can't have coffee while fasting like this. Feeling sleepy, well, sleepy. Alright, ladies and gentlemen, I apologize, I will count to three, and please, ladies and gentlemen, give your best smile for the first count, and then feel free to give any pose, thumbs up or two fingers, for the next three counts. Alright, first three, 1, 2, 3, best smile, please. Okay. Alright, next, for the second count, ladies and gentlemen, please give your free pose. 1, 2, 3. Okay, alright. Still quite a lot. It's only two slides. Oh, I apologize, my settings were wrong. There are participants who haven't arrived yet. I will take screenshots of the last slide myself. Thank you. Alright, yes. I'll proceed to the discussion and Q&A session. I'll read the questions that have been submitted, Mrs. Risa. There was actually a very interesting one. From Mr. Azar, Mrs. Risa, does every article intended for international publication indexed by Scopus require bibliometric analysis? Yes, this has been helped, it seems, for mandatory article review, or perhaps this is one of the methods. Ah, this is more valid. Please, Mrs. Risa. Is it mandatory, ma'am? Yes, yes. Alright, thank you, sir. I'll answer directly, Mr. Ibas, ma'am. Yes, an interesting question regarding whether bibliometric analysis is mandatory for every Scopus publication. No, sir. So, no. So, if you feel that the research gap you have found, ladies and gentlemen, is solid, I think there is no need for bibliometric analysis. But there is no harm in considering it. So, let's say we already have a research gap projection, and we want to be more confident. Especially if we want to publish in Scopus, we compare the trends in Scopus about this. So, we take the existing dataset in Scopus with that topic. Then we perform bibliometric analysis. From there, it will be clear whether my research gap claim is correct. So, that's the first point. The second question is related to this. Oh, yes. Now, this is a good question. Earlier, I mentioned in the disclaimer that this training is, in other words, an initiation if ladies and gentlemen want to proceed to literature review analysis. So, if we look at the current trend of literature review analysis, whether using systematic literature review or meta-analysis, if we add bibliometric analysis as a method, it will strengthen it, sir. So, that's perhaps what I can answer. Okay, thank you, Mrs. Risa. I'll proceed to the next question. From Mr. Sugeng Santoso from Jakarta. Excuse me, Mrs. Marisa. In the practice of writing articles, can the bibliometric method be presented in the research methods and also in the discussion? Please provide an example of an article based on bibliometrics, ma'am. Thank you. Yes, it is very possible, sir. So, during my dissertation process, in chapter two, I also involved bibliometric analysis to strengthen the literature review of the dissertation. Then, for several Sinta journals and also theses, I also recommended students to do bibliometric analysis as a literature to strengthen it. But for Scopus-indexed journals, I have never done a literature review with bibliometrics. Because, coincidentally, the template I usually use in those journals does not use literature review, so I did not include that visualization analysis in the analysis. But I claimed the research gap from there. But for an example of a literature review journal that uses bibliometric analysis, I can provide an example, may I share it, sir? Is it visible, sir? Still processing. Oh, yes, it's ready. Okay. So, this is an example of a literature review analysis that uses bibliometrics, of course, as a basis. We use what method, systematic, meta-analysis, or others. Then, bibliometrics is used as a kind of additional method to strengthen, especially for trends. So, here, I am still using the approach of SLR, systematic literature review. So, I still use PRISMA, so I still use PRISMA. After that, the literature review analysis with the systematic approach will be combined with bibliometrics, so the presentation is like this. This was accepted in a third journal, but I withdrew it, ladies and gentlemen. Because after it was accepted, the payment was initially Rp 12 million, which is Rp 3 million. So, I withdrew it first. I looked for another article, looked for another journal that was more affordable, in my opinion, Rp 23 million for Q3 is not very worthwhile. So, I tried to fight to find an article, find a journal that might be less pricey. So, that's perhaps what I can exemplify. Okay, thank you, Mrs. Risa. Hopefully, it answers Mr. Sugeng Santoso from Jakarta. Yes. Next, from Mr. Febri Nugroho. Mrs. Risa, excuse me, Mr. Febri Nugroho from Jogja, for VOS analysis. If we create a data sheet ourselves from articles that we have downloaded, can we do that, ma'am? If we create a data sheet ourselves from articles that we have downloaded, can we do that, ma'am? What type of data would it be, Ris, or CSV? How to do that, ma'am? Roughly. Oh, yes. Alright, I'll answer. Mr. Febri, it's very possible, sir. So, sometimes, for SLR purposes, we need more than one data source, let's say Scopus, connected with Web of Science and others. Yes, indeed, we have to work extra hard to combine them. How to combine them? Normally, sir. So, the best way is to put it into a CSV file. So, just follow the pattern. The way is, sir, you can download, for example, using Scopus as a reference. Then you can use one of the templates. Let's say you have two sources, source one is Scopus, one is, let's say, P-Mets. Use one of the templates, the safest is the Scopus template. Then you will get a CSV. Then you can read the CSV pattern, what is needed. Then adjust it. What is in P-Mets can be copied and merged into that CSV, then combined into one CSV. So, that's possible, or maybe manually from any source, as long as the metadata from the articles is collected into hundreds of journals. So, that's very possible as long as it follows one pattern. Follows one pattern by using one that can be accepted by VOSviewer, which is the CSV generated from Scopus. So, that's perhaps what I can answer. Okay, thank you. Yes. Hopefully, it answers Mr. Febri. Yes. Next question, perhaps this is the last one, ma'am. Because the time is already 14:55. This is from [laughs] Mrs. Ovi Ayuning Nareswari. When using Dimension AI, can we specify which database to get the dataset from, ma'am? Can we specify which database to get the dataset from? If using Dimension AI, as I saw in the filters earlier, there is, but I forgot, ma'am. So, you can check the filters on the left. There is the source, then the author, then the source, especially the source. If, for example, you remember which ones you want to generate, then you can filter according to the category of datasets to be included in the analysis. As far as I remember, it's in the source. Perhaps you can look for it. Okay, thank you. Hopefully, it answers Mrs. Ovi. You can re-watch it, ladies and gentlemen. If it was perhaps too fast, ladies and gentlemen, it's okay to re-watch the recording on YouTube that the Tumbedia team has sent. While you're watching, while practicing. If you watch while practicing, it's probably not 100% possible. It's indeed better to watch. Then you can re-watch the recording, and of course, there is the material from Mrs. Risa, which the Tumbopedia team has sent. Please fill out the attendance form, and then you can open it there. After finishing filling out the attendance form, Mrs. Risa, I think I want to greet one of the participants who I find in every Tumbopedia class. Why is she always present? Excuse me for greeting her. Assalamualaikum, Mrs. Lia Nurcahyani. [laughs] Assalamualaikum, Mrs. Lia Nurcahyani. Waalaikumsalam, sir. Yes. How are you, Mrs. Liya? I'm fine, sir. Alhamdulillah. I've been watching since earlier, and it feels familiar. This is Mrs. Lia Nurcahyani. What is it? If this is just not practicing, sir. I just fell behind, so. [laughs] It's okay, re-watch the recording. Yes. It's okay, ma'am. Yes, Mrs. Lia, perhaps you can share with your friends. Perhaps there is something interesting that can be shared. Why is Mrs. Liya always in the class? [laughs] Masyaallah, ma'am. God willing, ma'am. Yes. Yes, thank you, Mr. Ibas. I have always followed Tumbedia since February 15th, sir. So, first, because I want to increase my knowledge, clearly. Then, second, I am not doing a task, but fulfilling SKS credits for my current studies, for my further studies, which can be fulfilled by attending various research-related events. So, now there is also my friend, Dr. Fari, who is also participating with Mrs. Azkia. So, I share the information from Tumbuhia. There is a calendar, a training calendar, a webinar calendar, so I share it, and alhamdulillah, it started from fulfilling the SKS requirements. But I have gained so much knowledge from Tumbedia. Because here, most of us get to know our assistants, various, various things that have been introduced, especially with artificial intelligence. So, it is very beneficial when I create my proposal later. So, what has been presented is very relevant, and yes, it can be very helpful, Mr. Ibas. Okay. Thank you, ma'am. Perhaps there is a message you want to convey to Tumbuhia for our improvement, ma'am. [laughs] Yes. It's already good, sir. So, it's indeed better to have hands-on practice like this, so we can do it directly. So, like earlier. I have also tried to install publish or perish, ma'am. So, if publish or perish can be used for, what? Managing when we want to create an SLR, right, ma'am? Yes, it can be very useful, ma'am, from various databases. Especially, I have created an SLR using it. But I feel it's still manual, right, if using AI. Especially for filtering duplicate articles. Then, after that, to filter which ones we choose for the next stage, for the PRISMA stage. But I have never used publish or perish. So, will it be easier to design an SLR? Especially here, I want a systematic literature review, not a scoping review. So, a systematic review. But because the title is not about that, it's more about finding research gaps. Yes. Finding research gaps. Perhaps in the next meeting. Yes. That's it, Mr. Ibas. Thank you. Okay. You're welcome, Mrs. Lia. You're welcome. Stay healthy. May all your writing processes be smooth until submission. Is it a dissertation, ma'am? If I'm not mistaken, yes, ma'am. Yes. Dissertation, Mr. Ibas. May your dissertation go smoothly, ma'am. Yes. Thank you, Mrs. Lia, once again. Thank you, Mrs. Risa. May you stay healthy, may all your affairs be made easy, may your sustenance be abundant. Alright. Thank you, ladies and gentlemen. May God bless you all. Once again, thank you. Excuse me, ladies and gentlemen, I'm signing off. Stay healthy, ladies and gentlemen. Amen. Amen. Yes, sir. Assalamualaikum.