https://journal.binadarma.ac.id/index.php/BigDataScience/issue/feed Journal of Big Data Science 2023-10-18T14:00:48+07:00 Ferdi Aditya, M.Kom jit@binadarma.ac.id Open Journal Systems <p><strong>Journal of Big Data Science</strong><br><br>Welcome to the Journal of Big Data Science (JBDS) website. JBDS has <a href="https://issn.brin.go.id/terbit/detail/20230912321740146" target="_blank" rel="noopener">P-ISSN 3025-6615</a> is one of the journals managed by Jurnal Ilmiah Terpadu (JIT) Universitas Bina Darma.<br><br>Journal of Big Data Science (JBDS) aims to provide types of scientific literature regarding pure and applied research studies in the fields of Big Data, Data Science and Artificial Intelligence, as well as public observations of the development of theories, methods and applied science related to these subjects. JBDS is designed to facilitate not only local researchers but also international researchers to publish their work exclusively in English. JBDS publishes 2 editions per volume, namely in September and March</p> https://journal.binadarma.ac.id/index.php/BigDataScience/article/view/2565 Literature Review: Data Mining For Student Data Classification 2023-09-30T13:21:00+07:00 Billi Mahardika billymahardika123@gmail.com <p><em>The abundance of student data and data on the number of student graduations, hidden information can be found by processing student data so that it is useful for the university. Processing of student data needs to be done to find out important information in the form of new knowledge (knowledge discovery). <strong>Method</strong>: Literature review is carried out based on issues, methodology, equations.<strong>result :</strong> Decision Trees) Decision tree analysis (or decision tree analysis is a technique that belongs to the machine-learning family) is arguably the most popular classification technique in the data mining area. </em></p> 2023-09-30T13:20:47+07:00 ##submission.copyrightStatement## https://journal.binadarma.ac.id/index.php/BigDataScience/article/view/2564 Naïve Bayes Method for Text-Based Sentiment Analysis on Social Media 2023-09-30T14:37:12+07:00 Rezki Syaputra rezkis@lingkaran.id Ria Andryani ria.andryani@binadarma.ac.id Deni Erlansyah deni@binadarma.ac.id Rupianti Rupianti rupianti@binadarma.ac.id <p>Scientometrics is the study of&nbsp; measurement and analysis of science, innovation and technology through scientific publications. One form of measurement that can be taken is&nbsp; the network of authors measurement. This study uses author network analysis as a measurement tool performed in scientific studies. The purpose of this study was to observe the Authorsip network formed among professors at Bina Darma University, in order to determine which professors and departments are the most productive in producing yearbook articles&nbsp; or magazine. The method used in this study is the centrality of graphic degrees. Software used to view Gephi 0.9.2. The data used in this study are published data for the year 2015-2020. Based on the results of this study, it can be concluded that the agent with the highest central value is the EU with a value of 28, where the EU is the agent. with the largest number of publications. Meanwhile, the actor who has an influence or relationship and frequently collaborates on publications with the highest score on Betweenness Centrality is AM with a score of 61500.94.</p> 2023-09-30T14:37:12+07:00 ##submission.copyrightStatement## https://journal.binadarma.ac.id/index.php/BigDataScience/article/view/2563 Scientometrics Study and Authorship Network Analysis in Universitas Bina Darma Lecturer Publications 2023-10-18T14:00:48+07:00 Ferdi Aditya f.aditya@binadarma.ac.id Deni Erlansyah deni@binadarma.ac.id Dendi Triadi dendi.triadi@binadarma.ac.id Rezki Syaputra r.syaputra@binadarma.ac.id <p>Scientometrics is a study of the measurement and analysis of science, innovation and technology through scientific publications. One form of measurement that can be done is through the measurement of the network of authors. This study uses authorsip network analysis as a measuring tool implemented in scientomentrics studies. The purpose of this study is to observe the Authorsip network formed between Bina Darma university lecturers, so as to determine which lecturers and faculties are the most productive in producing articles published in proceedings or journal. The method used in this research is Graph Degree Centrality. The software used for visualization of Gephi 0.9.2. The data used in this study is publication data for 2015 – 2020. Based on the results of this study, it can be concluded that the actor with the highest degree centrality value is the EU with a value of 28, where the EU is the actor with the highest number of publications. Meanwhile, the actor who has influence or relationship and often collaborates in publications with the highest score on Betweenness Centrality is AM with a score of 61500.94.</p> 2023-10-18T00:00:00+07:00 ##submission.copyrightStatement## https://journal.binadarma.ac.id/index.php/BigDataScience/article/view/2562 Hadoop Perfomance Improvement For Big Data Processing On Trans-Regional Networks Using ViNe 2023-10-18T12:45:00+07:00 Rahmat Novrianda rahmat_novrianda@binadarma.ac.id Alek Wijaya alex_wj@binadarma.ac.id Timur Dali Purwanto timur.dali.purwanto@binadarma.ac.id Rezki Syaputra r.syaputra@binadarma.ac.id <p><em>The exponential growth of data continues to increase in human life, the data is sourced from sensor data, social media data and social networking service (SNS) data which is referred to as the Big Data era. As data grows bigger and faster, the cost of storing and analyzing data becomes more and more expensive. This article proposes an approach to improve Hadoop performance in a trans-regional network environment through a virtual networking tool called ViNe. This approach enables IoT-sourced Big Data to store data in geographically distributed storage. Additionally, the approach allows IoT Big Data analyzers to deploy MapReduce applications on top of a trans-regional Hadoop Distributed File System.</em></p> 2023-10-18T12:45:00+07:00 ##submission.copyrightStatement##