JICDA: Journal of Informatics, Computer Science, Data Science And Artificial Intelligence https://publication.arstech.co.id/index.php/JICDA <p><strong>JICDA: Journal of Informatics, Computer Science, Data Science And Artificial Intelligence</strong>: Is a Scientific Journal in the fields of Informatics Engineering, Computer Science, Data Science and also Artificial Intelligence. Journal of Informatics, Computer Science, Data Science and Artificial Intelligence or abbreviated as <strong>JICDA</strong> is published twice a year (6 months), namely in <strong>December </strong>and <strong>June</strong>. <strong>JICDA:</strong> <strong>Journal of Informatics, Computer Science, Data Science And Artificial Intelligence</strong> aims to publish research in the fields of computer science, Informatics Engineering, Data Science, and Artificial Intelligence which focus on publishing quality scientific papers about the latest information about developments in computer science. Articles submitted will be reviewed by the Reviewer Team (the Journal and Association technical committee). All articles submitted must be original reports, research results that have never been published before. Articles submitted to the Journal of Informatics, Computer Science, Data Science and Artificial Intelligence may not be published elsewhere before a decision has been made by the editor. Articles must follow the writing style provided and must go through a Peer-review process by applying the <strong>Double Blind Review concept.</strong></p> <p><strong>JICDA : Journal of Informatics, Computer Science, Data Science, and Artificial Intelligence </strong>consists of several special topics in the field of Informatics, Computer Science, Data Science, and Artificial Intelligence, including Algorithms and Programming, Cryptography and Security System, Steganograpy, Digital Image Processing, Networking, High-Performance Computing, Compter Vision, Pattern Recognition, Geographics Information System, Software Engineering, Internet and E-Commerce, Data Mining, Big Data, Machine Learning, Deep Learning, Data Science, Data Analysis, Artificial Intelligence, Soft Computing, Metaheuristic Optimization, Fuzzy Logic, Artificial Neural Network, Decision Support System, Robotics, and Information System.</p> <p><strong>JICDA : Journal of Informatics, Computer Science, Data Science, and Artificial Intelligence </strong>published by<strong> Arka Sains Tech (Arstech), </strong>Medan, Indonesia. <strong>E-ISSN : 3031-9145.</strong></p> en-US rahmatikahizria@arstech.co.id (Rahmatika Hizria, S.Kom., M.Kom) ariefrahmanh1@gmail.com (Arief Rahman Hakim, S.Kom.,M.Kom) Wed, 10 Jun 2026 20:22:23 +0700 OJS 3.3.0.11 http://blogs.law.harvard.edu/tech/rss 60 Implementation of the Naïve Bayes Algorithm in the Assessment of Competency Examinations for Training Program Participants at the Medan Industrial Training Center https://publication.arstech.co.id/index.php/JICDA/article/view/88 <p>The development of information technology has driven digital transformation across various sectors, including education and industrial training. One important aspect is the assessment process of competency examinations for training program participants, which has traditionally been conducted manually, resulting in lengthy processing times, potential subjectivity, and low efficiency. This study aims to design and implement a competency examination assessment system based on the Naïve Bayes algorithm at the Medan Industrial Training Center (Balai Diklat Industri/BDI Medan). The research methodology includes problem identification, literature review, data collection in the form of examination questions and participants’ answer results, data preprocessing, system design, implementation using the Python programming language with a MySQL database, and system performance evaluation. The developed system involves three user roles: administrator, assessor, and participant. Participants complete theoretical examinations in the form of multiple-choice questions, while assessors provide evaluations for interviews and practical examinations. The administrator is responsible for managing data and examination questions, as well as processing assessment results using the Naïve Bayes algorithm. The implementation results indicate that the Naïve Bayes–based assessment system is capable of automatically classifying participants’ examination outcomes into Pass or Fail categories with a good level of accuracy. The system has proven to improve efficiency, accelerate the assessment process, and minimize subjectivity compared to manual methods. The conclusion of this study is that the application of the Naïve Bayes algorithm in a competency examination assessment system can serve as an effective and innovative solution for the digitalization of the evaluation process at BDI Medan. Future research is recommended to further develop the system to support essay-type questions using more advanced algorithmic approaches.</p> <p>&nbsp;</p> <p><strong>Keywords :&nbsp;</strong>Examination Assessment, Naïve Bayes, Machine Learning, Competency</p> Sirli Rizqiya Nur Khalaliya, Mhd. Zulfansyuri Siambaton, Heri Santoso Copyright (c) 2026 Sirli Rizqiya Nur Khalaliya, Mhd. Zulfansyuri Siambaton, Heri Santoso https://creativecommons.org/licenses/by-nc/4.0 https://publication.arstech.co.id/index.php/JICDA/article/view/88 Wed, 10 Jun 2026 00:00:00 +0700