About the Journal
J-KOMA: Jurnal Ilmu Komputer dan Aplikasinya is dedicated to researchers and practitioners in the field of computer science and information technology, focusing on publishing high-quality research covering topics such as Artificial Intelligence, Data Mining, Big Data Analytics, Software Engineering, Information Systems, Cybersecurity, and the development of web, mobile, and human-computer interaction (HCI) systems. J-KOMA is committed to serving as a scientific platform that promotes the advancement of knowledge and technological innovation in computing and its applications across education, research, and industry sectors.
J-KOMA: Jurnal Ilmu Komputer dan Aplikasinya is published by Universitas Negeri Jakarta and managed by the Research and Community Service Institute (LPPM), Universitas Negeri Jakarta in collaboration with the Computer Science Study Program, Faculty of Mathematics and Natural Sciences, Universitas Negeri Jakarta. J-KOMA also maintains an academic partnership with the Faculty of Mathematics and Natural Sciences, Universitas Negeri Surabaya, under collaboration agreements 3940/UN39.5.FMIPA/HK.07/2025 and 98477/UN38.3/KS.03.02/2025.
Publisher Information
Publisher: Universitas Negeri Jakarta
Management: LPPM Universitas Negeri Jakarta
Address: Kampus A, Universitas Negeri Jakarta, Jl. Rawamangun Muka, Jakarta Timur, Indonesia
Website: https://lppm.unj.ac.id/jurnal/
Contact Person: Devi Anggraeni (ojs@unj.ac.id)
Dedicated Email: uniilmukomouter@gmail.com
Current Issue
This Issues presents six research articles spanning computer science, applied statistics, and information systems. This edition features a broad spectrum of analytical and modeling approaches, from the Vector Error Correction Model (VECM) for examining the long- and short-term relationships between taxation and Indonesia's economic growth, to discriminant analysis methods—Linear and Quadratic Discriminant Analysis—for early cervical cancer risk prediction based on behavioral and psychosocial attributes. The articles in this volume also demonstrate extensive applications of machine learning and deep learning: sentiment analysis using Random Forest to gauge public opinion on the reinstatement of academic streaming policy in senior high schools, a comparative study of lexicon-based and machine learning approaches (SVM, Naïve Bayes, Random Forest) for classifying sentiment in Indonesian telemedicine reviews, and a CNN-based transfer learning approach using MobileNetV2 and DenseNet121 for automated lung disease diagnosis from MRI imaging. Rounding out this volume is a systems development study presenting the optimization of a web-based room information management system (SIPERAD) using the Waterfall method. Overall, the contributions in this volume illustrate the application of modern computational and statistical methods to real-world problems across the fiscal, educational, healthcare, and higher-education administration domains, making it relevant reading for researchers, data science practitioners, health informatics scholars, and policymakers.
