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Muhammad Wali
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muhammadwali487@gmail.com
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cj@ypmma.org
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Jl. B. Aceh – Medan GP. Pasir Putih Kec. Peureulak Aceh Timur.
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INDONESIA
Computer Journal
Published by Yayasan YPMMA
ISSN : 29646200     EISSN : 29646219     DOI : https://doi.org/10.58477/cj
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management
Computer Journal, e-ISSN: 2964-6219 and p-ISSN: 2964-6200 is a free and open-access journal published by the Research Division, YPMMA Institute, Indonesia. Computer Journal is an international, scientific, peer-reviewed, open-access computer science journal, including computer and network architecture and human-computer interaction as its main focus, published six months online. Computers Journal is an international, open access journal which provides an advanced forum for computer sciences. It publishes reviews, regular research papers and short communications. Our aim is to encourage scientists to publish their experimental and theoretical results in as much detail as possible. There is no restriction on the length of the papers. The full experimental details must be provided so that the results can be reproduced.
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 92 Documents
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Comparison of Support Vector Machine and K-Nearest Neighbor Methods for Predicting Student Learning Outcomes Based on Student Performance Data Chynthia Drinita; Felicia Felicia; Palma Juanta
Computer Journal Vol. 4 No. 2 (2026): August
Publisher : Yayasan Pendidikan Mitra Mandiri Aceh (YPMMA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58477/cj.v4i2.512

Abstract

Information systems and machine learning have encouraged the use of academic data to predict student learning outcomes in the education sector. This study evaluated the performance of Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) algorithms in predicting student learning outcomes using the Student Performance in Exams Dataset from Kaggle. A quantitative computational experiment was conducted through several preprocessing stages, including target variable creation, categorical data conversion, feature standardization using StandardScaler, and data splitting into 80% training data and 20% testing data. Model performance was evaluated using accuracy, precision, recall, F1-score, Receiver Operating Characteristic (ROC) curve, and Area Under the Curve (AUC). The results show that SVM achieved an accuracy of 97%, with average precision, recall, and F1-score values of 0.97, while KNN achieved an accuracy of 90% and an F1-score of 0.88. The AUC values of 1.00 for SVM and 0.97 for KNN indicate that both models performed well, although SVM provided better class separation. Therefore, SVM was more effective than KNN in predicting student learning outcomes on the dataset used and may support the development of machine learning-based academic prediction systems.
Evaluasi Usability Mentari UNPAM Menggunakan Model SUS (System Usability Scale) Firman Pratama; Devi Damayanti; Haekal Mimtazulfaqhi Zaydan; Gilang Ramadhan; M. Hafizh Rafid Hidayat
Computer Journal Vol. 4 No. 2 (2026): August
Publisher : Yayasan Pendidikan Mitra Mandiri Aceh (YPMMA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58477/cj.v4i2.513

Abstract

Current higher education infrastructure heavily relies on Learning Management Systems (LMS) as digital hubs for academic interaction. Universitas Pamulang implemented Mentari UNPAM to support large-scale online learning. However, the platform’s usability and User Experience (UX) have not been empirically assessed using standardized metrics. This lack of evidence contributes to increased cognitive load for users and limits the implementation of User-Centered Design (UCD). This study aims to evaluate the usability level of Mentari UNPAM and propose interface redesign recommendations. A descriptive quantitative approach was conducted by administering the System Usability Scale (SUS) to 150 active Informatics Engineering students. The results show an average SUS score of 68.27. Based on the Sauro-Lewis scale, this corresponds to a Fair-to-Good range with an Acceptable rating. Data distribution analysis further indicates satisfaction polarization associated with interaction friction during the First-Time User Experience (FTUE) phase, navigation disorientation, and server latency during peak traffic. As mitigation, the study recommends restructuring the layout hierarchy of critical features, integrating breadcrumb navigation, providing digital onboarding, and optimizing server architecture.

Page 10 of 10 | Total Record : 92

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