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All Journal Jurnal Ilmu Komputer dan Informatika
Arung Buana Subuh
Universitas Negeri Medan
Computer Science & IT Electrical & Electronics Engineering Engineering Mathematics

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Rancang Bangun Sistem Manajemen Nilai Siswa Menggunakan PHP dan MySQL Maulana Al Nouri; M. Rafiif Albani; M. Hafif Naibaho; Arung Buana Subuh
Jurnal Ilmu Komputer dan Informatika | E-ISSN : 3063-9026 Vol. 2 No. 2 (2025): Oktober - Desember
Publisher : GLOBAL SCIENTS PUBLISHER

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This study aims to design and develop a web-based student score sorting system by applying a sorting algorithm to improve the effectiveness and efficiency of academic data processing. The research employs a Research and Development (R&D) approach using the Waterfall development model, which includes the stages of requirements analysis, system design, implementation, and testing. The system was developed using the PHP programming language and a MySQL database. The score sorting process was implemented by integrating a sorting algorithm into the system to automatically arrange student data based on specific criteria. The system successfully achieved 100% accuracy in sorting student scores and demonstrated stable time efficiency across various data sizes. Therefore, this system can assist teachers and schools in managing student score data quickly, accurately, and in a well-structured manner.
Evaluasi Performa Gaussian Mixture Model dan K-Means terhadap Ketidakseimbangan Data pada Clustering Muhammad Farrel Evan Yuri; Farzad Sahnadi Pasaribu; Arung Buana Subuh; Muhammad Hafif Naibaho; Arnita Piliang
Jurnal Ilmu Komputer dan Informatika | E-ISSN : 3063-9026 Vol. 2 No. 4 (2026): April - Juni
Publisher : GLOBAL SCIENTS PUBLISHER

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Data imbalance represents a primary challenge in clustering analysis, particularly in datasets with highly disproportionate class distributions such as the Credit Card Fraud Detection dataset from Kaggle. This study aims to evaluate and compare the performance of the Gaussian Mixture Model (GMM) and K-Means algorithms under such conditions through a systematic literature review of nine prior studies. Clustering quality is evaluated using three internal validation metrics: Silhouette Score, Davies-Bouldin Index (DBI), and Calinski-Harabasz Index (CHI). The findings indicate that GMM consistently produces more stable and flexible clusters in data with overlapping distributions, as its probabilistic approach through the Expectation-Maximization (EM) algorithm allows each data point to hold multiple cluster membership probabilities. In contrast, K-Means produces sharper cluster boundaries with lower computational complexity, yet remains sensitive to outliers and the spherical distribution assumption frequently unmet in imbalanced data. The dominance of the majority class risks distorting K-Means centroids, resulting in suboptimal detection of fraudulent transactions, whereas GMM proves more adaptive for this scenario despite its higher computational cost.
Co-Authors Arnita Piliang Farzad Sahnadi Pasaribu M. Hafif Naibaho M. Rafiif Albani Maulana Al Nouri Muhammad Farrel Evan Yuri Muhammad Hafif Naibaho