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All Journal Jurnal Ilmiah Edutic : Pendidikan dan Informatika Jurnal Ilmu Komputer dan Informatika
Muhammad Hafif Naibaho
Universitas Negeri Medan
Computer Science & IT Education Electrical & Electronics Engineering Engineering Mathematics

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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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Abstract

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.
Design and Development of the “CampusVents” Platform: An Integrated Campus Event Aggregator and Management System with Interest-Based Recommendation Features Muhammad Farrel Evan Yuri; Farzad Sahnadi Pasaribu; Muhammad Hafif Naibaho; Debi Yandra Niska
EDUTIC Vol 13, No 2: 2026
Publisher : Universitas Trunodjoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/edutic.v13i2.34567

Abstract

The proliferation of campus events across various student organizations has led to fragmented information distribution, making it difficult for students to discover and participate in relevant activities. This study presents the design and development of CampusVents, a web-based platform that integrates three core functions: event aggregation from multiple student organizations, integrated event management, and a user interest-based event recommendation system. The platform was developed using the Laravel 10 framework, PHP 8.2, MySQL 8.0, and Bootstrap 5, following the waterfall software development methodology. System testing was conducted through User Acceptance Testing (UAT) involving 50 respondents using a Likert-scale questionnaire covering aspects of usability, functionality, performance, aesthetics, and acceptability. The overall average score reached 4.37 out of 5.00, categorized as “Good,” with the usability and performance aspects obtaining the highest scores (4.40). The recommendation feature employing an interest-based filtering approach was positively received, with 52% of respondents indicating willingness to adopt the platform if officially deployed on campus. These findings confirm that CampusVents effectively addresses the problem of fragmented campus event information and demonstrates significant potential for adoption in academic environments.
Co-Authors Arnita Piliang Arung Buana Subuh Farzad Sahnadi Pasaribu Muhammad Farrel Evan Yuri Muhammad Farrel Evan Yuri Niska, Debi Yandra