Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal Jurnal Sistem Informasi dan Informatika Jurnal Ilmu Komputer dan Informatika
Arnita Piliang
Ilmu Komputer, Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Negeri Medan
Computer Science & IT Electrical & Electronics Engineering Engineering Materials Science & Nanotechnology Mathematics

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
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

Show Abstract | Download Original | Original Source | Check in Google Scholar

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.
Penerapan Metode Agglomerative Clustering Untuk Segmentasi Data Dalam Lingkungan Big Data Paskal Arienda Epindonta Ginting; Risky Immanuel Situmorang; Muhammad Raihansyah Lubis; Raja Ansel Hartama Sihombing; Arnita Piliang
Jurnal Sistem Informasi Dan Informatika Vol 4 No 1 (2026): Januari 2026
Publisher : Prodi Sistem Informasi Universitas Dharma Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jiska.v4i1.2639

Abstract

The exponential growth of data in the digital era has increased the need for analytical methods capable of handling Big Data characteristics. This study examines the application of Agglomerative Hierarchical Clustering (AHC) for data segmentation using two datasets: (1) an Iris dataset of 24 samples with 8 morphological attributes, and (2) an e-commerce transaction dataset of 10 customer records. Ward linkage was selected based on literature evidence of its superiority. Results on the Iris dataset yielded 3 optimal clusters with a Silhouette Score of 0.4196 and an Adjusted Rand Index of 0.3635, achieving 70.83% classification accuracy. In the e-commerce dataset, three customer segments were formed: premium, middle-tier, and passive customers. These findings confirm AHC as an effective multidimensional data segmentation method.
Co-Authors Arung Buana Subuh Farzad Sahnadi Pasaribu Muhammad Farrel Evan Yuri Muhammad Hafif Naibaho Muhammad Raihansyah Lubis Paskal Arienda Epindonta Ginting Raja Ansel Hartama Sihombing Risky Immanuel Situmorang