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All Journal Jurnal Ilmu Komputer dan Informatika
Farzad Sahnadi Pasaribu
Universitas Negeri Medan
Computer Science & IT Electrical & Electronics Engineering Engineering Mathematics

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Implementasi Queue Berbasis Linked List Pada Aplikasi Web Manajemen Antrian Print Mahasiswa Sirlia Sahid; Farzad Sahnadi Pasaribu; Mika Monika Fransiska Simanullang; Muhammad Raihansyah Lubis
Jurnal Ilmu Komputer dan Informatika | E-ISSN : 3063-9026 Vol. 2 No. 2 (2025): Oktober - Desember
Publisher : GLOBAL SCIENTS PUBLISHER

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Queue irregularities are a common problem with photocopying services, particularly with student assignment printing. Because there is no clear system for managing the order in which documents are printed, students often have to wait long periods. This can lead to dissatisfaction and confusion. The purpose of this research is to create an interactive web application capable of managing routine print queues using a linked list-based queue structure. A queue system based on the FIFO (First In First Out) principle was created. This allows each student to enter their identity, the files to be printed, and any special notes about their printing needs. The system then displays a real-time queue list, allowing the photocopy operator to download the files in the correct order.
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.
Co-Authors Arnita Piliang Arung Buana Subuh Mika Monika Fransiska Simanullang Muhammad Farrel Evan Yuri Muhammad Hafif Naibaho Muhammad Raihansyah Lubis Sirlia Sahid