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All Journal TEKNIK INFORMATIKA CESS (Journal of Computer Engineering, System and Science) JURNAL TEKNOLOGI DAN OPEN SOURCE Journal of Computer Networks, Architecture and High Performance Computing Jurnal Kolaboratif Sains Jurnal Sistem Informasi Triguna Dharma (JURSI TGD) Journal of Artificial Intelligence and Engineering Applications (JAIEA) Jurnal Ilmiah Sistem Informasi dan Ilmu Komputer Journal Of Informatics And Busisnes Jurnal Publikasi Ilmu Komputer dan Multimedia
Mhd. Furqan
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Religion Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Environmental Science Health Professions Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Medicine & Pharmacology Nursing Public Health Other

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Comparison of Machine Learning Classification Algorithm Performance for Depressive Symptom Recognition in College Students Arinda Aulia; Falah Affandi; Puan Syaharani Sitorus; Chairil Umri; Ferizal Fadli Tanjung; Mhd. Furqan
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 2 (2026): February 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i2.1998

Abstract

College students are vulnerable to depressive symptoms due to academic, social, and personal pressures, which can impact mental health and academic achievement. Early detection is necessary to prevent this condition from developing into a more serious condition, but conventional methods often lack objectivity. With the development of artificial intelligence, machine learning classification algorithms offer a more accurate approach to recognizing patterns of depressive symptoms. This study compared the performance of several classification algorithms, namely Random Forest, K-Nearest Neighbor, Logistic Regression, Decision Tree, Naive Bayes, and Support Vector Machine, using a dataset of depressive symptoms in college students. Evaluation was carried out based on accuracy, precision, recall, and F1-score. The results showed that Logistic Regression achieved the best performance with an accuracy of 95.62%. This suggests that selecting the right algorithm can improve the effectiveness of early depression detection systems in college students and support data-driven mental health efforts.
Efficiency vs. Accuracy: A Comparative Analysis of Lightweight MobileNetV2 and VGG16 for Brain Tumor MRI Classification Using Deep Feature Extraction Nasution, Raja Anan; Mhd. Furqan; Rika Rosnelly
JURNAL TEKNIK INFORMATIKA Vol. 19 No. 1: JURNAL TEKNIK INFORMATIKA
Publisher : Department of Informatics, Universitas Islam Negeri Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/jti.v19i1.45002

Abstract

Brain tumor detection using magnetic resonance imaging (MRI) is a crucial task for early diagnosis and treatment planning, requiring models that are not only accurate but also computationally efficient. This study presents a comparative analysis of two Convolutional Neural Network (CNN) architectures, MobileNetV2 and VGG16, combined with Principal Component Analysis (PCA) for deep feature dimensionality reduction. The dataset consists of 253 brain MRI images (155 tumor and 98 non-tumor), which have been preprocessed and divided into training and testing sets using an 80:20 stratification split. Experimental results show that MobileNetV2 with PCA achieves an accuracy of 86.27%, with a precision of 87.50% and a recall of 90.32% for the tumor class, demonstrating balanced performance in classifying tumor and non-tumor images. VGG16 with the same PCA configuration achieves an accuracy of 64.71%, with a recall of 100% for the tumor class but a low recall of 10% for the non-tumor class. These findings suggest that extreme dimensionality reduction affects deep feature representation differently depending on the original feature structure. The results show that MobileNetV2 provides a better balance between accuracy and feature compactness at high dimensionality reduction settings, making it more suitable for resource-constrained medical image classification scenarios.
Analisis dan Perancangan Prototype Sistem Antrian Online Berbasis Web untuk Layanan Bank Pebi Mina Husania; Rani Chantika; Mhd. Furqan
Jurnal Publikasi Ilmu Komputer dan Multimedia Vol. 4 No. 2 (2025): Mei: Jurnal Publikasi Ilmu Komputer dan Multimedia
Publisher : Pusat Riset dan Inovasi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jupikom.v4i2.4074

Abstract

This study focuses on the analysis and design of a web-based online queue system prototype for banking services, aiming to improve service quality and operational efficiency. In the banking sector, managing customer waiting times is a significant challenge that impacts their comfort and satisfaction. The online queue system enables customers to obtain queue numbers online, thereby minimizing waiting times and making queue management more efficient. This research includes system requirements analysis, user interface design, and process flow development tailored to customer needs and banking operations. The resulting prototype is expected to serve as a foundation for developing a more comprehensive digital queue system, supporting digital transformation in the banking sector, and providing practical and theoretical contributions to web-based service development.
Co-Authors Adli Rahman Harun Harahap Aina Nur Nabila Alvi Nur Ilmi Br Ginting Alwi Syahputra Arinda Aulia Chairil Umri Dimas Aqila Aptanta Falah Affandi Fauhan Alfarizi Saragih Ferizal Fadli Tanjung Hasibuan, Suci Adina Ramadani Lubis , Aidil Halim M. Rifqi Arrafi Manihuruk , Selly Novia Meutia Nanda Muhammad Farhan Nasution, Raja Anan Nazifa Edilia, Fazila Pebi Mina Husania Prananda, Dimas Raka Puan Syaharani Sitorus Rani Chantika Rika Rosnelly Ritonga, Abdul Rois Rusma Riansyah Siti Saniah Tiara Amanda Lestari Windi Imelda Putri