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All Journal Journal of Artificial Intelligence and Engineering Applications (JAIEA) Jurnal Ilmiah Sistem Informasi dan Ilmu Komputer
Arinda Aulia
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Automotive Engineering Computer Science & IT Control & Systems Engineering Industrial & Manufacturing Engineering

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Implementasi Sistem Absensi Peserta Magang Berbasis Web Menggunakan Metode Waterfall Arinda Aulia; Falah Affandi; Yusuf Ramadhan Nasution
Jurnal ilmiah Sistem Informasi dan Ilmu Komputer Vol. 5 No. 3 (2025): November: Jurnal ilmiah Sistem Informasi dan Ilmu Komputer
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/juisik.v5i3.1678

Abstract

The internship attendance process for students in government institutions is still carried out manually, which often leads to irregularities and inefficiencies in recording attendance. To address this issue, this study designs a web-based attendance system for internship participants at the Regional House of Representatives (DPRD) of North Sumatra using the Waterfall method. This method was chosen because it provides a structured sequence of stages, starting from requirements analysis, system design, implementation, testing, and maintenance. Through this approach, system requirements can be clearly analyzed from the beginning, resulting in a design that aligns with the intended goals. The developed system features user login, attendance input for internship participants, attendance data recap, and report generation. The results show that this web-based attendance system improves the efficiency of attendance recording, minimizes errors, and creates a more organized internship environment. Therefore, the implementation of a web-based attendance system using the Waterfall method can serve as an effective and structured solution to support the management of internship attendance data.
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.
Co-Authors Chairil Umri Falah Affandi Ferizal Fadli Tanjung Mhd. Furqan Puan Syaharani Sitorus Yusuf Ramadhan Nasution