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All Journal JURIKOM (Jurnal Riset Komputer) Journal of Advanced Computer Knowledge and Algorithms
Defry Hamdhana
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Sistem Presensi Mahasiswa Berbasis Pengenalan Wajah Real-Time dengan Deteksi Anti-Spoofing Menggunakan YOLOv8 dan ArcFace Fajar Satria; Defry Hamdhana; Lidya Rosnita
JURNAL RISET KOMPUTER (JURIKOM) Vol. 13 No. 1 (2026): Februari 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v13i1.9502

Abstract

Student attendance recording is an important aspect in supporting discipline and administrative order in academic environments. Manual attendance methods still have several limitations, such as potential fraud and inefficient recapitulation processes. This study aims to develop a real-time face recognition-based student attendance system by implementing the YOLOv8 algorithm for face detection and ArcFace for identity recognition, complemented with an anti-spoofing feature to prevent fraudulent attempts. The system is designed to detect faces directly, recognize registered student identities, and record attendance automatically. The main contribution of this study lies in the integration of YOLOv8-based face detection, ArcFace-based face recognition, and an anti-spoofing mechanism into a single unified real-time attendance system. Experimental results show that the system successfully recognizes all registered students with a 100% success rate. The YOLOv8 anti-spoofing model demonstrates excellent performance in distinguishing real and fake faces, achieving an mAP@0.5 value of 0.995 and an F1-score close to 1. The system is also able to record attendance time in real time according to the actual time and present attendance data systematically. Based on these results, the developed real-time face recognition attendance system is accurate, secure, and feasible to be implemented as an attendance solution in academic environments
Comparison of Coffee Bean Sales Predictions at the Ketiara Coffee Traders Cooperative (KOPEPI) Using Linear Regression and Random Forest Methods Syadzwina, Nada; Defry Hamdhana; Ar Razi
Journal of Advanced Computer Knowledge and Algorithms Vol. 3 No. 2 (2026): Journal of Advanced Computer Knowledge and Algorithms - April 2026 (In Press)
Publisher : Department of Informatics, Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/jacka.v3i2.25974

Abstract

Most of Indonesia's land is used for agriculture and plantations because it is an agrarian country. Harvests or agricultural products can be exported to help the country's economic recovery. Coffee, the most traded tropical crop in the world, is one of the most valuable commodities. Approximately 25 million farming households contribute up to 80% of global coffee production (FAO Organizational 2023). Indonesia's coffee industry continues to experience significant annual growth. To optimize their production and distribution, Indonesian coffee producers must understand coffee bean sales trends. This study compares two methods for predicting coffee bean sales at the KOPEPI Ketiara Aceh Tengah Cooperative using Linear Regression and Random Forest methods. The research methods used in this study are data collection and system design. The results show a comparison of the Linear Regression and Random Forest methods in predicting coffee bean sales. Linear regression provides fairly good accuracy for the price variable with low MAPE values (3.35%–4.55%) and MAE that is still within reasonable limits, but produces large prediction errors for the export variable with high MAPE (67.84%–80.65%) and large MAE (5982–7960). In contrast, Random Forest shows superior performance with very low MAPE (2.69%–3.46%) and smaller MAE (4275–6038) on price variables, as well as more stable and consistent export predictions even though the MAPE values are still quite high (54.25%–84.97%). Overall, Random Forest is a more appropriate model to use because it provides accurate price predictions and more consistent export performance compared to Linear Regression.
Co-Authors Ar Razi Fajar Satria Lidya Rosnita Syadzwina, Nada