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All Journal Faktor Exacta Jurnal Teknologi Informasi dan Pendidikan
Achmad Indra Aulia
Institut Teknologi Sains Bandung
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering

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Lightweight YOLO Models for Robust Facial Expression Detection Achmad Indra Aulia; Albert Jofrandi Hutapea; Amril Mutoi Siregar; Surjandy
Jurnal Teknologi Informasi dan Pendidikan Vol. 19 No. 2 (2026): Jurnal Teknologi Informasi dan Pendidikan
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/jtip.v19i2.1120

Abstract

Facial expression recognition is a fundamental component of artificial intelligence systems, particularly in human–machine interaction. However, achieving robust detection accuracy remains challenging due to variations in lighting, facial orientation, and limited training data diversity. While recent lightweight YOLO architectures—YOLOv8n, YOLOv10n, and YOLO11n—have demonstrated strong performance in general object detection, comparative studies evaluating these models specifically for facial expression detection remain limited. This study addresses this gap by systematically comparing these three nano-variant models on a dataset of 2,000 labeled facial images across four expression categories: flat face, angry, sad, and smile. The dataset was divided into training (70%), validation (20%), and test (10%) subsets. Experiments were conducted under two scenarios—with and without data augmentation—using identical training configurations. Augmentation techniques included mosaic composition, HSV variation, geometric transformations, and flipping. Results show that augmentation improved the F1 score of YOLOv10n from 0.68 to 0.72 and YOLO11n from 0.65 to 0.72, with the latter achieving the highest overall precision of 0.82. YOLOv8n exhibited stable performance with an F1 score of 0.75 under both conditions. Confidence threshold optimization revealed distinct optimal operating points for each model, ranging from 0.1 to 0.6, confirming that per-model threshold tuning is necessary to maximize detection performance. These findings provide practical guidance for selecting and configuring lightweight YOLO models for facial expression detection in resource-constrained environments.
Ketahanan Pembelajaran Mesin terhadap Adversarial examples: Metodologi dan Pertahanan Ade Kurniawan; Ely Aprilia; Achmad Indra Aulia; Amril Mutoi Siregar; Leonard Goeirmanto
Faktor Exacta Vol 18, No 2 (2025)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v18i2.26078

Abstract

This paper examines the vulnerability of machine learning models to adversarial examples: inputs that are subtly manipulated to deceive a model into making incorrect predictions. Although deep learning has demonstrated remarkable performance across various tasks, the security of these models remains a significant challenge. This study provides a comprehensive review of various methods for generating adversarial examples, a classification of attack techniques, and corresponding defense strategies, including both active and passive approaches. The findings indicate that a combination of several defense techniques is significantly more effective in enhancing model robustness compared to any single approach. This research is expected to provide a foundation for the development of more secure and reliable machine learning models for critical applications.
Co-Authors Ade Kurniawan Albert Jofrandi Hutapea Amril Mutoi Siregar Ely Aprilia Leonard Goeirmanto Siregar, Amril Mutoi Surjandy