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All Journal Jurnal Teknologi Informasi dan Pendidikan JOURNAL OF APPLIED SCIENCE (JAPPS)
Aulia, Achmad Indra
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Education Engineering

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Perkembangan Metode Pembelajaran Mesin pada Sistem Deteksi Dini Tsunami Aulia, Achmad Indra
JOURNAL OF APPLIED SCIENCE (JAPPS) Vol 5, No 2 (2024): Journal of Applied Science (JAPPS)
Publisher : Institut Teknologi Sains Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36870/japps.v5i2.380

Abstract

Tsunamis pose a significant threat to coastal areas, particularly in seismically active regions like Indonesia. The Tsunami Early Warning System (TEWS) plays a vital role in mitigating the impact of such disasters by providing timely and accurate warnings. This paper presents a comprehensive survey of TEWS developments, focusing on seismic parameter-based methods and machine learning approaches. The study reviews current methodologies, highlights their strengths and limitations, and identifies research opportunities for improving TEWS performance. The survey methodology involves collecting literature from leading databases such as IEEE Xplore, Springer, and Scopus, using keywords related to tsunami detection and warning systems. The selected articles were analyzed based on detection methods, seismic parameters, and system performance in terms of accuracy, computational efficiency, and handling imbalanced data. Key findings include the high accuracy of simulation-based systems like InaTEWS for predefined scenarios but their limitations in unexpected cases. Machine learning methods offer significant potential to address these gaps, but challenges remain regarding real-time processing and imbalanced data handling. Future research directions include developing time-efficient machine learning models for near-field tsunami detection, optimizing algorithms for imbalanced data, and integrating seismic with non-seismic data to enhance prediction accuracy. This paper provides insights for researchers and practitioners aiming to advance TEWS technology, contributing to more effective disaster mitigation strategies.
Lightweight YOLO Models for Robust Facial Expression Detection Aulia, Achmad Indra; Hutapea, Albert Jofrandi; Siregar, Amril Mutoi; 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.
Co-Authors Hutapea, Albert Jofrandi Siregar, Amril Mutoi Surjandy