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INDONESIA
EDUMATIC: Jurnal Pendidikan Informatika
Published by Universitas Hamzanwadi
ISSN : -     EISSN : 25497472     DOI : 10.29408
Core Subject : Science, Education,
Computer Science & IT Education
EDUMATIC: Jurnal Pendidikan Informatika (e-ISSN: 2549-7472) adalah jurnal ilmiah bidang pendidikan informatika yang diterbitkan oleh Universitas Hamzanwadi dua kali setahun yaitu pada bulan Juni dan Desember. Adapun fokus dan skup jurnal ini adalah (1) Komputer dan Informatika dalam Pendidikan; (2) Model Pembelajaran dan Model TIK; (3) Pengembangan Media Pembelajaran Berbasis Teknologi Informatika; (4) Interaksi Manusia dan Komputer; (5) Sistem Informasi dan Teknologi Informasi.
Arjuna Subject : -
Articles 472 Documents
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Benchmarking CNN and YOLO Models for Automated Classification of Fish Freshness I Gede Andika Diana Putra; I Gede Aris Gunadi; I Made Gede Sunarya
Jurnal Pendidikan Informatika (EDUMATIC) Vol 10 No 2 (2026): Edumatic: Jurnal Pendidikan Informatika (IN PRESS)
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v10i2.35019

Abstract

Fish freshness assessment is essential for ensuring food quality and consumer safety; however, conventional visual inspection remains subjective and inconsistent. Although deep learning has shown promising performance in image classification, standardized benchmarking of Convolutional Neural Networks (CNN) and YOLOv8 Classification under identical experimental settings for fine-grained fish freshness classification remains limited. This study compares both models using the same dataset, preprocessing pipeline, augmentation strategy, and training configuration to evaluate predictive performance and computational efficiency. The dataset comprised digital images of tongkol and slungsung fish categorized into four classes: Fresh Tongkol, Rotten Tongkol, Fresh Slungsung, and Rotten Slungsung. Model performance was evaluated using accuracy, precision, recall, F1-score, training time, and inference speed. CNN achieved superior predictive performance with 99.25% accuracy, 99.13% precision, 99.13% recall, and 99.13% F1-score, whereas YOLOv8 Classification achieved 89.88% accuracy, 89.96% precision, 89.88% recall, and 89.89% F1-score. Conversely, YOLOv8 required only 15 minutes for training and 9 ms per image for inference, compared with 23 minutes 20 seconds and 18 ms for CNN. These findings establish a robust benchmark for selecting deep learning architectures by balancing predictive accuracy and computational efficiency in automated fish freshness inspection systems.
Real-Time Student Attendance Recognition Using a Centroid Based MTCNN–ArcFace Framework Suci Putri Widyani; Suroso Suroso; Ahmad Taqwa
Jurnal Pendidikan Informatika (EDUMATIC) Vol 10 No 2 (2026): Edumatic: Jurnal Pendidikan Informatika (IN PRESS)
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v10i2.35448

Abstract

Student attendance remains susceptible to proxy attendance and operational inefficiencies, while many face recognition systems are evaluated only on benchmark datasets and rarely investigate identity representation strategies under real-world educational conditions. This study evaluates a hybrid MTCNN–ArcFace framework incorporating centroid-based identity representation, application-specific threshold calibration, and real-time validation for automated student attendance. A quantitative experimental design was conducted using a locally collected dataset from a secondary school. MTCNN was employed for face detection and alignment, whereas ArcFace with a ResNet-50 backbone generated facial embeddings that were aggregated into centroid templates for identity matching. The framework was assessed through offline performance evaluation and operational deployment. The proposed approach achieved 92.86% accuracy, 97.22% precision, 92.86% recall, and a 93.49% F1-score, with a 4.76% false acceptance rate and 2.38% false rejection rate. In addition, centroid representation reduced template storage requirements and supported efficient real-time recognition using limited enrollment samples. These findings demonstrate that centroid-based identity representation enhances the practicality of deep face recognition for educational attendance systems by improving computational efficiency while maintaining reliable recognition performance in authentic school environments.

Page 48 of 48 | Total Record : 472

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