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All Journal Journal of Artificial Intelligence and Digital Business
Wa Ode Asriyani
Universitas Halu Oleo
Computer Science & IT Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering

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MobileNetV2 Hyperparameter Optimization for Freshness Classification of Tuna Fish Based on Fish Eye Imagery Using Optuna Tree-structured Parzen Estimator (TPE) Wa Ode Asriyani; Muhammad Ihsan Sarita; Sutardi Sutardi
RIGGS: Journal of Artificial Intelligence and Digital Business Vol. 5 No. 2 (2026): Mei-Juli
Publisher : Prodi Bisnis Digital Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/riggs.v5i1.11506

Abstract

Manually assessing the freshness of tuna (Euthynnus affinis) within traditional auction markets is frequently subjective, inconsistent, and prone to human error. To address these limitations, this study implemented a robust Deep Learning model utilizing the MobileNetV2 architecture to classify fish freshness levels based on ocular imagery into three distinct categories: Fresh, Less Fresh, and Not Fresh. Although the MobileNetV2 architecture is inherently reliable for image classification, conventional manual hyperparameter tuning often leads to suboptimal model configurations and severe overfitting challenges. As a strategic solution, this study implemented the advanced Tree-structured Parzen Estimator (TPE) algorithm through the Optuna framework to automatically optimize critical hyperparameters, including learning rate, dropout rate, optimizer type, and the fine-tune depth of the convolutional layers. Empirical testing results rigorously proved that the strategic integration of Optuna TPE with the MobileNetV2 model successfully provided a significant performance boost. The optimized model achieved an accuracy of 93.51%, representing a substantial increase of 32.04% compared to the 61.47% accuracy observed in the manual baseline model. Correspondingly impressive improvements were recorded in key performance metrics, specifically achieving a Precision of 93.64%, a Recall of 93.37%, and an F1-Score of 93.49%. In conclusion, this research demonstrates that automated hyperparameter optimization using the Optuna TPE framework significantly enhances the reliability, efficiency, and generalization capability of Deep Learning models, establishing a highly accurate and practical solution for the automated detection of tuna freshness in real-world fishery environments.
Co-Authors Muhammad Ihsan Sarita Sutardi Sutardi