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All Journal BERKALA FISIKA Proceedings of KNASTIK Jurnal Simetris Bulletin of Electrical Engineering and Informatics Scientific Journal of Informatics JITK (Jurnal Ilmu Pengetahuan dan Komputer) Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Jurnal Teknik Informatika (JUTIF) Jurnal Pendidikan dan Teknologi Indonesia Journal of Electronics Technology Exploration (JOeTEx)
Ipung Permadi
Program Studi Teknik Informatika, Fakultas Sains dan Teknik, Unsoed
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Physics

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Journal : Journal of Electronics Technology Exploration (JOeTEx)

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Milkfish Freshness Detection Based On Eye Images Using Convolutional Neural Network (CNN) With Mobilenetv3 Architecture On A Mobile Application Musaadah, Khalimah; Afuan, Lasmedi; Permadi, Ipung
Journal of Electronics Technology Exploration Vol. 3 No. 2 (2025): December 2025
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joetex.v3i2.649

Abstract

Indonesia has abundant fishery resources, making it one of the world's largest producers and consumers of fish. One of the most commonly consumed types is milkfish (Chanos chanos). Before consumption, it is important to determine the freshness level of the fish. This freshness can be identified using a Convolutional Neural Network (CNN) model with the MobileNetV3 architecture, which is efficient and suitable for mobile application implementation. This study aims to detect the freshness level of milkfish based on eye images using the MobileNetV3 CNN architecture implemented in a mobile application. The dataset used consists of 500 images, divided into training, validation, and testing sets with proportions of 70%, 20%, and 10%, respectively. The data underwent preprocessing, including resizing and image augmentation, to increase data variation. The model was developed using hyperparameter tuning with both random search and grid search methods. The results show that random search achieved better performance with a training accuracy of 92.88%, validation accuracy of 89.90%, and an overall test accuracy of 91%. The trained model was successfully implemented into a mobile application named ScanBang, which can classify the freshness level of milkfish and display its confidence score in a practical and user-friendly manner.
Co-Authors Alfarez Marchelian, Reyno Alfarizi, Rizki Zakaria Arief Kelik Nugroho, Arief Kelik Bintang Pradana Yosua, Panky Didit Suprihanto, Didit Eddy Maryanto Faturrahim, Muhammad Guntur Satya Pramudya Hanifa, Aini Hartono Hartono Iqbal Iqbal Kurniawan, Yogiek Indra Lasmedi Afuan Musaadah, Khalimah Nofiyati Nofiyati, Nofiyati Nur Chasanah Nurul Hidayat Priatna, Irfan Putra Rahmat, Muhammad Aryo Rahayu, Swahesti Puspita Sari, Enjelita Sugito - Tuti Alawiyah Yasifa, Ana Romadhona