https://penerbitgoodwood.com/index.php/jkbpm/issue/feed 2026-02-20T10:26:39+07:00 Open Journal Systems <p align="justify"><strong>Jurnal Kecerdasan Buatan dan Pembelajaran Mesin (JKBPM)</strong> is an international, peer-reviewed, and open-access journal that publishes high-quality research papers, review articles, and case studies in the fields of Artificial Intelligence and Machine Learning. The journal aims to foster scientific advancement by providing a platform for scholars and professionals to disseminate innovative ideas, computational methods, and empirical findings.</p> https://penerbitgoodwood.com/index.php/jkbpm/article/view/6025 2025-12-25T15:25:55+07:00 Arya Gunawan aryakk008@gmail.com Ketut Artaye artajaya@darmajaya.ac.id

<p><strong>Purpose: </strong>This study aimed to develop a web-based system to detect the freshness level of Cavendish bananas using image-based classification to help assess fruit quality and reduce losses during distribution.</p> <p><strong>Methodology/approach: </strong>This study used a dataset of 1,030 labeled images of Cavendish bananas categorized into four classes: fresh, ripe, unripe, and rotten. Image preprocessing techniques, such as resizing, normalization, and data augmentation, were applied. A Convolutional Neural Network (CNN) model was trained using Python with machine learning libraries, and the trained model was integrated into a web application built with Streamlit to enable real-time image-based predictions.</p> <p><strong>Results: </strong>The proposed CNN model achieved an accuracy of 99%, with average precision, recall, and F1-score values of 0.99. The web-based system successfully provided real-time freshness predictions through image uploads or camera captures.</p> <p><strong>Conclusions:</strong> This study confirms that the web-based CNN system effectively detects Cavendish banana freshness with high accuracy. The integration into a Streamlit application enables real-time and practical use for quality assessment. The system demonstrates the potential of machine learning to support agricultural quality control, reduce post-harvest losses, and enhance food safety.</p> <p><strong>Limitations: </strong>This study was limited to Cavendish banana images and controlled image conditions; therefore, performance may vary when applied to other banana varieties or different lighting environments.</p> <p><strong>Contribution</strong><strong>s</strong><strong>: </strong>This study contributes to the application of machine learning and computer vision in agricultural quality assessment. The system provides a practical tool for farmers, distributors, and consumers to objectively evaluate banana freshness, reduce economic losses, and support public health.</p>

2026-02-20T00:00:00+07:00 Copyright (c) 2026 Arya Gunawan, Ketut Artaye