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All Journal Arcitech: Journal of Computer Science and Artificial Intelligence
Firna Yenila
Universitas Putra Indonesia “YPTK” Padang
Computer Science & IT

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Model Klasifikasi Tingkat Kematangan Sayur Hijau Menggunakan Ekstraksi Fitur Warna dan Convolutional Neural Network Rini Widyastuti; Firna Yenila; Eko Syaputra; Wandi Syahindra; Murlena
Arcitech: Journal of Computer Science and Artificial Intelligence Vol. 6 No. 1 (2026): June 2026
Publisher : Institut Agama Islam Negeri (IAIN) Curup

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29240/arcitech.v6i1.17403

Abstract

The maturity level of green vegetables is an important factor affecting product quality, market value, and shelf life. Maturity identification is generally performed visually based on leaf color changes, making the assessment subjective and potentially inconsistent. This study aims to develop a classification model for green vegetable maturity levels using a combination of color feature extraction and a Convolutional Neural Network (CNN) to provide a more objective and accurate system. The research began with image acquisition of green vegetables categorized into three maturity levels: immature, mature, and overripe. Preprocessing included image resizing, normalization, and segmentation. Color feature extraction was performed using RGB and HSV color spaces to represent maturity conditions. The dataset was divided into training and testing sets with a 90:10 ratio and processed using a CNN architecture. Model performance was evaluated using accuracy, precision, recall, and F1-score. Results showed that the proposed model achieved 95.2% accuracy, 94.8% precision, 95.6% recall, and 95.1% F1-score. These findings indicate that combining color features and CNN effectively supports automated vegetable sorting and quality control systems.
Co-Authors Eko Syaputra Murlena Rini Widyastuti Wandi Syahindra