Deteksi Kematangan Buah Tomat Menggunakan Convolutional Neural Network Berdasarkan Ekstraksi Fitur Warna dan Tekstur - Jayanegara, Adi Prabu; Sejati, Rr. Hajar Puji; Sanjaya, Fadil Indra (Forum Kerjasama Pendidikan Tinggi (FKPT), 2025)
Determining the ripeness level of tomatoes is a crucial aspect in ensuring consumption quality. However, the methods commonly used by the public are still manual and subjective, relying on visual observations of color and surface texture. This limitation leads to a high rate of errors in selecting fruits suitable for consumption or processing purposes, potentially resulting in food waste, economic loss, and decreased efficiency within the agricultural supply chain. Without the development of a technology-based assistance system, these impacts will continue to recur and may threaten food security on both micro and macro scales. As a solution, this study develops an intelligent system based on digital image processing to detect tomato ripeness levels. The system utilizes color feature extraction using the HSV histogram and texture feature extraction using the Local Binary Pattern (LBP), which are then processed through a Convolutional Neural Network (CNN) model for image classification. The results show that the system achieves an accuracy of 95.12%, outperforming (or matching) state-of-the-art end-to-end CNN-based methods on the same or similar datasets, demonstrating the effectiveness of HSV-LBP features. The implementation of this system is expected to help users make more accurate decisions when selecting tomatoes according to their needs, reduce waste, and improve consumption efficiency.