Sistem Informasi Deteksi Kualitas Cabai Kopay Berbasis Image Processing dan Deep Learning dengan Python

Abstract

Kopay chili is a local agricultural commodity with high economic value. Manual quality assessment of Kopay chili is still subjective and time-consuming; therefore, a technology-based solution is required. This study develops an information system for detecting the quality of Kopay chili using image processing methods and deep learning based on Convolutional Neural Networks (CNN) implemented in Python. Key image features such as color, texture, and shape are analyzed to support the classification process. The dataset used consists of 2,000 images of Kopay chili categorized into good, medium, and poor quality classes. Experimental results show that the proposed system achieves an accuracy of 94.7% in classifying chili quality. This system provides an efficient and accurate solution for farmers and agribusiness stakeholders in evaluating agricultural product quality.