Deteksi Tingkat Kematangan Buah Pepaya Menggunakan Convolutional Neural Network dengan Metode Deep Learning

Abstrak

This study developed a detection model for the maturity level of papaya fruit using a Convolutional Neural Network (CNN) with a deep learning approach. This model is designed to identify three levels of papaya fruit ripeness, namely raw, half-ripe, and ripe based on images of fruit taken directly from the garden. The dataset consists of 300 images that are divided into training, validation, and testing data. The test results show that the CNN model used is able to classify the level of maturity with high accuracy. This system is expected to help farmers and traders determine the maturity level of papaya fruit more accurately and efficiently than the traditional manual method, which relies on visual observation and human senses, thereby improving the quality and productivity in the process of harvesting and distributing papaya fruit.