Klasifikasi Kematangan Buah Kelapa Sawit Menggunakan Model Yolov8 Berbasis Deep Learning - Muna, Mukhes Sri; Setiyo, Yohanes; Wirawan, I Putu Surya; Syarovy, Muhdan; Jaya, Gigieh Henggar (Program Studi Teknik Pertanian, Fakultas Pertanian, Universitas Jenderal Soedirman, 2025)
Determining the ripeness level of oil palm fruit is a crucial aspect in enhancing the efficiency and quality of palm oil production. To date, most ripeness classification processes are still manually conducted, leading to inconsistencies and human error. This study aims to develop an oil palm fruit ripeness classification model using YOLOv8, a state-of-the-art deep learning architecture known for its excellence in computer vision tasks. The dataset consists of six ripeness classes, divided into training, validation, and testing sets sourced from the Roboflow platform. The training process involved five YOLOv8 sub-models with optimized parameter configurations. Evaluation was carried out using MAPE and confidence score metrics to measure prediction accuracy. The results showed that all sub-models successfully classified fruit ripeness with high accuracy, with YOLOv8l-cls achieving the lowest MAPE value of 0.01167. These, confirm that the YOLOv8-based approach is highly effective in supporting automated classification of oil palm fruit ripeness, offering faster, more accurate, and consistent results, and holds strong potential for widespread application in the plantation industry.