Classification of Cocoa Fruit Quality Based on Digital Images Using the Integration of Image Processing with SVM and Random Forest - Iksan, Nur; Mustamin; Muhammad Fajar B (Jurnal EECCIS (Electrics, Electronics, Communications, Controls, Informatics, Systems), 2026)
This study aims to develop an automatic classification model using digital images to assess the quality of cocoa fruit more accurately and efficiently compared to manual methods: The methodology includes image preprocessing, feature extraction of color (mean R, G, B converted to H, S, V) and shape (area, perimeter, aspect ratio, and circularity), followed by splitting the dataset into training, validation, and testing sets with a proportion of 70:15:15. Two machine learning algorithms, namely Support Vector Machine (SVM) and Random Forest (RF), are used to classify cocoa fruit into three quality classes: ripe, unripe, and rotten. The training process is conducted using optimal hyperparameter tuning through Grid Search, specifically with 3-fold cross-validation. The results show that the combination of color and shape features provides the best accuracy of 96%. Therefore, the Random Forest model demonstrates better performance in the developed classification system. The resulting model has the potential to be applied as a decision-support system for automatics and consistent cocoa fruit quality assessment in agricultural or industrial settings.