Random Forest Implementation for Indonesian Coffee Identification Based on Digital Image Feature Extraction

Abstract

Accurate identification of coffee types is a critical challenge in Indonesia's coffee industry, which relies heavily on subjective human visual inspection. This study implements the Random Forest ensemble learning algorithm to classify three commercially important coffee species  Arabica (Coffea arabica), Liberica (Coffea liberica), and Robusta (Coffea canephora) using digital image features. The dataset consists of 1,913 real coffee bean images from Roboflow Universe (Coffee Bean Type v1, CC BY 4.0), split into train (1,530), valid (287), and test (96). Feature extraction produces an 11-dimensional vector combining five Gray Level Co-occurrence Matrix (GLCM) texture features (Contrast, Energy, Homogeneity, Correlation, Dissimilarity) computed at four angles and six RGB color statistical features (mean and standard deviation per channel). The model is trained on 1,817 combined train-valid samples with optimal hyperparameters (n_estimators=200, max_depth=None, criterion=gini) selected via Grid Search with 5-fold stratified cross-validation. Evaluation on 96 held-out test samples yields accuracy 98.96%, macro precision 99.05%, macro recall 98.67%, macro F1-score 98.84%, and ROC-AUC 0.9996, with only one misclassification. Cross-validation confirms model stability at 98.07%±0.60%. Feature importance analysis identifies GLCM Energy (21.98%) and Homogeneity (14.57%) as the most discriminative features. The Robusta class achieves perfect classification (100% all metrics). Manual verification confirms that the reported metrics are consistent with the confusion matrix. These results demonstrate that Random Forest with GLCM and RGB color statistical features is effective for image-based coffee classification, achieving higher accuracy than several prior KNN and neural-network-based studies, although those studies differ in task, dataset, and class composition and are therefore not directly comparable.

GS Cited Analysis