Application of K-Nearest Neighbor Method and Support Vector Machine for Noni Fruit Ripeness Classification

Abstract

Noni fruit (Morinda citrifolia) is one of Indonesia’s export commodities. It is available year-round and is well known for its numerous health benefits. Native to Southeast Asia, including Indonesia, noni fruit is widely used in traditional medicine. Typically, the ripeness of noni fruit is determined manually based on visual inspection, which can lead to subjective judgments and inconsistent results. Therefore, this study aims to develop a machine-learning model to classify the ripeness levels of noni fruit. The classification methods employed are K-Nearest Neighbor (KNN) and Support Vector Machine (SVM), utilizing Hue-Saturation-Intensity (HSI) color features and Local Binary Pattern (LBP) texture features. Experimental results show that the KNN algorithm outperforms the SVM algorithm in terms of classification accuracy. The highest accuracy achieved using KNN was 88.62% at k = 11, whereas the best accuracy obtained with SVM using a polynomial kernel was 87.80%, with parameters set to C = 0.1, Gamma = 1, Degree = 5, and coef0 = 1.0. These results were achieved using an 80:20 split ratio for training and testing data.