Classification of Matoa Fruit Ripeness Levels Using PCA and KNN Methods Based on RGB Color

Abstract

Matoa fruit or in scientific language Pometia Pinnata is a fruit that is quite popular and is spread in several regions. Matoa fruit has several benefits such as for health, processed drinks, and for consumption. However, up to now, matoa fruit farmers are still sorting the quality of ripe matoa fruit using methods that are still manual, which can cause errors and mistakes in sorting the quality of matoa fruit. Based on this problem, this research developed a system that is capable of classifying the maturity level of matoa fruit using RGB feature extraction and using the Principal Component Analysis (PCA) and K-Nearest Neighbor (KNN) methods. This research uses a dataset of 67 data, namely 42 training data and 25 test data consisting of 3 classes, namely raw, mature, and fully cooked. Data classification that applies KNN with the nearest neighbor value, namely K=3, gets an accuracy result of 92%, with 23 image being classified correctly and 2 image being classified incorrectly.