Identifying Cocoa ripeness using K-Nearest Neighbor (KNN) Method
Hendra Yufit Riskiawan, Trismayanti Dwi Puspitasari, Faik Inayatul Hasanah, Nanang Dwi Wahyono, Mokhamad Fatoni Kurnianto · 2018 International Conference on Applied Science and Technology (iCAST) · 2018
There are three level maturity of cocoa namely mature, medium and raw. Nowadays for identification the process quality of maturity by manually, allowing errors to occur and take a time. So technology is needed to help identify cocoa maturity. One technology that can be used is digital image processing techniques by utilizing several techniques such as image segmentation and color extraction which aims to improve image quality. The process of identifying the quality of cocoa maturity using the K-Nearest Neighbor method (KNN). KNN is a method uses a supervised algorithm that stores all available cases and classifies new cases based on a similarity measure. The purpose of this algorithm is to identify new objects based on attributes and training data. The accuracy of this application is 100%.