Setya Putra Adenugraha
Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika, Jakarta

Published : 1 Documents Claim Missing Document
Claim Missing Document

Found 1 Documents

Klasifikasi Kematangan Buah Pisang Ambon Menggunakan Metode KNN dan PCA Berdasarkan Citra RGB dan HSV Setya Putra Adenugraha; Veri Arinal; Dadang Iskandar Mulyana
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i1.3287


Banana fruit or in scientific language is called Musa Paradisiaca. One type of banana that is easy to grow and develop in the tropics of Indonesia is the Cavendish Banana or commonly known as the Ambon Banana. The quality of Ambon bananas must be maintained because Ambon bananas grown in Indonesia also supply the needs of foreign markets. The quality of bananas is very influential from the time of harvesting, the level of maturity of bananas is related to marketing reach. Basically, farmers use manual methods in determining the maturity level of Ambon bananas, so there are several factors that can make the classification results less accurate. Based on these problems, a system was made to classify the maturity level of Ambon bananas by utilizing the RGB and HSV color features using the K-Nearest Neighbor (KNN) method. Classification uses image processing by utilizing matlab software for making a classification system with 3 classes, namely raw, ripe, and overcooked. The results of this study are expected to help Ambon banana farmers in classifying the maturity level of Ambon bananas. In this study using data obtained from the place of observation. The data used in this study were 41 data which were divided into 30 training data and 11 test data. The data is classified using the KNN method by measuring the distance to the nearest neighbor with a value of K=5. From this study, the results obtained accuracy of 90.9% with the results of the classification of test data as many as 10 data received accurate classification results and 1 data received inaccurate classification results.