The Image Extraction Using the HSV Method to Determine the Maturity Level of Palm Oil Fruit with the k-nearest Neighbor Algorithm

The Image Extraction Using the HSV Method to Determine the Maturity Level of Palm Oil Fruit with the k-nearest Neighbor Algorithm

oil palm; maturity classification; HSV; k-NN; confusion matrix

The oil palm is one of the monocot oil-producing plants in Indonesia. Sorting errors in oil palm fruit is caused by a sorter error
when distinguishing the color of ripe and immature oil palm fruit. In addition to inefficient time, the area of oil palm plantations
is also a factor causing the sorter to make mistakes in sorting. This study aims to produce a system that can classify oil palm
maturity based on feature extraction of hue, saturation, and value (HSV) color features. The HSV method is used to produce
color characteristics from the image of the oil palm fruit. The classification of oil palm fruit maturity is classified using the KNearest Neighbor (KNN) algorithm with a dataset of 400 oil palm fruit image data with a data sharing ratio of 70% training
data and 30% test data. 280 image data were used as training data which is divided into 140 image data of ripe oil palm fruit
140 image data of immature oil palm fruit and 120 image data of oil palm used as test data which is divided into 60 image data
of ripe oil palm and 45 image data unripe palm oil. Based on the result of tests that have been carried out using a confusion
matrix with varied k values, namely, 5 and 7, the average accuracy is 94.16%

Mohammad Yazdi Pusadan, Indah Safitri, Wirdayanti

http://jurnal.iaii.or.id

Professional Organization Ikatan Ahli Informatika Indonesia (IAII)/Indonesian Informatics Experts Association

December 2023

Sri Wahyuni

ISSN Media Electronic: 2580-0760

PDF

English

Text