Deteksi Tingkat Kematangan Tandan Buah Segar Kelapa Sawit dengan Algoritme K-Means

Wahyuni Eka Sari; Muslimin Muslimin; Annafi Franz; Putu  Sugiartawan

doi:10.31598/sintechjournal.v5i2.1146

Authors

Wahyuni Eka Sari Politeknik Negeri Samarinda
Muslimin Muslimin Politeknik Negeri Samarinda
Annafi Franz Politeknik Negeri Samarinda
Putu Sugiartawan Institut Bisnis dan Teknologi Indonesia

DOI:

https://doi.org/10.31598/sintechjournal.v5i2.1146

Keywords:

image processing, k-means, clustering, palm fruit, kaltim, kaltara

Abstract

Oil extraction rate (OER) of fresh fruit bunches (FFB) of palm oil is depend on the stage of ripeness. The process of detecting the ripeness of oil palm FFB has difficult by manually. Farmers find it difficult to reach the fruit to detect ripeness with the eye, when the palm tree is tall. So farmers need a system that is able to detect the maturity level of oil palm FFB based on color. The K-Means method is capable of clustering based on the closest mean value to the centroid from a number of objects to cluster k. Data obtained from 2 oil palm plantations in East and North Kalimantan. In this study, the clustering of fresh fruit bunches of oil palm has four levels of maturity based on the calculation of the elbow method. The training data used in this study is 80 data. The test image data used in this study is 40 data. There are 36 appropriate data based on the classification method so the accuracy obtained in grouping using the k-means clustering segmentation method is 90%.

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