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

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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Published

2022-10-28

How to Cite

[1]
W. E. Sari, M. Muslimin, A. Franz, and P. . Sugiartawan, “Deteksi Tingkat Kematangan Tandan Buah Segar Kelapa Sawit dengan Algoritme K-Means”, SINTECH Journal, vol. 5, no. 2, pp. 154-164, Oct. 2022.

Issue

Vol. 5 No. 2 (2022): SINTECH Journal Edition Oktober 2022

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Copyright (c) 2022 Wahyuni Eka Sari, Muslimin, Annafi Franz, Putu Sugiartawan

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