Implementasi Metode K-Means Clustering Terhadap Identifikasi Tingkat Kematangan Buah Kelapa Sawit

Muhammad Ikhsan Al-Arrafi(1*), Ahmad Syarif(2), Edo Permata(3), Sandra Yulihartati(4), Rini Sovia(5),

(1) Universitas Putra Indonesia “YPTK” Padang, Indonesia
(2) Universitas Putra Indonesia “YPTK” Padang, Indonesia
(3) Universitas Putra Indonesia “YPTK” Padang, Indonesia
(4) Universitas Putra Indonesia “YPTK” Padang, Indonesia
(5) Universitas Putra Indonesia “YPTK” Padang, Indonesia
(*) Corresponding Author

Abstract


Oil palm is a key commodity in the vegetable oil industry, and the ripeness level of oil palm fruit significantly affects the quality of the produced oil. Manual identification of fruit ripeness is often inaccurate and time-consuming. Therefore, this study aims to implement the K-Means Clustering method to classify oil palm fruit based on its ripeness level. The K-Means Clustering method is used to group oil palm fruits into several categories based on color, texture, and size features. Image processing techniques are applied to extract relevant features from oil palm fruit images. The K-Means algorithm is then utilized to form optimal ripeness clusters. The Clustering effectiveness is evaluated using the Silhouette Score and Davies-Bouldin Index methods. The results show that the K-Means Clustering method can classify oil palm fruit with relatively high accuracy compared to conventional methods. This implementation is expected to assist the palm oil industry in improving efficiency and oil quality by providing a more accurate and automated ripeness identification process

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DOI: https://doi.org/10.30645/kesatria.v6i2.579

DOI (PDF): https://doi.org/10.30645/kesatria.v6i2.579.g574

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