Vol. 8 No. 2 (2025): May 2025
Artikel

IMPLEMENTASI METODE K-MEANS UNTUK MENGKLASTERISASI JENIS BUAH NAGA DENGAN TEKNIK PENGOLAHAN CITRA

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  • Muhammad Idris,
  • Agung Ramadhanu
Muhammad Idris
Universitas Putra Indonesia YPTK Padang
Agung Ramadhanu
Universitas Putra Indonesia YPTK Padang

Published 2025-05-11

How to Cite

IMPLEMENTASI METODE K-MEANS UNTUK MENGKLASTERISASI JENIS BUAH NAGA DENGAN TEKNIK PENGOLAHAN CITRA. (2025). JOURNAL OF SCIENCE AND SOCIAL RESEARCH, 8(2), 1266-1271. https://doi.org/10.54314/jssr.v8i2.3091

Abstract

Abstract: This research aims to implement the K-Means method for clustering red and yellow dragon fruit types using image processing techniques. Image processing is carried out by utilizing features such as color, texture, and shape from dragon fruit images obtained using a digital camera. The captured images are then processed through preprocessing stages such as conversion to a specific color space  and feature extraction to describe the visual characteristics of the dragon fruit. Afterward, the K-Means algorithm is applied to cluster the images based on the similarity of their features. The clustering results show that the K-Means method is effective in distinguishing between red and yellow dragon fruit types, with a satisfactory accuracy rate. This study contributes to the development of an automated classification system for dragon fruit type identification based on images, which can be applied in agriculture, especially in the processing and distribution of products.

 

Keyword: K-Means Method; Red Dragon Fruit Clustering; Yellow Dragon Fruit; Image Processing.

 

Abstrak: Penelitian ini bertujuan untuk mengimplementasikan metode K-Means dalam mengklasterisasi jenis buah naga merah dan buah naga kuning menggunakan teknik pengolahan citra. Pengolahan citra dilakukan dengan memanfaatkan fitur-fitur warna, tekstur, dan bentuk dari citra buah naga yang diperoleh menggunakan kamera digital. Citra yang diambil kemudian diproses melalui tahap pra-pemrosesan seperti konversi ke ruang warna tertentu dan ekstraksi fitur untuk mendeskripsikan karakteristik visual dari buah naga. Setelah itu, algoritma K-Means diterapkan untuk mengelompokkan citra berdasarkan kemiripan fitur yang dimilikinya. Hasil pengklasteran menunjukkan bahwa metode K-Means efektif dalam memisahkan jenis buah naga merah dan kuning, dengan tingkat akurasi yang memadai. Penelitian ini memberikan kontribusi dalam pengembangan sistem klasifikasi otomatis untuk identifikasi jenis buah naga berdasarkan citra, yang dapat diterapkan dalam bidang pertanian, terutama dalam proses pengolahan dan distribusi produk.

 

Kata kunci:  Metode K-Means; Pengklasteran buah naga merah; Buah naga kuning; Pengolahan citra.

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