Perbandingan Algoritma K-Means dan Fuzzy C-Means untuk Clustering Citra Daun Melon
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Ardinsyah Siregar
*
Institut Pertanian Bogor, Bogor,
Indonesia
- Agus Buono Institut Pertanian Bogor, Bogor, Indonesia
- Karlisa Priandana Institut Pertanian Bogor, Bogor, Indonesia (*) Corresponding Author
Abstract
Melon plants are plants that are susceptible to disease, both diseases caused by viruses and those caused by bacteria. One part of the plant that can be affected by the disease is the leaves. Leaves on diseased plants generally change color which will then affect other leaves and inhibit the development and growth of these plants. This study aims to classify melon plant diseases from melon leaf images. The data used in this study are 160 images of melon leaves which will be grouped into several groups from the healthy group to the unhealthy group. The method used is the Clustering method, namely: K-Means algorithm and Fuzzy C-Means algorithm. Clustering results using K-Means and Fuzzy C-Means can be compared to get the best clustering results. The comparison results show the Fuzzy C-Means Clustering method with a validation value of 0.8359 and the K-Means Clustering method with a validation value of 0.5793. The final result shows that the Fuzzy C-Means Clustering method is better than the K-Means Clustering method because the validation value is close to 1.
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