IDENTIFIKASI PENYAKIT TOMAT MENGGUNAKAN ALGORITMA KNN BERDASARKAN SEGMENTASI ENHANCED K-MEANS CLUSTERING

ALVIN, ANA TINCE SIREGAR, MONICA SARI SINAGA (2022) IDENTIFIKASI PENYAKIT TOMAT MENGGUNAKAN ALGORITMA KNN BERDASARKAN SEGMENTASI ENHANCED K-MEANS CLUSTERING , SKRIPSI, UNIVERSITAS PRIMA INDONESIA

ABSTRAK

Image segmentation is an important process in identifying tomato diseases. The technique that is often used in this segmentation is k-means clustering. One of the main problems in this technique is the case of local minima, where the cluster that is formed is not suitable due to the incorrect selection of the initial centroid. In image data, this case will have an impact on poor segmentation results because it can erase parts that are actually important to be lost or there is still background in the recognition process, which has an impact on decreasing accuracy results. In this research, a method for image segmentation will be proposed using the k-means clustering algorithm, which has been added with the cosine similarity method as the proposed contribution. The use of the cosine method will determine the initial centroid by calculating the level of similarity of each image feature based on color and dividing them into several categories (low, medium, and high values). Based on the results obtained, the proposed algorithm is able to segment and distinguish between leaf and background images with good results. Accuracy results with the kNN method and the proposed method for identifying tomato diseases reached a value of 94.90%. While the results obtained by identifying tomato diseases using the kNN method without segmentation obtained an accuracy of 90.22%, using k-means clustering segmentation obtained an accuracy of 92.46%.

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