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Membandingkan Analisa Kesalahan Metode K-Means Clustering dan Canopy K-Means Clustering Dengan Data Gambar Terfilter
Abstract
Pada penelitian ini menganalisa kesalahan yang diperoleh pada data pembelajaran pada data gambar dengan membandingkan metode K-Means Clustering dan metode Canopy K-Means Clustering. Data yang digunakan adalah data gambar yang diujikan pada aplikasi yang dibangun dan menelaah nilai kesalahan pada setiap iterasi. Analisa kesalahan dengan memahami karakteristik formula pada metode K-Means Clustering dan Canopy K-Means Clustering dan menganalisa angka kesalahan berdasarkan formula kedua metode dengan demikian maka karakteristik perolehan error pada metode Canopy K-Means Clustering diperoleh berdasarkan karakteristik formula dari metode tersebut. Dari hasil beberapa uji coba dengan dataset yang data berbeda maka diperoleh rata-rata bahwa metode Canopy K-Means Clustering memiliki nilai kesalahan lebih sedikit sejumlah 0,0264% dibandingkan metode K-Means Clustering dengan Euclidean distance dan rata-rata keberhasilan 85% sesuai kelompok.
Keywords
Canopy K Means Clustering, K Means Clustering, Flowchart, PHP Programming, Image Data Sets, Image Filter.
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DOI: https://doi.org/10.24114/cess.v9i1.50978
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CESS (Journal of Computer Engineering, System and Science)
CESS (Journal of Computer Engineering, System and Science) is licensed under a Creative Commons Attribution 4.0 International License