Membandingkan Analisa Kesalahan Metode K-Means Clustering dan Canopy K-Means Clustering Dengan Data Gambar Terfilter

Ariadi Retno Hayati, Wilda Imama, Puspa Kirana, Vit Zuraida

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)

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CESS (Journal of Computer Engineering, System and Science) is licensed under a Creative Commons Attribution 4.0 International License