Perbandingan Cacat Ubin Keramik dengan Metode K-Nearest Neighbor dan Support Vector Machine
DOI:
https://doi.org/10.33633/tc.v22i4.8897Keywords:
KNN, SVM, Ubin KeramikAbstract
Penentuan kualitas ubin keramik sudah dilakukan secara otomatis dalam beberapa tahun terakhir. Kendala saat penentuan ubin keramik bercacat dapat berpengaruh terhadap penurunan kualitas produk akhir. Isu yang menjadi fokus dalam penelitian yaitu perbandingan metode antara KNN dengan SVM untuk mendeteksi cacat pada ubin keramik untuk mencapai hasil yang lebih akurat. Untuk mengatasi isu ini, proses yang dilakukan meliputi pengumpulan data gambar dari ubin keramik, yang kemudian diikuti oleh tahap preprocessing dan ekstraksi fitur berdasarkan tekstur. Data gambar tersebut kemudian diklasifikasikan dengan metode KNN dan SVM. Temuan dari penelitian ini menunjukkan bahwa pengklasifikasian dengan metode KNN pada k = 3 mampu memberikan hasil yang lebih unggul, yaitu mencapai akurasi 98.947%, sedangkan pengklasifikasian dengan metode SVM hanya mencapai akurasi 85.263%.References
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