DOI: https://doi.org/10.21456/vol9iss2pp185-191

Klasifikasi Citra Alat Musik Tradisional dengan Metode k-Nearest Neighbor, Random Forest, dan Support Vector Machine

*Herry Sujaini scopus  -  Program Studi Informatika, Universitas Tanjungpura, Indonesia
Open Access Copyright (c) 2019 JSINBIS (Jurnal Sistem Informasi Bisnis)

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Abstract
Dalam dekade terakhir, metode non-parametrik (algoritma berbasis pembelajaran mesin) semakin banyak dipergunakan dari berbagai aplikasi berbasis pengolahan citra digital. Penelitian ini bertujuan untuk membandingkan tiga metode non-parametrik yaitu Metode k-Nearest Neighbor (kNN), Random Forest (RF), dan Support Vector Machine (SVM) terhadap klasifikasi citra alat musik tradisional di Indonesia yang populer di kalangan masyarakat yaitu : angklung, djembe, gamelan, gong, gordang, kendang, kolintang, rebana, sasando, dan serunai. Dari hasil eksperimen pengklasifikasian dengan metode kNN, RF dan SVM, metode kNN memiliki akurasi yang paling baik. Rata-rata nilai precision ketiga metode tersebut berturut-turut adalah 92,1% untuk kNN, 85,4% untuk SVM, dan 69,4% untuk RF

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Keywords: Klasifikasi; alat musik; k-Nearest Neighbor; Random Forest; Support Vector Machine

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