Support Vector Machine Untuk Pengenalan Bentuk Manusia Menggunakan Kumpulan Fitur Yang Dioptimalkan
DOI:
https://doi.org/10.23887/jstundiksha.v11i1.44437Kata Kunci:
Pengenalan Manusia, Support Vector Machine, Klasifikasi, ANOVAAbstrak
Artikel ini meneliti penerapan sebuah pendekatan pembelajaran mesin bernama Support Vector Machine (SVM) untuk pengenalan bentuk manusia dan bukan manusia. Banyak penelitian telah menunjukkan bahwa kontur siluet sebuah bentuk mengandung informasi bentuk yang esensial. Oleh karena itu, sebuah skema yang cocok, yang kami namai sebagai profil pusat, dikembangkan yang merepresentasikan jarak euclidean antara pusat bentuk dan piksel batas bentuknya. Profil pusat 100 bentuk manusia dan non manusia pada interval 10° diekstraksi dan menghasilkan 36 profil fitur pusat. Profil fitur yang terekstraksi ini kemudian dianalisa dan dikenakan ke proses seleksi fitur untuk mengoptimalkan jumlah profil fitur yang digunakan. Ada dua metode yang digunakan dalam penelitian ini yaitu metode statistik analisis varian (ANOVA) dan metode forward feature selection. Fitur terpilih diatur dari kedua metode tersebut dan digunakan sebagai input untuk SVM. Secara keseluruhan, kemampuan klasifikasi SVM ditemukan tidak berpengaruh lintas tiga fungsi inti yaitu linear, polynomial, dan basis radial gaussian. Metode ANOVA terbukti unggul dibandingkan metode forward feature selection. Setelah dipertimbangkan semuanya, efektifitas SVM sebagai pengklasifikasi terconfirmasi. Kinerja yang sempurna bisa dicapai ketika SVM diberikan sekumpulan fitur yang teroptimasi sebagai input. Temuan dalam penelitian ini membuktikan potensi yang besar dari penerapan SVM pada pendeteksian manusia untuk berbagai penerapan.Referensi
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