Support Vector Machine untuk Pengenalan Bentuk Manusia Menggunakan Kumpulan Fitur yang Dioptimalkan

Authors

  • Alex Wenda UIN SUSKA Riau

DOI:

https://doi.org/10.23887/jstundiksha.v11i1.44437

Keywords:

Pengenalan Manusia, Support Vector Machine, Klasifikasi, ANOVA

Abstract

Banyak terjadinya kendala dalam pendeteksian tubuh. Dengan demikian, beberapa proses pegenalan tubuh menjadi menjadi cukup suli dan metode pengenalan tubuh yang memilki tingkat akurasi yang baik juga dibagi dalam dalam beberapa penelitian. Penelitian ini bertujuan untuk menganalisis penerapan sebuah pendekatan pembelajaran mesin bernama Support Vector Machine (SVM) untuk pengenalan bentuk manusia dan bukan manusia. Jenis penelitian eksperimen. Profil pusat 100 bentuk manusia dan nonmanusia pada interval 10° diekstraksi dan menghasilkan 36 profil fitur pusat. Profil fitur yang terekstraksi ini kemudian dianalisis dan dikenakan 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. Hasil penelitian menunjukkan bahwa efektivitas SVM sebagai pengklasifikasi terconfirmasi. Kinerja yang sempurna bisa dicapai ketika SVM diberikan sekumpulan fitur yang teroptimasi sebagai input. Temuan dalam penelitian ini membuktikan bahwa potensi yang besar dari penerapan SVM pada pendeteksian manusia untuk berbagai penerapan.

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Published

2022-02-27

Issue

Vol. 11 No. 1 (2022)

Section

Articles

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