Analisis Pengaruh Kernel SVM terhadap Kinerja Klasifikasi Citra Wajah Asli dan Berfilter
DOI:
https://doi.org/10.29407/mcpwyg31Abstract
Penggunaan filter wajah pada media sosial menyebabkan perubahan karakteristik visual wajah yang dapat memengaruhi proses analisis citra digital. Penelitian ini bertujuan menganalisis pengaruh kernel Support Vector Machine (SVM) terhadap kinerja klasifikasi citra wajah asli dan berfilter. Dataset yang digunakan terdiri dari 100 citra wajah, masing-masing 50 citra wajah asli dan 50 citra wajah berfilter. Tahapan penelitian meliputi deteksi wajah menggunakan Haar Cascade, preprocessing, ekstraksi fitur menggunakan Histogram of Oriented Gradients (HOG) dan Local Binary Pattern (LBP), serta klasifikasi menggunakan kernel Linear, Polynomial, dan Radial Basis Function (RBF). Evaluasi dilakukan menggunakan confusion matrix dengan metrik accuracy, precision, recall, dan F1-score. Hasil penelitian menunjukkan bahwa setiap kernel menghasilkan performa klasifikasi yang berbeda, sehingga pemilihan kernel berpengaruh terhadap kemampuan model dalam membedakan citra wajah asli dan berfilter. Penelitian ini menunjukkan bahwa SVM berpotensi digunakan sebagai metode deteksi filter wajah berbasis machine learning.
Keywords:
citra, HOG, klasifikasi, LBP, SVM##plugins.themes.default.displayStats.downloads##
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