Pengenalan Wajah menggunakan Principle Component Analysis (PCA) dengan Model Algoritma Machine Learning untuk Mengidentifikasi Jenis Kelamin pada Kartu Identitas Mahasiswa
Abstract
Kajian tentang pengenalan wajah sampai saat ini masih banyak orang yang melakukan eksplorasi, hal ini dapat dilihat dari perkembangan teknologi Computer Vision yang diterapkan diberbagai aplikasi kehidupan. Tujuan penelitian ini adalah untuk mengidentifikasi wajah seseorang berdasarkan ciri atau featur jenis kelamin pada kartu identitas mahasiswa di sebuah perguruan tinggi. Metode yang digunakan melalui pendekatan data sains atau machine learning yaitu SEMMA (Sample, Explore, Modify, Model dan Asses) dengan penerapan pemodelan 2 (dua) algoritma yakni Support Vector Machine (SVM) dan Artificial Neural Network (ANN). Namun pemodelan tersebut juga didukung dengan pre-processing dengan teknik Principle Component Analysis (PCA) yang tujuannya mereduksi dimensi dari berbagai fitur gambar yang ada menjadi fitur yang terpilih. Hasil yang diperoleh dari penelitian ini bahwa adanya peningkatan performance pada aspek akurasi 77.50% untuk algoritma SVM dan 78.10%. Perolehan kinerja tersebut lebih baik dari penelitian sebelumnya yang tidak melibatkan teknik dimensi reduksi menggunakan PCA
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References
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