Prediksi Gender Berdasarkan Citra Mata Menggunakan Metode Convolutional Neural Network, Inception dan MobileNet

Nur Aini; Dewi Yanti Liliana

doi:10.51967/tanesa.v23i1.1272

Authors

Nur Aini Magister Ilmu Komputer, Universitas Nusa Mandiri
Dewi Yanti Liliana Magister Ilmu Komputer, Universitas Nusa Mandiri

DOI:

https://doi.org/10.51967/tanesa.v23i1.1272

Keywords:

Transfer Learning, CNN, InceptionV3, MobileNet

Abstract

Gender manusia dianggap sebagai sifat demografis utama karena berbagai penggunaannya dalam domain praktis. Klasifikasi gender manusia dalam lingkungan yang tidak dibatasi adalah tugas yang rumit karena variasi besar dalam skenario gambar. Karena banyaknya gambar internet, akurasi klasifikasi menderita dari metode pembelajaran mesin tradisional. Penelitian ini bertujuan untuk mengefektifkan proses klasifikasi gender dengan menggunakan konsep transfer learning. Hasil eksperimen menunjukkan bahwa model CNN, InceptionV3 dan MobileNet menggunakan transfer learning tiga hidden layer, setiap hidden layer terdiri dari convolutional layer, aktivasi ReLU dan max-pooling dapat mengklasifikasikan citra berjenis kelamin – laki-laki dan perempuan dengan tingkat yang baik. Akurasi ini juga karena pengoptimal kinerja yang digabungkan selama pelatihan dan pengujian data menggunakan pengoptimal Adam. Hasil pengujian dan evaluasi menggunakan menghasilkan nilai loss terendah untuk model MobileNet sebesar 0,149, dan juga nilai akurasi tertinggi 0,9390. Dengan hasil percobaan ini, maka kesimpulan yang dapat diambil adalah bahwa model MobileNet memiliki tingkat akurasi yang paling tinggi dalam proses pelatihan dan pengujian.

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