KLASIFIKASI KONDISI MENTAL MAHASISWA DARI CITRA EKSPRESI WAJAH DEEP SIAMESE NETWORK
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https://doi.org/10.23960/jitet.v14i1.8362Abstract Views: 28 File Views: 15
Keywords:
Deteksi Mental Dini; Ekspresi Wajah; Deep Siamese Network; EmosiAbstract
Kesehatan mental mahasiswa merupakan aspek penting yang perlu diperhatikan seiring meningkatnya tekanan akademik, sosial, maupun emosional yang mereka hadapi. Deteksi dini kondisi mental masih banyak mengandalkan metode konvensional seperti kuesioner, yang dinilai kurang efisien dan bersifat subjektif. Untuk mengatasi hal tersebut, penelitian ini mengusulkan pendekatan baru dengan memanfaatkan Deep Siamese Network (DSN) guna mengklasifikasikan kondisi mental mahasiswa berdasarkan ekspresi wajah. Dataset citra wajah dikumpulkan dari mahasiswa dengan berbagai ekspresi, kemudian dikategorikan ke dalam tiga kondisi mental utama, yaitu normal, cemas, dan stres. DSN digunakan untuk mengukur tingkat kemiripan antar ekspresi wajah sehingga mampu membedakan kondisi mental berdasarkan pola visual yang halus. Proses penelitian meliputi tahap preprocessing citra, ekstraksi fitur, pembentukan pasangan data, pelatihan model, serta evaluasi menggunakan metrik akurasi. Hasil pengujian menunjukkan bahwa sistem yang dibangun mampu mencapai tingkat akurasi 89%, sehingga dinilai cukup andal untuk dijadikan alat bantu dalam pemantauan kesehatan mental mahasiswa secara real-time.
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