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Syawal Gultom
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Analisis Citra Fitur Wajah Untuk Deteksi Dini Depresi  Dengan Teknik Hibrida Berdasarkan GabunganFitur Convolutional Neural Network (CNN) Syahputra, Hermawan; Gultom, Syawal; Arnita; Kiswanto, Dedy; Batubara, Saatira Hilma; Manurung, Jeremia
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 13 No 4: Agustus 2026
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.134

Abstract

Depresi merupakan salah satu masalah kesehatan mental yang umum dan menjadi penyebab utama disabilitas di seluruh dunia. Deteksi dini sangat penting karena gejala awal sering tidak dikenali atau diabaikan. Penelitian ini mengusulkan sistem deteksi dini depresi berbasis kecerdasan buatan melalui analisis citra wajah menggunakan pendekatan hibrida Convolutional Neural Network (CNN). Data diperoleh dari rekaman wawancara klinis pasien dengan diagnosis depresi dan individu normal. Ekstraksi fitur dilakukan menggunakan tiga arsitektur CNN, yaitu VGG16, ResNet101V2, dan MobileNetV3, yang kemudian digabungkan untuk membentuk representasi fitur gabungan. Selanjutnya, algoritma Random Forest digunakan sebagai klasifikator. Hasil eksperimen menunjukkan bahwa kombinasi VGG16 dan MobileNetV3 memberikan performa terbaik dengan akurasi 94.55%, presisi 98.04%, dan spesifisitas 98.18%. Temuan ini menunjukkan bahwa pendekatan hibrida CNN dengan Random Forest berpotensi menjadi metode efektif untuk mendukung deteksi dini depresi berbasis citra wajah.   Abstract Depression is a major mental health disorder and one of the leading causes of disability worldwide. Early detection is crucial since initial symptoms are often overlooked or unrecognized. This study proposes an artificial intelligence–based early detection system for depression through facial image analysis using a hybrid Convolutional Neural Network (CNN) approach. Facial data were obtained from clinical interview recordings of patients diagnosed with depression and healthy individuals. Feature extraction was carried out using three CNN architectures VGG16, ResNet101V2, and MobileNetV3 whose features were then combined into a joint representation. A Random Forest algorithm was employed for classification. Experimental results indicate that the hybrid model combining VGG16 and MobileNetV3 achieved the best performance, with 94.55% accuracy, 98.04% precision, and 98.18% specificity. These findings demonstrate that the proposed CNN Random Forest hybrid approach is a promising method for supporting early depression detection based on facial features.
Co-Authors . Sherly Agum Budianto Aida Fitriani Sitompul Akmaluddin Akmaluddin Aman Simare-mare Ani Sutiani Arif Rahman Arif Rahman Arnita Batubara, Saatira Hilma Bornok Sinaga Calen, Calen Darmawati Darmawati Darwin . Darwin Darwin Darwin Darwin Darwin Darwin Dedy Kiswanto Dharma, Edy Edi Surya, Edi Edi Syahputra Eka Daryanto Eka Daryanto, Eka Elly Romy Erbin Chandra Fakhrurrozi Fakhrurrozi Harianja, Anrygif Fantri Hasibuan, Rani Chyntia Hermawan Syahputra Hotmaida Sidabutar Ida Mariani Pasaribu Kevin Indajang Khairunnisa Lumban Gaol Kisno Kisno Lasma Ivana Maria Hutasoit Lubis, Lia Maharani M. Basir. S. Hasibuan Maidar, Maidar Manurung, Jeremia Mardhani, Lies Eka Maria Silaen Marlina Sihombing, Humiras Betty Martina Restuati Mei Yati Simatupang Muhammad Syahril Harahap Nanda Pratiwi Nanda Pratiwi Nasrun Nasrun NETTI KARIANI MENDOFA Nurhasanah Siregar Panjaitan, Darma Ebson Rambe, Imelda Wardani Rosmala Dewi Rosnelli Rosnelli, Rosnelli Roy Siagian Saloma Banjarnahor Sanimah Saut Purba sherly sherly Sihombing, Humiras Betty Marlina Simanjorang, Mangaratua M Siregar, Putri Auliany Siti Salamah Br Ginting Sukarman Purba Sumaryanto Sumaryanto Sutiani, Ani Syahreza, Hadian Firman Syaputra, Hermawan Umi Kalsum Weni Ayu Sunita Zandroto Wildansyah Lubis Yati Simatupang, Mei Yohanes Kefi Yuniarto Mudji Susatyo Yuniarto Mudjisusatyo Yuniarto Mudjisusatyo