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All Journal Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Rekayasa: Jurnal Penerapan Teknologi dan Pembelajaran SemanTIK : Teknik Informasi INTECOMS: Journal of Information Technology and Computer Science JURNAL PENDIDIKAN TAMBUSAI J-SAKTI (Jurnal Sains Komputer dan Informatika) Juriti Prima (Jurnal Ilmiah Teknik Industri Prima) JTIM : Jurnal Teknologi Informasi dan Multimedia JISA (Jurnal Informatika dan Sains) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Community Development Journal: Jurnal Pengabdian Masyarakat Jurnal Pendidikan dan Teknologi Indonesia J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Riset dan Aplikasi Mahasiswa Informatika (JRAMI) COMSERVA: Jurnal Penelitian dan Pengabdian Masyarakat Jurnal Algoritma Jurnal Pemberdayaan Masyarakat Jurnal Indonesia Sosial Teknologi Indonesian Journal of Applied Technology Jurnal Indonesia : Manajemen Informatika dan Komunikasi Ulil Albab
Sutopo, Joko
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Religion Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mechanical Engineering Public Health Social Sciences

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Journal : Indonesian Journal of Applied Technology

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Comparison of YOLO-v8 and YOLO-v10 in Detecting Human Facial Emotions Rasyid, Guilliano; Sutopo, Joko
Indonesian Journal of Applied Technology Vol. 2 No. 1 (2025): January
Publisher : Indonesian Journal Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47134/ijat.v2i1.3395

Abstract

This study evaluates the performance of YOLOv8 and YOLOv10 in recognizing human facial emotions. Both state-of-the-art object detection models were trained on a diverse dataset of facial expressions. While YOLOv10 demonstrated superior performance in certain metrics, it required significantly more training time compared to YOLOv8. Both models exhibited effective learning, as evidenced by the steady decrease in training loss. However, both models encountered challenges in accurately recognizing subtle emotions, such as disgust and contempt. To enhance the accuracy and robustness of facial emotion recognition systems, future research should prioritize improving data quality, exploring advanced model architectures, and optimizing hyperparameters.
Co-Authors Abimanyu, Silvan Azindha, Fillah Syakur Charles De Haan Erwan Bali Loko, Hermanus Fadillah, Naufal Syafiq Faizy, Akmal Hartono, Ardan Dwi Hidayati, Anindya Nurindah Ichsan, Muhammad Ichsan Asy'ari Islamey, Daffa Sholah Juhansar Andi Latief Kuswantoro, RB. Hendri Mustaqim Pabbajah Nugroho, Agung Yuliyanto Nurahdika, Ni Putu Irene Pasca Nuraini, Fina Oktafiana, Oni Pramono, Owen Denpas Prasetio, Sixtus Albern Joand Rachman, Annisa Rasyid, Guilliano Santoso, Muhamad Agung Sinaga, Thomas Sunandar, Nandi Sunardi Sunardi Supriyo, Sarwo Susilo, Tarsisius Syamaidzar, Fata Nurlaudza' 'aza Syukor Mohamad Jaya, Abdul Taalungan, Anggita Taufiq Rohman, Ahmad Wahyanti, C. Murni Wisnu, Aditya Indra Zaman, Komaruz