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All Journal Bianglala Informatika : Jurnal Komputer dan Informatika Akademi Bina Sarana Informatika Yogyakarta Jurnal INSYPRO (Information System and Processing) Ainet : Jurnal Informatika Madaniya Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Jurnal Informatika Progres Jurnal INFOTEL Jurnal Intelek Dan Cendikiawan Nusantara ARUS JURNAL SAINS DAN TEKNOLOGI Journal of Muhammadiyah’s Application Technology
Fachrim Irhamna Rachman
Universitas Muhammadiyah Makassar
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Student Emotion Recognition from Low-Quality Videos Using Multimodal Deep Learning TAIBA, ANDI MAWADDA TAIBA MAWADDA; Bakti, Rizki Yusliana; Faisal, Muhammad; S. Kuba, Muhammad Syafaat; Anas, Lukman; H. T, Emil Agusalim; Rahman, Fahrim I.
JURNAL INFOTEL Vol 18 No 1 (2026): February
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v18i1.1523

Abstract

Emotion recognition plays a critical role in intelligent e-learning systems by enabling adaptive feedback and timely pedagogical interventions based on students’ affective states. However, most existing approaches rely heavily on visual facial cues, which are highly vulnerable to real-world conditions such as low-resolution video, partial facial occlusion, poor lighting, and unstable network connections commonly encountered in online learning environments. These limitations significantly degrade the performance of unimodal deep learning models. To address this challenge, this study proposes a multimodal deep learning framework for student emotion recognition that is robust to low-quality and occluded video input. The proposed model integrates visual and audio modalities through a hybrid architecture, combining a lightweight CNN-based visual feature extractor with a BiLSTM-based speech emotion model. An attention-based fusion mechanism is employed to adaptively weight cross-modal features, allowing the system to compensate for degraded or missing visual information using complementary acoustic cues. Experimental evaluations are conducted using publicly available datasets representative of realistic online learning scenarios, including DAiSEE and RAVDESS, with additional augmentation to simulate varying levels of occlusion and video degradation. The results demonstrate that the multimodal approach consistently outperforms unimodal baselines, particularly under high occlusion conditions, while maintaining computational efficiency suitable for near real-time deployment. These findings confirm that multimodal fusion with attention mechanisms provides a more resilient and practical solution for emotion-aware e-learning systems operating under non-ideal input conditions
Co-Authors Adrianingsih, Rizka Ahmad Faisal ANDIKA SAPUTRA ardi24, ardiansyah_01 Aritmawijaya, Suandi Bakti, Rizki Yusliana Budi Setiawan Chyquitha, Chyquitha Danu Putri Dika, Andika Saputra Ferdiansyah H, chalidah_azzahra00 Habi Talib, Emil Agusalim Hasbir, Syahrul Hermanto, Chalidah Azzahrah Indriani, Lis Iskandar, Aryansyah Kamal, Safutri Kotte, Erick Yusuf La Ode Taufik Ismail La Ode Taufik Ismail LUKMAN ANAS Lukman Lukman Muhammad Faisal Muhammad Faisal Muhyiddin A.M Hayat Mujadilah, Siti Mustakim Mustakim Nur Alam Nur Rahman, Ahmad Nur, Makmur Jaya Reski Awalia S. Kuba, Muhammad Syafa'at S.Kuba, Muhammad Syafaat Salam, Abd Setiawan, Tommy Reynaldy Sri Hastati suandi_17, suandi_aritmawijaya Suhardi, Syahrul Tahir, Gempar Perkasa TAIBA, ANDI MAWADDA TAIBA MAWADDA Titin Wahyuni Uddin , Ardiansyah Virgiawan, David Arian Wibawa. Ar, Arya Yanti, Wilda