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S. Kuba, Muhammad Syafa'at
Teknik Pengairan, Fakultas Teknik, Universitas Muhammadiyah Makassar
Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mechanical Engineering Social Sciences Transportation Other

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KLASIFIKASI TANAMAN OBAT TRADISIONAL BERBASIS CITRA BUAH DAN DAUN Kusumawardani, Nurul; Danuputri, Chyquitha; Darniati; Faisal, Muhammad; A.M Hayat, Muhyiddin; S. Kuba, Muhammad Syafaat; Anggreani, Desi
PROGRESS Vol 18 No 1 (2026): April
Publisher : P3M STMIK Profesional Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56708/progres.v18i1.534

Abstract

Indonesia is a megabiodiversity country with extensive use of traditional medicinal plants; however, plant identification in natural environments remains largely manual and error-prone. Recent advances in deep learning, particularly Vision Transformer (ViT), provide a promising solution by effectively capturing global spatial features for image classification. This study applies a ViT-Base/16 model to automatically classify fruit and leaf images of Indonesian medicinal plants. The dataset comprises 1,000 field-collected images from Galung Village, West Sulawesi, covering 20 classes (10 medicinal and 10 non-medicinal plants). The model was fine-tuned using the AdamW optimizer with a learning rate of 2×10⁻⁵ and trained for 30 epochs with cosine annealing. The proposed approach achieved high performance, with 99.33% accuracy, 99.41% precision, 99.33% recall, and a 99.33% F1-score, while binary classification between medicinal and non-medicinal plants reached 100% accuracy. The system was deployed as a Flask-based web application, demonstrating reliable functionality and practical response times. Overall, the results confirm the effectiveness of Vision Transformer for medicinal plant classification under natural conditions and highlight its potential to support digital documentation, education, and the preservation of local ethnobotanical knowledge.
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 . Darniati Abdul Rahman, Titik Khawa Ade Irfan Agus, Fauziah Agusalim, Agusalim Agusalim, M. Ahmad Syafi'i Zulmi Akbar, Syahril Akrar Syah Al Imran, Hamzah Ali, Muhammad Yunus Amal, Citra Amalia AMRI, MUH ULIL Amrullah Anas, Andi Bunga Tongeng Andi Bunga Tongeng Anas Andi Makbul Syamsuri Andi Rahmat Anggreani, Desi Anis Dandi Juandani Antaria, Sukmasari Arman, Muayyanah Arsyad, Zulfikar Asnita Virlayani, Asnita Bakti, Rizki Yusliana Bakti, Rizki Yusliana Berni Satria Gemilang Danuputri, Chyquitha Danuputri, Chyquitha Djunur, Lutfi Hair Fachrim Irhamna Rachman Faeruddin, Muhammad Asygar Fausiah Latief Fauzan Hamdi Fithriyah Arief Wangsa Gaffar, Farida Gemilang, Berni Satria Habi Talib, Emil Agusalim Hamzah Al Imran Hasanuddin, Novianingsih Hayat, Muhyiddin A M Hayat, Muhyiddin A M Irma Suryana Irwan Irwan, Muhammad Ahlil Khairi Juandani, Anis Dandi Juliandro, Juliandro Karim, Nenny Kasmawati Kato, Muh Alvin Achmad Kusumawardani, Nurul Lantara, Andi Bintang Latief, Fausiah Lisnawati Lisnawati LUKMAN ANAS Lukman Lukman Lukman, Lukman Lutfi Hair Djunur M Agusalim Ma'rupah, Ma'rupah Mahmud, Rajib Mahmuddin Mahmuddin Mahmuddin Mohamad Munawir Muh Alvin Achmad Kato Muhammad Aminuddin, Muhammad Muhammad Faisal Muhyiddin A.M Hayat Mujidah, Jihan Izzathul Munawir, Mohamad Nenny Nenny Nenny Nenny, Nenny Nini Apriani Rumata Nur Alam Nur Rahman, Ahmad Nurdiansah, Nurdiansah Nurnawaty Nurnawaty Nurnawaty, Nurnawaty Panguriseng, Darwis Pawara, Ismail Putri, Adriani Rahmasari, St. Rajib Mahmud Risman, Andi Muh. Riswal Karamma Riswal Karamma Sahril Sandi, Andi Muhammad Sari, Reski Anugrah Sarina Siba, Ikhsan Syah, Akrar Syahrul, Syahrulrahman Syamsuri, Andi Makbul Syamsuri, Andi Maqbul Syarifuddin, Nur Annisa T Karim, Nenny TAIBA, ANDI MAWADDA TAIBA MAWADDA Taufiq, Muh Titin Wahyuni Toha Andi Lala Usman, Sucipto Wangsa, Fithriyah Arief Zulfikar Arsyad Zulhaidir DJ, Muhammad Zulmi, Ahmad Syafi'i