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All Journal (JELIKU) Jurnal Elektronik Ilmu Komputer Udayana Telematika : Jurnal Informatika dan Teknologi Informasi Jurnal Teknologi Informasi dan Ilmu Komputer Seminar Nasional Informatika (SEMNASIF) Conference SENATIK STT Adisutjipto Yogyakarta Journal of Information Systems and Informatics International Journal of Economics, Business and Accounting Research (IJEBAR) JUSTIN (Jurnal Sistem dan Teknologi Informasi) Masyarakat Berkarya: Jurnal Pengabdian dan Perubahan Sosial
Hari Prapcoyo
Jurusan Teknik Informatika UPN Veteran Yogyakarta
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Mechanical Engineering Public Health

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Journal : Journal of Information Systems and Informatics

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EfficientNet B0 Feature Extraction with L2-SVM Classification for Robust Facial Expression Recognition Akbar, Ahmad Taufiq; Saifullah, Shoffan; Prapcoyo, Hari; Rustamadji, Heru; Cahyana, Nur Heri
Journal of Information System and Informatics Vol 7 No 2 (2025): June
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i2.1071

Abstract

Facial expression recognition (FER) remains a challenging task due to the subtle visual variations between emotional categories and the constraints of small, controlled datasets. Traditional deep learning approaches often require extensive training, large-scale datasets, and data augmentation to achieve robust generalization. To overcome these limitations, this paper proposes a hybrid FER framework that combines EfficientNet B0 as a deep feature extractor with an L2-regularized Support Vector Machine (L2-SVM) classifier. The model is designed to operate effectively on limited data without the need for end-to-end fine-tuning or augmentation, offering a lightweight and efficient solution for resource-constrained environments. Experimental results on the JAFFE and CK+ benchmark datasets demonstrate the proposed method’s strong performance, achieving up to 100% accuracy across various hold-out splits (90:10, 80:20, 70:30) and 99.8% accuracy under 5-fold cross-validation. Evaluation metrics including precision, recall, and F1-score consistently exceeded 95% across all emotion classes. Confusion matrix analysis revealed perfect classification of high-intensity emotions such as Happiness and Surprise, while minor misclassifications occurred in more ambiguous expressions like Fear and Sadness. These results validate the model’s generalization ability, efficiency, and suitability for real-time FER tasks. Future work will extend the framework to in-the-wild datasets and incorporate model explainability techniques to improve interpretability in practical deployment Keywords: Facial Expression Recognition, EfficientNet, SVM, Deep Features, Emotion Classification
Co-Authors Adhita Putra, Bertha Pratama Ahmad Taufiq Akbar Akbar, Bagus Muhammad Andiko Putro Suryotomo Eni Farida Frans Richard Kodong Frans Richard Kodong Herry Sofyan Mohamad As'ad Nur Heri Cahyana Rifki Indra Perwira Rifki Indra Perwira Rochmat Husaini Rochmat Husaini Rustamadji, Heru Saifullah, Shoffan Sigit Setyowibowo Sujito Sujito