Experimental Modeling of Face Emotion Recognition Using Machine Learning Classification (SVM, KNN, Random Forest) and Deep Learning CNN
Facial Emotion Recognition (FER) is a technology that analyzes facial expressions to detect emotions, playing a growing role in psychology and Human-Computer Interaction. In Indonesia, mental health issues are rising, with emotional disorders increasing from 6.0% in 2013 to 9.8% in 2018. Over 19 mi...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Center for Research and Community Service, Institut Informatika Indonesia Surabaya
2025-07-01
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| Series: | Teknika |
| Subjects: | |
| Online Access: | https://ejournal.ikado.ac.id/index.php/teknika/article/view/1232 |
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| Summary: | Facial Emotion Recognition (FER) is a technology that analyzes facial expressions to detect emotions, playing a growing role in psychology and Human-Computer Interaction. In Indonesia, mental health issues are rising, with emotional disorders increasing from 6.0% in 2013 to 9.8% in 2018. Over 19 million people aged 15+ were affected in 2018, a number likely worsened by the COVID-19 pandemic. Given the urgency of early detection, FER offers a non-invasive method to help identify mental health issues. It can support timely intervention and promote psychological well-being, especially in under-resourced settings. This study compares several Machine Learning (ML) and Deep Learning (DL) models—SVM, K-Nearest Neighbor, Random Forest, and Convolutional Neural Networks (CNN)—to classify facial emotions. The dataset used is the Facial Expression Recognition dataset by Jonathan Oheix from Kaggle. Images were preprocessed and used to train and evaluate each model. Traditional ML models relied on extracted features, while CNN learned features directly from images. Results show that CNN achieved the highest accuracy among the tested models. This suggests that FER, especially with CNN, can be a useful tool for early detection of emotional disorders in mental health contexts.
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| ISSN: | 2549-8037 2549-8045 |