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All Journal JOIV : International Journal on Informatics Visualization
Saringat, Zainuri
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Computer Science & IT

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Music Recommendation Based on Facial Expression Using Deep Learning Kurniawan, -; Kurniawan, Tri Basuki; Dewi, Deshinta Arrova; Zakaria, Mohd Zaki; Saringat, Zainuri; Firosha, Ardian
JOIV : International Journal on Informatics Visualization Vol 9, No 1 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.1.3794

Abstract

Music's profound impact on human emotions is essential for creating personalized experiences in entertainment and therapeutic settings. This study introduces a cutting-edge music recommendation system that utilizes facial expression analysis to tailor music suggestions according to the user's emotional state. Our approach integrates a haar-cascade classifier for real-time face detection with a Convolutional Neural Network (CNN) that classifies emotions into seven distinct categories: happiness, sadness, anger, fear, disgust, surprise, and neutrality. This emotionally aware system recommends music tracks corresponding to the user's current emotional condition to enhance mood regulation and overall listener satisfaction. The effectiveness of our system was evaluated through rigorous testing, where the CNN model demonstrated a high degree of accuracy. Notably, the model achieved an overall accuracy of 84.44% in recognizing facial expressions. Precision, recall, and F1 scores consistently exceeded 84%, indicating robust performance across diverse emotional states. These results underscore the system's capability to accurately interpret and respond to complex emotional cues through tailored music suggestions. Integrating advanced deep learning techniques for face and emotion recognition enables our recommendation system to adapt dynamically to the user's emotional fluctuations. This responsiveness ensures a highly personalized music listening experience that reflects the user's feelings and potentially enhances their emotional well-being. By bridging the gap between static user profiles and the dynamic nature of human emotions, our system sets a new standard for personalized technology in music recommendation, promising significant improvements in user engagement and satisfaction.
The Effects of Imbalanced Datasets on Machine Learning Algorithms in Predicting Student Performance Sujon, Khaled Mahmud; Hassan, Rohayanti; Khairudin, Alif Ridzuan; Moi, Sim Hiew; Mohd Shafie, Muhammad Luqman; Saringat, Zainuri; Erianda, Aldo
JOIV : International Journal on Informatics Visualization Vol 8, No 3-2 (2024): IT for Global Goals: Building a Sustainable Tomorrow
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.3-2.2449

Abstract

Predictive analytics technologies are becoming increasingly popular in higher education institutions. Students' grades are one of the most critical performance indicators educators can use to predict their academic achievement. Academics have developed numerous techniques and machine-learning approaches for predicting student grades over the last several decades. Although much work has been done, a practical model is still lacking, mainly when dealing with imbalanced datasets. This study examines the impact of imbalanced datasets on machine learning models' accuracy and reliability in predicting student performance. This study compares the performance of two popular machine learning algorithms, Logistic Regression and Random Forest, in predicting student grades. Secondly, the study examines the impact of imbalanced datasets on these algorithms' performance metrics and generalization capabilities. Results indicate that the Random Forest (RF) algorithm, with an accuracy of 98%, outperforms Logistic Regression (LR), which achieved 91% accuracy. Furthermore, the performance of both models is significantly impacted by imbalanced datasets. In particular, LR struggles to accurately predict minor classes, while RF also faces difficulties, though to a lesser extent. Addressing class imbalance is crucial, notably affecting model bias and prediction accuracy. This is especially important for higher education institutes aiming to enhance the accuracy of student grade predictions, emphasizing the need for balanced datasets to achieve robust predictive models.
Co-Authors - Kurniawan, - Aldo Erianda, Aldo Dewi, Deshinta Arrova Firosha, Ardian Khairudin, Alif Ridzuan Kurniawan, Tri Basuki Mohd Shafie, Muhammad Luqman Moi, Sim Hiew Rohayanti Hassan, Rohayanti Sujon, Khaled Mahmud Zakaria, Mohd Zaki