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All Journal JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal Kecerdasan Buatan dan Teknologi Informasi
Bhuana, Chindu Lintang
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Computer Science & IT

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Perancangan Sistem Deteksi Emosi Mahasiswa Pada Jam Perkuliahan Menggunakan Metode Yolo Helmiyah, Siti; Bhuana, Chindu Lintang
JATISI Vol 12 No 1 (2025): JATISI (Jurnal Teknik Informatika dan Sistem Informasi)
Publisher : Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/jatisi.v12i1.10195

Abstract

This study aims to detect students' emotions during lecture hours using the YOLO (You Only Look Once) method. Emotions influence learning success, where positive emotions can enhance motivation and understanding, while negative emotions can hinder the learning process. This research employs an artificial intelligence-based video analysis approach to recognize students' facial expressions in real-time. The research stages include data acquisition using lecture videos, data preprocessing through annotation and labeling with bounding boxes, and the implementation of the YOLO method to detect three emotion categories: Enthusiastic, Confused, and Bored. Evaluation was conducted using precision, recall, and mean average precision (mAP) metrics. The test results showed that the model achieved an overall accuracy of 91.7%, with the best performance in the Enthusiastic category (97.0% accuracy) and good performance in the Bored category (93.4%). However, the model failed to detect the Confused emotion (0.0% accuracy), indicating the need for additional training data. This study demonstrates that the YOLO method has the potential to assist lecturers in understanding students' emotional states, enabling more adaptive teaching. Further development is needed to improve accuracy across all emotion categories and ensure the system functions optimally.
COMPARATIVE STUDY OF CLASSIFICATION MODELS IN PROCESSING STUDENT TEST SCORES DATASETS Pramestiawan, Rico; Verdian, Arry; Bhuana, Chindu Lintang; Susanto, Lilik Joko
Jurnal Kecerdasan Buatan dan Teknologi Informasi Vol. 5 No. 2 (2026): May 2026
Publisher : Ninety Media Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69916/jkbti.v5i2.475

Abstract

The development of Machine Learning (ML) has contributed significantly to the field of education, particularly in analyzing student academic data to support data-driven decision-making. Predicting student exam results is important for identifying academic performance patterns, detecting potential failures, and improving learning interventions. However, variations in student characteristics and dataset complexity require the selection of appropriate classification models to achieve optimal prediction performance. This study aims to compare the effectiveness of several ML classification models in predicting student exam results using a student academic dataset. The dataset consists of 306 records, seven attributes, and five grade classes (A, B, C, D, and E), including attendance, quiz scores, midterm examination scores, final examination scores, and assignment scores. Data preprocessing was conducted to handle missing values, duplication, inconsistencies, and outliers. The dataset was split into training and testing data with a ratio of 75:25 and evaluated using 10-fold cross-validation. Several classification models were applied, including k-Nearest Neighbour (kNN), Decision Tree, Naive Bayes, Support Vector Machine (SVM), and Random Forest. Model performance was evaluated using accuracy, precision, recall, and F1-score metrics. The experimental results showed that Random Forest achieved the best performance with an accuracy of 73.9%, precision of 74.0%, recall of 73.9%, and F1-score of 73.9%, followed by Naive Bayes and Decision Tree. Meanwhile, SVM produced the lowest performance among the tested models. The findings indicate that Random Forest is the most effective method for predicting student exam results and has strong potential to support educational decision-making systems.
COMPARATIVE ANALYSIS OF PERFORMANCE OF MACHINE LEARNING FEATURE SELECTION (GINI DECREASE AND RELIEF-F) IN HEART DISEASE DATASET Bhuana, Chindu Lintang; Pramestiawan, Rico; Susanto, Lilik Joko; Verdian, Arry
Jurnal Kecerdasan Buatan dan Teknologi Informasi Vol. 5 No. 2 (2026): May 2026
Publisher : Ninety Media Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69916/jkbti.v5i2.477

Abstract

Heart disease remains one of the leading causes of mortality worldwide and presents a major challenge in healthcare systems. Early detection plays an essential role in improving survival rates and minimizing complications through timely intervention. Recent advances in Machine Learning (ML) have provided new opportunities for developing accurate and efficient prediction systems for heart disease detection. However, one of the major challenges in ML-based prediction is identifying the most relevant features to improve classification performance while reducing computational complexity and noise. This study aims to evaluate the effectiveness of two feature selection techniques, namely Gini Decrease (GD) and ReliefF, combined with several ML models, including Support Vector Machine (SVM), Tree, Naïve Bayes, and Random Forest, for heart disease classification. The study employed the UCI Heart Disease Dataset consisting of 303 records and 14 attributes. Data preprocessing included handling missing values using mean imputation, followed by feature selection and classification using 10-fold cross-validation with an 80:20 training-testing ratio. Experimental results showed that ReliefF outperformed GD, achieving the highest average accuracy of 0.796, compared to GD with 0.767 and all features with 0.771. The SVM model achieved the highest accuracy using GD (0.833), while Random Forest demonstrated optimal performance with ReliefF (0.817). Furthermore, the Tree model exhibited the fastest computational time among all evaluated models. These findings indicate that integrating suitable feature selection methods with ML models significantly enhances heart disease classification performance, particularly in improving predictive accuracy and computational efficiency for early medical diagnosis applications.
Co-Authors Helmiyah, Siti Pramestiawan, Rico Susanto, Lilik Joko Verdian, Arry