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All Journal ComTech: Computer, Mathematics and Engineering Applications International Journal of Computer Science and Humanitarian AI
Harvianto Harvianto, Harvianto
Bina Nusantara University
Humanities Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Mathematics

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Analysis And Voice Recognition In Indonesian Language Using MFCC And SVM Method Harvianto, Harvianto; Ashianti, Livia; Jupiter, Jupiter; Junaedi, Suhandi
ComTech: Computer, Mathematics and Engineering Applications Vol 7, No 2 (2016): ComTech
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/comtech.v7i2.2252

Abstract

Voice recognition technology is one of biometric technology. Sound is a unique part of the human being which made an individual can be easily distinguished one from another. Voice can also provide information such as gender, emotion, and identity of the speaker. This research will record human voices that pronounce digits between 0 and 9 with and without noise. Features of this sound recording will be extracted using Mel Frequency Cepstral Coefficient (MFCC). Mean, standard deviation, max, min, and the combination of them will be used to construct the feature vectors. This feature vectors then will be classified using Support Vector Machine (SVM). There will be two classification models. The first one is based on the speaker and the other one based on the digits pronounced. The classification model then will be validated by performing 10-fold cross-validation.The best average accuracy from two classification model is 91.83%. This result achieved using Mean + Standard deviation + Min + Max as features.
Comparative Analysis of Decision Tree, Random Forest, and XGBoost for Student Category Prediction Lim, Rayson Calvianto; Harvianto, Harvianto
International Journal of Computer Science and Humanitarian AI Vol. 3 No. 1 (2026): IJCSHAI (In Press)
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/ijcshai.v3i1.14936

Abstract

This research aims to develop and evaluate a lightweight machine learning framework for predicting student performance categories as a foundation for personalized curriculum design in a mid-sized school context. The study compares three baseline algorithms such as Decision Tree, Random Forest, and XGBoost implemented using an end-to-end workflow involving data preprocessing, feature engineering, model training, and evaluation. A dataset of anonymized student academic and behavioral attributes was prepared through cleaning, encoding, normalization, and stratified splitting to ensure consistency and reliability. Each model was assessed using accuracy, precision, recall, and F1-score to determine its predictive effectiveness. The experimental results show that the Random Forest model achieved the highest overall performance, demonstrating stronger generalization compared to Decision Tree and XGBoost. Medium-performing students were classified most reliably, while Low-performing students displayed greater variability, indicating the need for more comprehensive data to improve sensitivity toward at-risk learners. The originality of this study lies in its focus on implementing an accessible, resource-efficient predictive pipeline suitable for schools with limited technological capacity. The findings provide evidence that practical machine learning approaches can support early stages of data-driven curriculum planning and help educators make more informed instructional decisions. The study also highlights opportunities for future work, including the expansion of data sources and adoption of more advanced algorithms to enhance predictive accuracy and support broader educational applications.
Co-Authors Jupiter, Jupiter Lim, Rayson Calvianto Livia Ashianti, Livia Suhandi Junaedi