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All Journal International Journal of Electrical and Computer Engineering Engineering, Mathematics and Computer Science Journal (EMACS)
Setiawan, Karli Eka
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mathematics

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Journal : Engineering, Mathematics and Computer Science Journal (EMACS)

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Leveraging Support Vector Machines and Ensemble Learning for Early Diabetes Risk Assessment: A Comparative Study Shiddiqi, Hafizh Ash; Setiawan, Karli Eka; Fredyan, Renaldy
Engineering, MAthematics and Computer Science Journal (EMACS) Vol. 7 No. 1 (2025): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v7i1.12846

Abstract

Currently, diabetes is a hidden, serious threat to human lifestyles through daily food and drink, which has become a formidable global health challenge. As a contribution, this study suggests a way to use machine learning to find people with diabetes by looking at certain health parameters. It does this by using different Support Vector Machine (SVM)-based models, such as different SVMs with different kernels, such as linear, polynomial, radial basis function, and sigmoid kernels; different ensemble bagging with SVM; and different ensemble stacking with various SVM models. The findings demonstrated that utilizing a single SVM model with a linear kernel, ensemble bagging with a linear SVM, and ensemble stacking with different SVM models yielded the most accurate results, achieving 95% accuracy in both diabetes presence and absence. This lends credence to the idea that the incorporation of a linear kernel has the potential to improve the accuracy of determining whether or not diabetic illness is present.
Advancing Indonesian Audio Emotion Classification: A Comparative Study Using IndoWaveSentiment Majiid, Muhammad Rizki Nur; Setiawan, Karli Eka; Pamungkas, Prayoga Yudha; Annas, Taufiq; Setiawan, Nicholas Lorenzo
Engineering, MAthematics and Computer Science Journal (EMACS) Vol. 7 No. 2 (2025): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v7i2.13415

Abstract

This study addresses the critical gap in Indonesian Speech Emotion Recognition (SER) by evaluating machine learning models on the IndoWaveSentiment dataset, a novel corpus of 300 high-fidelity recordings capturing five emotions (neutral, happy, surprised, disgusted, disappointed) from native speakers. The research aims to identify optimal classification techniques and acoustic features for Indonesian SER, given the language’s unique linguistic characteristics and the scarcity of annotated resources. Six models, Logistic Regression, KNN, Gradient Boosting, Random Forest, Naive Bayes, and SVC, were trained on 45 acoustic features, including spectral contrast, MFCCs, and zero crossing rate, extracted using Librosa. Results demonstrated Random Forest as the top performer (90% accuracy), followed by Gradient Boosting (85%) and Logistic Regression (75%), with spectral contrast (contrast2, contrast7) and MFCC1 emerging as the most discriminative features. The findings highlight the efficacy of ensemble methods in capturing nuanced emotional cues in Indonesian speech, outperforming prior studies on locally sourced datasets. Practical implications include applications in customer service analytics and mental health tools, though limitations such as the dataset’s-controlled conditions and fixed sentence structure necessitate caution in real-world deployment. Future work should expand the dataset to include regional dialects, spontaneous speech, and hybrid architectures like CNN-LSTMs. This study establishes foundational benchmarks for Indonesian SER, advocating for culturally informed models to enhance human-computer interaction in underrepresented linguistic contexts.
Co-Authors Alam, Islam Nur Annas, Taufiq Fredyan, Renaldy Majiid, Muhammad Rizki Nur Minor, Kelvin Asclepius Pamungkas, Prayoga Yudha Setiawan, Nicholas Lorenzo Shiddiqi, Hafizh Ash