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All Journal Jurnal Tekno Kompak Jurnal Teknik Informatika (JUTIF)
Yustianisa, Dita
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Computer Science & IT

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Hyperparameter Tuning for Optimizing Stunting Classification with KNN, SVM, and Naïve Bayes Algorithms Firgiawan, Wawan; Yustianisa, Dita; Nur, Nurrahmi Afiah; Gabrelia, Gabrelia
Jurnal Tekno Kompak Vol 19, No 1 (2025): FEBRUARI
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/jtk.v19i1.4574

Abstract

The purpose of this study is to illuminate and compare the performance of three classifiers, namely Naive Bayes (NB), Support Vector Machine (SVM), and K-Nearest Neighbors (KNN), in classifying stunting data. Using evaluation measures such as accuracy, precision, recall, and F1 score, the performance of each algorithm is measured before and after hyperparameter adjustment. The experimental results show that SVM provides a strong balance between precision and recall before hyperparameter adjustment, KNN excels in recall, and NB achieves the highest precision. After hyperparameter adjustment, all models show improved performance, with SVM achieving the best accuracy and F1 score. While NB remains highly precise and reduces false positives, KNN continues to win the recall. The results show that hyperparameter adjustment is critical to optimizing algorithm performance and that algorithms should be selected according to specific research objectives to maximize detection accuracy and balance recall and precision.
Performance Comparison Of K-Nearest Neighbors And Decision Tree Algorithms With Random Oversampling For Imbalanced Heart Disease Classification Yustianisa, Dita; Wajidi, Farid; Firgiawan, Wawan; Sholeha, Adinda Gama
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 3 (2026): JUTIF Volume 7, Number 3, June 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.3.5626

Abstract

Heart disease remains one of the leading causes of mortality worldwide, including in Indonesia, where delayed detection continues to be a serious challenge. In medical data mining, class imbalance often degrades classification performance by reducing sensitivity toward minority class cases. This study aims to compare the performance of the K-Nearest Neighbors (KNN) and Decision Tree algorithms for heart disease classification and to evaluate the effectiveness of random oversampling in handling imbalanced data. This research uses a heart disease dataset consisting of 10,000 medical records obtained from Kaggle. Data preprocessing includes categorical transformation, missing value imputation using KNN Imputer, and Min–Max normalization. Random oversampling is applied to increase minority class representation. Model evaluation is performed using stratified 10-fold cross-validation with accuracy, precision, recall, F1-score, and Receiver Operating Characteristic–Area Under the Curve (ROC–AUC) as performance metrics. Experimental results show that after random oversampling, the KNN model achieves the best performance with an accuracy of 94%, precision of 96%, recall of 90%, F1-score of 92%, and ROC–AUC of 90.2%. In comparison, the Decision Tree model records an accuracy of 80%, precision of 78%, recall of 81%, F1-score of 79%, and ROC–AUC of 81.5%. These findings demonstrate that random oversampling significantly improves minority class detection, particularly for KNN. This study contributes to Informatics by providing empirical evidence that simple and efficient data mining strategies can effectively address class imbalance in large-scale medical datasets, supporting the development of accurate, interpretable, and accessible AI-based diagnostic systems for early heart disease detection.
Co-Authors Farid Wajidi Firgiawan, Wawan Gabrelia, Gabrelia Nur, Nurrahmi Afiah Sholeha, Adinda Gama