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All Journal Inferensi MATHunesa: Jurnal Ilmiah Matematika
Siswanto
Hasanuddin University
Computer Science & IT Decision Sciences, Operations Research & Management Engineering Mathematics Social Sciences

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COMPARISON OF THE PERFORMANCE OF NAÏVE BAYES AND CORRELATED NAÏVE BAYES METHODS WITH THE APPLICATION OF SYNTHETIC MINORITY OVER-SAMPLING TECHNIQUE Radia Sultan; Siswanto; Andi Isna Yunita
MATHunesa: Jurnal Ilmiah Matematika Vol. 13 No. 2 (2025)
Publisher : Universitas Negeri Surabaya

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Abstract

Classification is the process of creating a model to recognize patterns with the aim of mapping them into specific classes and predicting classes. Naive Bayes is a popular, simple and effective classification method with a probabilistic approach based on Bayes' Theorem. The assumption of independence in this method sometimes makes the classification performance decrease. Correlated naïve bayes corrects this assumption by considering attribute correlations, while SMOTE is used to overcome data imbalances. This approach is important in medical data analysis, one of which is predicting ischemic heart disease. This study aims to compare the performance of Naïve Bayes and Correlated Naïve Bayes methods in the classification of ischemic heart disease, with the application of SMOTE to overcome data imbalance. The analysis was carried out using ischemic heart disease data at the Integrated Heart Center of Dr. Wahidin Sudirohusodo Hospital, Makassar City, for the period of July 2021 to July 2022. Naïve Bayes managed to classify 66 data with 75% accuracy, 94% precision, and 62% sensitivity. Meanwhile, Correlated Naïve Bayes showed better performance by correctly classifying 77 data, resulting in 87.5% accuracy, 86% precision, and 94% sensitivity. These results show that Correlated Naïve Bayes has a superior performance in classifying ischemic heart disease.
Modeling with Robust Kernel Nonparametric Regression on Childhood Stunting in Kalimantan Samsul Arifin; Selvi Annisa; Siswanto
Inferensi Vol 9 No 2 (2026)
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213862.v9i2.9914

Abstract

This study models stunting prevalence across 56 regencies/cities in Kalimantan using robust kernel nonparametric regression. This approach addresses the nonlinear relationship between stunting and four predictors: access to improved sanitation, low birth weight, population density, and poverty rate. An examination of influential observations using DFFITS identified five regencies as outliers; thus, the robust MM-estimator approach was applied to mitigate the influence of these extreme observations on the estimation results. Optimal bandwidth selection was performed using the Cross-Validation (CV) method across several kernel functions, namely Epanechnikov, Gaussian, and Uniform. The results demonstrated that the Uniform kernel function yielded the smallest CV value with a bandwidth combination of h1=0.6, h2=0.2, h3=0.6, and h4=0.2. The Robust Uniform Kernel model delivered the best performance, with an MSE of 2.0556, RMSE of 1.4337, MAE of 0.6581, and of 0.9510. This study indicates that robust MM-estimator kernel nonparametric regression can produce stunting prevalence estimates that are more accurate, flexible, and stable in the presence of outliers.
Co-Authors Andi Isna Yunita Arifin, Samsul Radia Sultan Selvi Annisa