PERFORMANCE ANALYSIS OF THE IMBALANCED DATA METHOD ON INCREASING THE CLASSIFICATION ACCURACY OF THE MACHINE LEARNING HYBRID METHOD - Paper metadata

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This study analyzes the performance of hybrid machine learning methods to improve classification accuracy on imbalanced data using Dengue Hemorrhagic Fever case data from 2017 to 2021 in Bandung City. Attributes include demographic, education, and climate variables such as Total Population, rainfall, average temperature, and humidity. Five learning models are combined, while Voting and Stacking are used as hybrid strategies. Random Oversampling and ADASYN are applied to address imbalance, yielding up to 97.22% accuracy.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages12

Reading Time30 min

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INTRODUCTION

Classification and imbalanced data

Handling imbalance with oversampling

JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Journal homepage: [https://jurnal.stkippgritulungagung.ac.id/index.php/jipi](https://jurnal.stkippgritulungagung.ac.id/index.php/jipi) ISSN: 2540-8984 Vol. 8, No. 1, Maret 2023, Pp. 115-126 PERFORMANCE ANALYSIS OF THE IMBALANCED DATA METHOD ON INCREASING THE CLASSIFICATION ACCURACY OF THE MACHINE LEARNING HYBRID METHOD Azmi Aulia Rahman*1), Sri Suryani Prasetiyowati2), Yuliant Sibaroni3) 1. Telkom University, Bandung, Indonesia 2. Telkom University, Bandung, Indonesia 3. Telkom University, Bandung, Indonesia Article Info Keywords: Classification, Machine Learning, Hybrid Methods, Random Oversampling, Adasyn. Article history: Received 4 November 2022 Revised 18 November 2022 Accepted 25 November 2022 Available online 1 March 2023 DOI : [https://doi.org/10.29100/jipi.v8i1.3286](https://doi.org/10.29100/jipi.v8i1.3286) * Corresponding author. Corresponding Author E-mail address: [azmiar@student.telkomuniversity.ac.id](azmiar@student.telkomuniversity.ac.id) ABSTRACT This study analyzes the performance of hybrid methods in improving accuracy on imbalanced data using Dengue Hemorrhagic Fever Case Data from 2017 to 2021 in Bandung City. The attributes used in this study consist of Total Population, Total Male, Elementary School Graduation, Junior High School Graduation, High School Graduation, College Graduation, Rainfall, Average Temperature, Humidity, Male Cases, Number of Cases, and Class. This research combines five Machine Learning methods, such as Decision Tree, Support Vector Machine, Artificial Neural Network, KNearest Neighbor, and Naïve Bayes. Hybrid Methods used in this research are Voting and Stacking methods. The oversampling methods used to handle imbalanced data in this study are Random Oversampling and Adasyn. The results show that Voting and Stacking without Random Oversampling and Adasyn get the same accuracy of 88,88% . While using Random Oversampling, voting gets an accuracy of 95,37% and stacking gets an accuracy of 96,29% . While using Adasyn, voting gets an accuracy of 94,44% and stacking gets an accuracy of 97,22% . Based on the results obtained, it can be concluded that the Random Oversampling and Adasyn Method can improve the performance of the Machine Learning hybrid method on imbalanced data. The contribution of this research is to provide information on the study and analysis of the implementation of the Random Oversampling and Adasyn methods in improving the performance of the Voting and Stacking methods in hybrid classification. I. INTRODUCTION CLASSIFICATION is a technique in machine learning using supervised learning algorithms that can predict the class of a new sample from a model inferred from training data, this classification classifies samples or examples of data into predetermined labels into a given set of data [1] . In this study, the authors used data on Dengue Hemorrhagic Fever (DHF) cases in Bandung City. Dengue Hemorrhagic Fever (DHF) is a disease caused by the dengue virus which is transmitted by mosquitoes of the Aedes Aegypti and Aedes Albopictus species [2] . Based on data from the Bandung City Health Office, dengue fever cases range from 2010 to 2018 the highest number of cases in 2013 was 5,736 cases while in 2014 it fell to 3,132, but again rose in 2015 to 3,640 cases and rose again in 2016 with a total of 3,880 cases, then decreased in 2017 by having a total of 1,786 cases and increased again in 2018 with a total of 2,826 cases [3] . In 2019 the number of cases reached 4,424 cases, compared to 2020 the number of Dengue Hemorrhagic Fever (DHF) cases in Bandung City decreased, namely in 2020 the number of cases was 2,790 cases. However, in 2021 there was an increase in the number of cases 2021 totaling 3,743 cases. In machine learning, unbalanced data is an important problem because unbalanced data can affect the model in the classification process. Unbalanced data occurs bec

FAQs

What hybrid learning methods are evaluated in this study?

The study evaluates Voting and Stacking as hybrid methods to improve classification accuracy on imbalanced data.

How does the research handle imbalanced data?

It applies oversampling techniques, specifically Random Oversampling and ADASYN, to balance class distribution before hybrid classification.

What dataset and time range are used for performance analysis?

The experiments use Dengue Hemorrhagic Fever case data from 2017 to 2021 for Bandung City.

Aug 5, 2026

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