[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123406-en":3,"doc-seo-123406-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123406,13056703019662,"Evangeline","https://ap-avatar.wpscdn.com/avatar/be000253a8e92610077?_k=1778726343310543188",8,"Research & Report","Comparative Analysis of Machine Learning Models for Rainfall Classification in Yogyakarta","Precise rainfall classification is central to meteorological forecasting and disaster risk mitigation, especially in Yogyakarta where extreme weather events are a recurring threat. Prior studies have explored rainfall classification using meteorological variables, yet systematic comparisons of machine learning algorithms for this specific region remain limited. This study builds predictive models using temperature, humidity, atmospheric pressure, and precipitation with KNN, decision trees, and logistic regression. Decision trees achieve the highest accuracy on both training and testing data, and precipitation is identified as the most influential variable. The study is constrained to selected meteorological variables and does not address spatial or temporal variability, which may limit broader applicability; future work can add remote sensing and spatiotemporal modeling.","COMPARATIVE ANALYSIS OF MACHINE LEARNING MODELS FOR RAINFALL CLASSIFICATION IN  \nYOGYAKARTA  \nDina Tri Utari1*, Ghalang Rambu Putera Palage2, Faiz Fadhlirobby3,  \nArtheta Bimo Nuswantoro4  \n1,2,3,4Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Islam Indonesia  \nJln. Kaliurang Km 14.5, Sleman, Yogyakarta, 55584, Indonesia  \nCorresponding author’s e-mail: * [dina.t.utari@uii.ac.id](dina.t.utari@uii.ac.id)  \nArticle History:  \nReceived: 24th February 2025  \nRevised: 15th April 2025  \nAccepted: 19th May 2025  \nAvailable online: 1st September 2025  \nKeywords:  \nDecision Tree;  \nKNN;  \nLogistic Regression;  \nRainfall Classification; Weather Variables;  \nYogyakarta.  \nABSTRACT  \nPrecise rainfall classification is most important for meteorological forecasting and disaster risk mitigation, particularly in regions such as Yogyakarta, which are vulnerable to extreme weather events. Although previous studies have examined rainfall classification through the lens of meteorological variables, a notable lack of research has systematically evaluated the effectiveness of diverse machine learning algorithms for categorizing rainfall types within this specific locale. This study aims to rectify this gap by incorporating essential weather variables, specifically temperature, humidity, atmospheric pressure, and precipitation, into predictive models that utilize K-Nearest Neighbors (KNN), decision trees, and logistic regression techniques. Among the evaluated models, the decision tree demonstrated the highest degree of accuracy across both training and testing datasets. An examination of feature significance indicated that precipitation emerged as the most pivotal variable, aligning with the fundamental physical mechanisms associated with rainfall. This study contributes significantly to the evolving field of weather informatics by illustrating the utility of machine learning approaches in classifying regional rainfall. However, the parameters of this research are limited to specific meteorological variables and do not account for spatial or temporal variations, which could potentially influence the model ’s broader applicability. Future research endeavors could augment this framework by integrating remote sensing data and methodologies for spatiotemporal modeling.  \nThis article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-ShareAlike 4.0 International License ([https://creativecommons.org/licenses/by-sa/4.0/](https://creativecommons.org/licenses/by-sa/4.0/)) .  \nHow to cite this article:  \nD. T. Utari, G. R. P. Palage, F. Fadhlirobby, and A. B. Nuswantoro,“COMPARATIVE ANALYSIS OF MACHINE LEARNING MODELS FOR RAINFALL CLASSIFICATION IN YOGYAKARTA,” BAREKENG: J. Math. & App., vol. 19, iss. 4, pp. 2765-2776, December, 2025.  \nCopyright © 2025 Author(s)  \nJournal homepage: [https://ojs3.unpatti.ac.id/index.php/barekeng/](https://ojs3.unpatti.ac.id/index.php/barekeng/)  \nJournal e-mail: [barekeng.math@yahoo.com](barekeng.math@yahoo.com); [barekeng.journal@mail.unpatti.ac.id](barekeng.journal@mail.unpatti.ac.id)  \nResearch Article • Open Access  \n1. INTRODUCTION  \nWeather classification, an essential meteorological component, entails systematically categorizing atmospheric phenomena based on empirical evidence rather than predictive models of forthcoming conditions. Conventional classification techniques were predominantly dependent on observational heuristics, such as the interpretation of a red sunset as an indication of forthcoming clear weather, which, despite their intuitive attractiveness, were deficient in scientific rigor and methodological consistency. This circumstance prompted the development of data-centric approaches bolstered by technological innovationsand systematic observational practices [1] . Contemporary meteorological sciences utilize a comprehensive temperature, humidity, dew point, wind velocity and orientation, solar irradiance, and pre","cbCaiim2EpTVsaAe","https://ap.wps.com/l/cbCaiim2EpTVsaAe","pdf",677960,1,12,"English","en",105,"# Introduction\n## Weather classification and its importance\n## Machine learning approaches in meteorology\n## Rainfall patterns in Yogyakarta","[{\"question\":\"Which machine learning models are evaluated for rainfall classification in Yogyakarta?\",\"answer\":\"The study evaluates K-Nearest Neighbors (KNN), decision trees, and logistic regression using selected weather variables.\"},{\"question\":\"Which model performs best according to training and testing results?\",\"answer\":\"The decision tree model shows the highest accuracy across both training and testing datasets.\"},{\"question\":\"What weather variable is identified as most important for rainfall classification?\",\"answer\":\"Precipitation is identified as the most pivotal variable, consistent with the underlying physical mechanisms of rainfall.\"}]","Comparative Analysis of Machine Learning Models for Rainfall Classification in Yogyakarta | 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