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This study addresses a gap in systematically comparing machine learning algorithms for rainfall-type categorization in the region. Temperature, humidity, atmospheric pressure, and precipitation are used to train and evaluate K-Nearest Neighbors, decision trees, and logistic regression models. The decision tree achieves the highest accuracy on both training and testing data, and feature analysis identifies precipitation as the most influential variable. Limitations include reliance on a restricted set of meteorological variables without spatial or temporal variation; future work can integrate remote sensing and spatiotemporal modeling.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/comparative-analysis-of-machine-learning-models-for-rainfall-classification-in-yogyakarta-comparative-study/291222/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/comparative-analysis-of-machine-learning-models-for-rainfall-classification-in-yogyakarta-comparative-study/291222.png","ImageObject",300,407,{"name":92,"@type":93},"Ophelia","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-02","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",13,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What machine learning models are compared in this study for rainfall classification?","Question",{"text":113,"@type":114},"The study compares K-Nearest Neighbors (KNN), decision trees, and logistic regression, using selected weather variables as inputs.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"Which weather variable is found to be the most important for predicting rainfall types?",{"text":118,"@type":114},"Feature significance analysis shows precipitation is the most pivotal variable in the models.",{"name":120,"@type":111,"acceptedAnswer":121},"What limitation is noted regarding the model applicability beyond the study setup?",{"text":122,"@type":114},"The research is limited to specific meteorological variables and does not account for spatial or temporal variations, which may restrict broader applicability.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},291222,1789643790,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":140,"language":141,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":142,"faqs":143,"seo_title":144,"seo_description":67,"update_tm":130,"read_time":41,"preview_page_count":4},7971461741311,"https://ap-avatar.wpscdn.com/avatar/74000253aff267980c6?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779345379180704826","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.  \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 precipitation ","cbCaihx6WQ5iKn8S","https://ap.wps.com/l/cbCaihx6WQ5iKn8S","pdf",685375,12,"English","# INTRODUCTION\n## Background and motivation\n## Related work in machine learning for meteorology\n## Rainfall context in Yogyakarta","[{\"question\":\"What machine learning models are compared in this study for rainfall classification?\",\"answer\":\"The study compares K-Nearest Neighbors (KNN), decision trees, and logistic regression, using selected weather variables as inputs.\"},{\"question\":\"Which weather variable is found to be the most important for predicting rainfall types?\",\"answer\":\"Feature significance analysis shows precipitation is the most pivotal variable in the models.\"},{\"question\":\"What limitation is noted regarding the model applicability beyond the study setup?\",\"answer\":\"The research is limited to specific meteorological variables and does not account for spatial or temporal variations, which may restrict broader applicability.\"}]","COMPARATIVE ANALYSIS OF MACHINE LEARNING MODELS FOR RAINFALL CLASSIFICATION IN YOGYAKARTA - Comparative study | PDF"]