[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127879-en":3,"doc-seo-127879-105":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},127879,2336474459895,"Aria","https://ap-avatar.wpscdn.com/avatar/22000baeef7a5ed0655?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786071322749376916",8,"Research & Report","Predictive Analysis of Malaria Cases in Indonesia Using Machine Learning - Paper","Malaria remains a major global public health challenge, and Indonesia continues to experience endemic transmission across multiple regions. Reliable prediction of malaria incidence is essential to support prevention and control, especially where resources are limited. This study applies machine learning using climatic, epidemiological, and socioeconomic variables from 2010 to 2021. Random Forest, Support Vector Machine, and Artificial Neural Networks are evaluated, with Random Forest showing the strongest accuracy. Results highlight spatiotemporal transmission patterns and the value of data quality and model refinement for surveillance and decision-making.","Syntax Literate: Jurnal Ilmiah Indonesia p–ISSN: 2541-0849  \ne-ISSN: 2548-1398  \nVol. 9, No. 9, September 2024   \nPREDICTIVE ANALYSIS OF MALARIA CASES IN INDONESIA USING MACHINE LEARNING  \nRatih Syabrina1, Gunawan Wang2  \nUniversitas Bina Nusantara, Jakarta, Indonesia 1,2  \nEmail: [ratih.syabrina@binus.ac.id](ratih.syabrina@binus.ac.id1)[1](ratih.syabrina@binus.ac.id1), [gwang@binus.edu](gwang@binus.edu2)[2](gwang@binus.edu2)  \nAbstract  \nMalaria continues to pose a significant public health challenge globally, with Indonesia being among the countries most affected by the disease. Despite extensive efforts to control malaria transmission, the disease remains endemic in various regions, leading to substantial morbidity and mortality. Accurate prediction of malaria cases is crucial for guiding effective prevention and control strategies, particularly in resource-limited settings. This study investigates the application of machine learning (ML) techniques to predict malaria incidence in Indonesia, leveraging climatic, epidemiological, and socioeconomic data. Three ML algorithms, namely Random Forest, Support Vector Machine (SVM), and Artificial Neural Networks (ANN), are employed and evaluated for their predictive capabilities. The study spans from 2010 to 2021, incorporating diverse datasets from the Indonesian Meteorological, Climatological, and Geophysical Agency (BMKG), the Ministry of Health of Indonesia, and the Indonesian Bureau of Statistics (BPS) . Results indicate that the ML models exhibit strong predictive performance, with Random Forest demonstrating the highest accuracy. The integration of multidimensional data sources enhances the robustness of the predictive models, enabling the identification of spatiotemporal patterns in malaria transmission dynamics. The findings underscore the potential of ML-based approaches in improving malaria surveillance and control efforts in Indonesia, offering valuable insights for public health decision-makers and stakeholders. Moreover, the study highlights the importance of data quality, model refinement, and interdisciplinary collaboration in addressing complex public health challenges such as malaria. By harnessing the power of advanced analytics and innovative methodologies, this research contributes to the ongoing efforts to combat malaria and alleviate its burden on communities and healthcare systems in Indonesia and beyond.  \nKeywords: Malaria prediction, machine learning, data integration, Indonesia, public health, disease surveillance  \nIntroduction  \nMalaria, a mosquito-borne infectious disease caused by Plasmodium parasites, remains a major global health concern. It spreads through the bites of infected Anopheles mosquitos, which are common in tropical and subtropical areas. Despite great success in lowering malaria frequency worldwide, the disease remains a severe public health issue, particularly in areas with ideal climatic conditions for mosquito breeding (Bi et al., 2011; Haryanto, 2009) . According to the World Health Organization (WHO), there were an estimated 229 million malaria cases worldwide in 2019, with roughly 409,000 fatalities, mostly in Sub-Saharan Africa. However, Southeast Asia, especially Indonesia, bears a significant burden of the disease.  \n\n| How to cite: | Syabrina, R., & Wang, G. (2024) . Predictive Analysis of Malaria Cases in Indonesia Using Machine Learning. Syntax Literate. (9)9. [http://dx.doi.org/10.36418/syntax-literate.v9i9](http://dx.doi.org/10.36418/syntax-literate.v9i9) |\n| --- | --- |\n| E-ISSN: | 2548-1398 |\n\nRatih Syabrina, Gunawan Wang  \nIn Indonesia, malaria continues to be endemic in several regions, contributing to a considerable burden on healthcare systems and socioeconomic development. The diverse geography of Indonesia, consisting of numerous islands with varied climatic conditions, complicates malaria control efforts. Certain locations, such as Papua and East Nusa Tenggara, have greater malaria transmission rates due to optim","cbCaigvQ4bdkrGQC","https://ap.wps.com/l/cbCaigvQ4bdkrGQC","pdf",695213,1,11,"English","en",105,"# Introduction\n## Malaria as a Public Health Concern\n## Need for Predictive and Timely Surveillance\n## Role of Machine Learning in Disease Forecasting","[{\"question\":\"Why is predicting malaria cases important in Indonesia?\",\"answer\":\"Accurate prediction helps guide prevention and control strategies, since malaria remains endemic and burdens healthcare and socioeconomic development across regions.\"},{\"question\":\"Which machine learning algorithms are used in the study?\",\"answer\":\"The study evaluates Random Forest, Support Vector Machine (SVM), and Artificial Neural Networks (ANN) to predict malaria incidence.\"},{\"question\":\"What data sources and time period does the research use?\",\"answer\":\"The research uses data from 2010 to 2021, integrating climatic, epidemiological, and socioeconomic information from BMKG, the Indonesian Ministry of Health, and BPS.\"}]","Predictive Analysis of Malaria Cases in Indonesia Using Machine Learning - 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