[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124762-en":3,"doc-seo-124762-105":29,"detail-sidebar-cat-0-en-105":90},{"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":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},124762,1649267921044,"Ava Thompson","https://us-avatar.wpscdn.com/avatar/1800007509477c92dfb?_k=1782875107921204101",8,"Research & Report","Identification of Rainfall events on Climate Phenomena in Medan based on Machine Learning","Indonesia’s complex topography produces distinctive regional climate behavior, making rainfall a key indicator of local patterns. Rainfall events in Medan are influenced by climate phenomena including El Niño Southern Oscillation (ENSO), Indian Ocean Dipole (IOD), and Madden Julian Oscillation (MJO). Using machine learning with Matlab, four model combinations are built (IOD+SOI+MJO, IOD+SOI, SOI+MJO, and MJO+IOD). Model outputs are compared to observed rainfall via MAE and correlation, and the SOI+MJO combination yields the best verification results.","Identification of Rainfall events on Climate Phenomena in Medan based on  \nMachine Learning  \n1,2Deassy Eirene Diana Doloksaribu, 1,*Kerista Tarigan, 3Richard Mahendra Putra,  \n4Yahya Darmawan  \n1Faculty of Mathematics and Natural Sciences, University of Sumatera Utara, Medan,  \nIndonesia.  \n2Meteorological Station of Kualanamu, Deli Serdang, Indonesia 3Meteorological, Climatological, and Geophysical Agency (BMKG), Indonesia 4 State College of Meteorology Climatology and Geophysics (STMKG), Jakarta, Indonesia  \n*[Corresponding Author e-mail:](Corresponding Author e-mail: kerista@usu.ac.id)[ ](Corresponding Author e-mail: kerista@usu.ac.id)[kerista@usu.ac.id](Corresponding Author e-mail: kerista@usu.ac.id)  \nReceived: March 2022; Revised: March 2023; Published: April 2023  \nAbstract  \nIndonesia has diverse topographical conditions that result in Indonesia having a unique climate. One of the unique climate elements to be studied is rainfall, because rainfall has a different pattern in each region, this different rainfall pattern is caused by several climate phenomena factors that affect the rainfall pattern, including El-Nino Southern Oscillation (ENSO), Indian Ocean Dipole (IOD) and Madden Julian Oscillation (MJO) . Medan City is the capital of North Sumatra province which is one of the areas in the flood-prone category in North Sumatra, where the factor of flooding is due to rainfall events in a long period of time, so the author wants to know which climatic phenomena factors can affect rainfall events in Medan city by using Machine Learning technology through the Matlab application, where in this study has a method by forming four combination models, namely the combination of the influence of IOD, SOI and MJO; second combination of IOD and SOI; third combination of SOI and MJO; and fourth combination of MJO and IOD, these four combinations will bethe rainfall value of the four models. Furthermore, the rainfall value of the model is compared with the observed rainfall value and verification test using Mean Absolute Error (MAE) and correlation. Then the calculation of the comparison between the four rainfall models with the observed rainfall obtained the lowest MAE value during the SOI and MJO phenomenon of 15.0 mm and the highest correlation value during the IOD and SOI and SOI and MJO phenomena. So it is concluded that the combination of SOI and MJO has the best verification value. This shows that based on Machine Learning modeling, the model shown as the best predictor in Medan city is when the model combination consists of SOI and MJO.  \nKeywords: Rainfall Events, Climate Phenomena, Machine Learning  \nHow to Cite: Doloksaribu, D. , Tarigan, K., Putra, R., & Darmawan, Y. (2023) . Identification of Rainfall events on Climate Phenomena in Medan based on Machine Learning. Prisma Sains : Jurnal Pengkajian Ilmu dan Pembelajaran Matematika dan IPA IKIP Mataram, 11(2), 385-392. doi:[https://doi.org/10.33394/j](https://doi.org/10.33394/j)ps.v11i2.7738  \n[https://doi.org/10.33394/j-ps.v11i2.7738](https://doi.org/10.33394/j-ps.v11i2.7738)  \nCopyright© 2023, Doloksaribu et al. This is an open-access article under the CC-BY License.  \nINTRODUCTION  \nIndonesia has a variety of topographical conditions because Indonesia is located between the Asian and Australian continents, the Pacific Ocean and the Indian Ocean and is surrounded by vast oceans. This diverse topography makes the climate in Indonesia unique to study, where one of the elements of climate is Rainfall. Rainfall is the height of rainwater collected in rain gauges on a flat place, not absorbing, not seeping and not flowing. This element of rainfall is interesting to study because rainfall patterns in the Indonesian region are not all the same, but we can see the factors of global phenomena that can affect rainfall  \npatterns in the Indonesian region including El-Nino Southern Oscillation (ENSO), Indian Ocean Dipole (IOD) and Madden Julian Oscillation (MJO) .  \nENSO is a form o","cbCaitg3Mph5i3kp","https://ap.wps.com/l/cbCaitg3Mph5i3kp","pdf",889567,1,"English","en",105,"# Introduction\n# Climate Phenomena and Their Relationship with Rainfall\n# Machine Learning Method and Model Combinations\n## Verification Metrics (MAE and Correlation)\n# Results and Conclusion","[{\"question\":\"Which climate phenomena factors are analyzed to explain rainfall events in Medan?\",\"answer\":\"The study focuses on climate phenomena including El Niño Southern Oscillation (ENSO), Indian Ocean Dipole (IOD), and Madden Julian Oscillation (MJO), along with indices represented in the modeled combinations.\"},{\"question\":\"How many rainfall prediction model combinations are constructed in the research?\",\"answer\":\"Four combinations are formed to model rainfall values: IOD+SOI+MJO, IOD+SOI, SOI+MJO, and MJO+IOD.\"},{\"question\":\"Which model combination shows the best verification performance?\",\"answer\":\"The SOI and MJO combination provides the best verification value based on the comparison against observed rainfall using MAE and correlation.\"}]","Identification of Rainfall events on Climate Phenomena in Medan based on Machine Learning | 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climate phenomena factors are analyzed to explain rainfall events in Medan?","Question",{"text":74,"@type":75},"The study focuses on climate phenomena including El Niño Southern Oscillation (ENSO), Indian Ocean Dipole (IOD), and Madden Julian Oscillation (MJO), along with indices represented in the modeled combinations.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"How many rainfall prediction model combinations are constructed in the research?",{"text":79,"@type":75},"Four combinations are formed to model rainfall values: IOD+SOI+MJO, IOD+SOI, SOI+MJO, and MJO+IOD.",{"name":81,"@type":72,"acceptedAnswer":82},"Which model combination shows the best verification performance?",{"text":83,"@type":75},"The SOI and MJO combination provides the best verification value based on the comparison against observed rainfall using MAE and 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