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All Journal Pythagoras: Jurnal Matematika dan Pendidikan Matematika Jurnal Edukasi dan Penelitian Informatika (JEPIN) Infotech Journal Journal of Information System Jurnal Teknologi dan Sistem Komputer IJCIT (Indonesian Journal on Computer and Information Technology) JIKO (Jurnal Informatika dan Komputer) JURNAL MEDIA INFORMATIKA BUDIDARMA Indonesian Journal of Artificial Intelligence and Data Mining ILKOM Jurnal Ilmiah DoubleClick : Journal of Computer and Information Technology INTECOMS: Journal of Information Technology and Computer Science Progresif: Jurnal Ilmiah Komputer Jurnal Ilmiah Betrik : Besemah Teknologi Informasi dan Komputer Journal of Applied Engineering and Technological Science (JAETS) JATI (Jurnal Mahasiswa Teknik Informatika) Abdimasku : Jurnal Pengabdian Masyarakat Paradigma
Betha Nurina Sari
Program Studi Informatika, Universitas Singaperbangsa Karawang
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Health Professions Industrial & Manufacturing Engineering Mathematics Social Sciences

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Journal : Indonesian Journal of Artificial Intelligence and Data Mining

 1 
Random Forest Algorithm for Prediction of Precipitation Aji Primajaya; Betha Nurina Sari
Indonesian Journal of Artificial Intelligence and Data Mining Vol 1, No 1 (2018): March 2018
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (443.435 KB) | DOI: 10.24014/ijaidm.v1i1.4903

Abstract

Predicting rainfall needs to be done as one of such effort to anticipate water flooding. One of the algorithm that can be used to predict rainfall is random forest. The porpose of the research is to create a model by implementing random forest algorithm. The research method consist of four steps: data collection, data processing, random forest implementation, analysis. Random forest implementation with using training set resulted model that has accurracy 71,09%, precision 0.75, recall 0.85, f-measure 0.79, kappa statistic 0.33, MAE 0.35, RMSE 0.46, ROC Area 0.78. Implementation of random forest algorithm with 10-fold cross validation resulted the output with accurracy 99.45%, precision 0.99, recall 0.99, f-measure 0.99, kappa statistic 0.99, MAE 0,09, RMSE 0.14, ROC area 1.
Co-Authors Agil Aditya Agil Aditya Anggri, Anggri Apriade Voutama aqillah Aqillah Fadia Haya Ato Sugiharto Bayu Adhi Pamungkas Budi Arif Dermawan Delpidat, Derrenz Dinda Fitriani Fida Nafisah Giustin Hannie, Hannie Ibnu Alfitra Salam Ilham, Septian Ivan Jovian Katon Wahyudi Putra Khusaeri, Ahmad Khusaeri, Ahmad Muhamad Khandava Mulyadien Nurhasanah, Desi Nurhusaeni, Muhamad Padilah, Tesa Nur Rahmat Alfanza Ratna Mufidah Reynaldi Azhar Rini Mayasari Rini Mayasari Sani Shalihamidiq Silvana Fitria Mandang Sisca Yuliantina Sisca Yuliatina Susilawati Susilawati Syafrida Nabila Salsabila Tesa Nur Padilah Tesa Nur Padilah Tia Adha Mariam Putri Ultach Enri Wira Buana Yunita Nurohmah