Random Forest Algorithm for Prediction of Precipitation
DOI:
https://doi.org/10.24014/ijaidm.v1i1.4903Abstract
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.References
S. P. Nugroho. “Analisis Curah Hujan Penyebab Banjir Besar di Jakarta Pada Awal Februari 2007”. JAI. 2008; 4(1): 50-55.
A. Novandya, I. Oktria. “Penerapan Algoritma Klasifikasi Datamining C4.5 Pada Dataset Cuaca Wilayah Bekasi”. Jurnal Format. 2017; 6(2): 98-106.
A. Khusaeri, S. Ilham, D. Nurhasanah, D. Delpidat, Anggri, A. Primajaya, B. N. Sari. “Algoritma C4.5 untuk Pemodelan Daerah Rawan Banjir Studi Kasus Daerah Karawang Jawa Barat”. ILKOM Jurnal Ilmiah. 2017; 9(2): 132-136
Y. Wang, S. Shia, Q. Tang, J. Wu, X. Zhu. “A Novel Consistent Random Forest Framework: Bernoulli Random Forest”. IEEE Transaction On Neural Network and Learning Systems. 2017; 1-14
M. Dhawangkhara, E. Riksakomara. “Prediksi Intensitas Hujan Kota Surabaya dengan Matlab Menggunakan Teknik Random Forest dan CART (Studi Kasus Kota Surabaya)”. Jurnal Teknik ITS. 2017; 6(1): 94-99
L. Breiman. Random Forests. Machine Learning. 2001; 4(1): 5-32
A. Raditya. “Implementasi Datamining Classification untuk Mencari pola Prediksi Hujan dengan Menggunakan Algoritma C4.5”. Dalam: Publikasi Jurusan Teknik Informatika, Fakultas Teknologi Industri, Universitas Gunadarma. 2012.
V. Y. Kulkarni, P. K. Sinha. “Effective Learning and Classification Using Random Forest Algorithm”. International Journal of Engineering and Innovative Technology (IJEIT). 2014; 3(11): 267-273
M. G. Sadewo, A. P. Windarto, D. Hartama D. “Penerapan Datamining Pada Populasi Daging Ayam RAS Pedaging di Indonesia Berdasarkan Provinsi Menggunakan K-Means Clustering”. InfoTekJar (Jurnal Nasional Informatika dan Teknik Jaraingan). 2017; 2(1): 60-67
C. Ferri, J. Hernández-Orallo, R. Modroiu. “An Experimental Comparison of Performance Measures for Classification”. Pattern Recognitipns Letters. 2009; 30(1): 27-38
S. Dewi. “Komparasi 5 Metode Algoritma Klasifikasi Datamining Pada Prediksi Keberhasilan Pemasaran Produk Layanan Perbankan”. Jurnal Techno Nusa Mandiri. 2016; 13(1): 60-65.
