Rainfall Model Using Principal Component Regression Analysis with R Software in Sulawesi

Annisa Alma Yunia, Dianne Amor Kusuma, Bambang Suhandi, Budi Nurani Ruchjana

Abstract


Indonesia is a tropical country that has two seasons, rainy and dry. Nowadays, the earth is experiencing the climate change phenomenon which causes erratic rainfall. The rainfall is influenced by several factors, one of which is the local scale factor. This research was aimed to build a rainfall model in Sulawesi to find out how the rainfall relationship with local scale factor in Sulawesi. In this research, the data used were secondary data which consisted of 15 samples with 6 variables from Badan Pusat Statistik (BPS). The limitation of the sample size in this study was due to the limited secondary data available in the field. The data was processed using Principal Component Regression Analysis. The first step was reducing local scale factor variables so that the principal component variable could be obtained that can explain variability from the original data which then that variable was analyzed using principal regression analysis. The data were analyzed by utilizing R Studio software. The results show that two principal component variables can explain 75.2% of the variability of original data and only one principal component variable that was significant to the rainfall variable. The regression model explained that the relationship between rainfall, humidity, air temperature, air pressure, and solar radiation was in the same direction while the relationship between rainfall and wind velocity was not in the same direction. Overall, the results of the study provided an overview of the application of the Principal Component Regression analysis to model the rainfall phenomenon in the Sulawesi region using the R program.


Keywords


Curah Hujan Sulawesi, Faktor Skala Lokal, Analisis Regresi Komponen Utama

Full Text:

PDF

References


Alice, M. (2016). Performing principal components regression (PCR) in R. R User Group of Milano.

Chambers, J. (2008). Software for data analysis: programming with R. Springer.

Gorgees, H. M., & Ali, B. A. (2017). Employing ridge regression procedure to remedy the multicollinearity problem. Ibn AL-Haitham Journal For Pure and Applied Science, 26(1), 320–327.

Handiana, D., Wahyono, S. C., & Susanti, D. S. (2016). Perancangan model prediksi curah hujan bulanan berdasarkan suhu permukaan laut di kalimantan selatan. jurnal fisika flux: jurnal ilmiah fisika FMIPA Universitas Lambung Mangkurat, 10(1), 1–12.

Johnson, R. A., & Wichern, D. W. (2007). Applied multivariate statistical analysis (6th Edition). Prentice Hall.

Jolliffe, I. T. (2010). Principal component analysis. springer.

Mariana. (2013). Analisis komponen utama. matematika dan pembelajaran, 1(2), 189–204.

Montgomery, D. C., Peck, E. A., & Vining, G. G. (2012). Introduction to linear regression analysis. Wiley.

Navid, M. A. I., & Niloy, N. H. (2018). Multiple linear regressions for predicting rainfall for Bangladesh. Communications, 6(1), 1–4.

Rencher, A. C. (2002). Methods of multivariate analysis (2nd Edition). John Wiley & Sons.

Sudrajat, A. (2016). Metode principal component analysis untuk mengatasi multikolinearitas pada regresi linear berganda (studi kasus faktor yang mempengaruhi indek pembangunan manusia di Jawa Timur). Jurnal Penelitian Kesehatan, 14(4).

Untari, D. P., & Susanti, M. (2017). Latent root regression dalam mengatasi multikolinearitas. Pythagoras: Jurnal Pendidikan Matematika, 12(1), 23–32.




DOI: http://dx.doi.org/10.24042/djm.v3i3.6108

Refbacks

  • There are currently no refbacks.


Copyright (c) 2020 Desimal: Jurnal Matematika

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

  Creative Commons License
Desimal: Jurnal Matematika is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.