About The Project

Rainfall data is one of the biggest factors that affect the climate of a region. Frequently missing rainfall data found in rainfall data makes analysis and decision-making difficult, so it is important to find an accurate and efficient estimation method to fill in the data so that statistical inference can be carried out. The estimation is carried out using several interpolation methods in the RStudio program, namely the na.StructTS function, and the na.approx function in the "zoo" package as well as the na_ma function, na_locf function and na.interpolation function in the "imputeTS" package. The data processed is monthly rainfall data in 2009-2020 in the city of Makassar. Some values of the data were randomly considered as missing data which were divided into four categories, namely (6.9%), (10.4%), (13.9%), and (20%). The results showed that the na.StructTS method was the best method in finding missing data with the largest correlation coefficient of 0.85 and the smallest RMSE value of 159.967 in the 20% category. Keywords: rainfall, interpolation, missing values.