%0 Thesis
%9 Skripsi
%A Azizan, Muhammad
%A Harsani, Prihastuti
%A Herdianto Situmorang, Boldson
%A Universitas Pakuan,
%A Fakultas Matematika dan Ilmu Pengetahuan Alam,
%A Program Studi Ilmu Komputer,
%B Fakultas Matematika dan Ilmu Pengetahuan Alam
%D 2024
%F eprintsunpak:8643
%I Universitas Pakuan
%P 56
%T RAINFALL PREDICTION MODEL USING   MACHINE LEARNING ALGORITHM
%U http://eprints.unpak.ac.id/8643/
%X RAINFALL PREDICTION MODEL USING   MACHINE LEARNING ALGORITHM  Muhammad Azizan1  , Prihastuti Harsani2  , Boldson H.Situmorang3  1,2,3 Department of Computer Science, Faculty of Mathematics and Natural Science, Pakuan  University, Bogor, West Java, 16143, Indonesia  Abstract  Indonesia is a country where most regions experience two seasons: the dry season and the rainy   season. The significant difference between these seasons lies in the average monthly rainfall. Rainfall is   a key indicator in measuring weather conditions, as the amount of measured rainfall can indicate the   intensity of rain in a specific area. Rainfall is measured in millimeters (mm) at monthly intervals. Rainfall   has a major impact on human life, both in excess and in shortage. Excessive rainfall, such as heavy or   extreme rain, often leads to flooding, especially in the city of Bogor, which is known as a flood and   landslide-prone area due to its high rainfall intensity. Therefore, an effective rainfall prediction method   is needed to help reduce the negative impacts of extreme weather. In this study, a rainfall prediction model   was developed for the Bogor area using the Multilayer Perceptron algorithm, with 90% of the data for   training and 10% for testing. The results show that the model can predict rainfall accurately, with an   RMSE value of 17.8763, indicating a good level of accuracy. RMSE is used as the main indicator in   evaluating model performance, where the lower the value, the more accurate the prediction. This MLP�based rainfall prediction model has great potential for application in various sectors, including disaster   mitigation. With high accuracy, this model can support decision-making in areas vulnerable to floods and   droughts, thus minimizing the negative impacts of weather variability.  Keywords: Prediction; Rainfall; Multilayer Perceptron  1. Introduction  Indonesia is a country where most of its regions experience two seasons in a year: the dry season and   the rainy season. The main difference between these two seasons is the average monthly rainfall. Rainfall   refers to the height of water collected in a rain gauge placed on a flat surface that does not absorb, drain, or   evaporate [1]. Rainfall plays a significant role in human life on this planet. Both high and low rainfall levels   greatly impact the climate conditions on Earth’s surface. Excessive rainfall can cause various problems,   such as floods, crop failures, and other events. Heavy rain with high intensity, commonly referred to as   extreme rain, can trigger floods. Bogor, one of the cities in Indonesia, often falls victim to flooding due to   high rainfall, and this sometimes leads to landslides and building damage, causing significant losses to the   local population. Therefore, a specific method is required to obtain optimal rainfall predictions [2].   Artificial Neural Networks, specifically the Multilayer Perceptron (ANN-MLP) model, can be used to  predict weather changes more accurately, enabling the application of this forecasting system in daily life   [3]. The variables used in the MLP method include rainfall, average temperature, average humidity, and   sunshine duration. The advantage of using the MLP method lies in its ability to determine better weight   values compared to other methods [4]. In this study, the selection of the Multilayer Perceptron method is   expected to improve rainfall prediction accuracy by modeling complex relationships between variables in   the context of weather data. The goal of this research is to predict rainfall in Bogor in 2023 using data   downloaded from the BMKG website. The information generated from this research is expected to serve as   a basis for the Meteorology, Climatology, and Geophysics Agency (BMKG) of Bogor in anticipating the   future impacts of rainfall.