<> "The repository administrator has not yet configured an RDF license."^^ . <> . . . "RAINFALL PREDICTION MODEL USING \r\nMACHINE LEARNING ALGORITHM"^^ . "RAINFALL PREDICTION MODEL USING \r\nMACHINE LEARNING ALGORITHM\r\nMuhammad Azizan1\r\n, Prihastuti Harsani2\r\n, Boldson H.Situmorang3\r\n1,2,3 Department of Computer Science, Faculty of Mathematics and Natural Science, Pakuan\r\nUniversity, Bogor, West Java, 16143, Indonesia\r\nAbstract\r\nIndonesia is a country where most regions experience two seasons: the dry season and the rainy \r\nseason. The significant difference between these seasons lies in the average monthly rainfall. Rainfall is \r\na key indicator in measuring weather conditions, as the amount of measured rainfall can indicate the \r\nintensity of rain in a specific area. Rainfall is measured in millimeters (mm) at monthly intervals. Rainfall \r\nhas a major impact on human life, both in excess and in shortage. Excessive rainfall, such as heavy or \r\nextreme rain, often leads to flooding, especially in the city of Bogor, which is known as a flood and \r\nlandslide-prone area due to its high rainfall intensity. Therefore, an effective rainfall prediction method \r\nis needed to help reduce the negative impacts of extreme weather. In this study, a rainfall prediction model \r\nwas developed for the Bogor area using the Multilayer Perceptron algorithm, with 90% of the data for \r\ntraining and 10% for testing. The results show that the model can predict rainfall accurately, with an \r\nRMSE value of 17.8763, indicating a good level of accuracy. RMSE is used as the main indicator in \r\nevaluating 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 \r\nmitigation. With high accuracy, this model can support decision-making in areas vulnerable to floods and \r\ndroughts, thus minimizing the negative impacts of weather variability.\r\nKeywords: Prediction; Rainfall; Multilayer Perceptron\r\n1. Introduction\r\nIndonesia is a country where most of its regions experience two seasons in a year: the dry season and \r\nthe rainy season. The main difference between these two seasons is the average monthly rainfall. Rainfall \r\nrefers to the height of water collected in a rain gauge placed on a flat surface that does not absorb, drain, or \r\nevaporate [1]. Rainfall plays a significant role in human life on this planet. Both high and low rainfall levels \r\ngreatly impact the climate conditions on Earth’s surface. Excessive rainfall can cause various problems, \r\nsuch as floods, crop failures, and other events. Heavy rain with high intensity, commonly referred to as \r\nextreme rain, can trigger floods. Bogor, one of the cities in Indonesia, often falls victim to flooding due to \r\nhigh rainfall, and this sometimes leads to landslides and building damage, causing significant losses to the \r\nlocal population. Therefore, a specific method is required to obtain optimal rainfall predictions [2]. \r\nArtificial Neural Networks, specifically the Multilayer Perceptron (ANN-MLP) model, can be used to\r\npredict weather changes more accurately, enabling the application of this forecasting system in daily life \r\n[3]. The variables used in the MLP method include rainfall, average temperature, average humidity, and \r\nsunshine duration. The advantage of using the MLP method lies in its ability to determine better weight \r\nvalues compared to other methods [4]. In this study, the selection of the Multilayer Perceptron method is \r\nexpected to improve rainfall prediction accuracy by modeling complex relationships between variables in \r\nthe context of weather data. The goal of this research is to predict rainfall in Bogor in 2023 using data \r\ndownloaded from the BMKG website. The information generated from this research is expected to serve as \r\na basis for the Meteorology, Climatology, and Geophysics Agency (BMKG) of Bogor in anticipating the \r\nfuture impacts of rainfall."^^ . "2024-07-29" . . . "Universitas Pakuan"^^ . . . "Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Pakuan"^^ . . . . . . . . . . . . . . . . . . "Boldson"^^ . "Herdianto Situmorang"^^ . "Boldson Herdianto Situmorang"^^ . . "Muhammad"^^ . "Azizan"^^ . "Muhammad Azizan"^^ . . "Prihastuti"^^ . "Harsani"^^ . "Prihastuti Harsani"^^ . . "Universitas Pakuan"^^ . . . "Fakultas Matematika dan Ilmu Pengetahuan Alam"^^ . . . "Program Studi Ilmu Komputer"^^ . . . . . . . "RAINFALL PREDICTION MODEL USING \r\nMACHINE LEARNING ALGORITHM (Text)"^^ . . . "Skripsi_065119157_M.Azizan.pdf"^^ . . "HTML Summary of #8643 \n\nRAINFALL PREDICTION MODEL USING \nMACHINE LEARNING ALGORITHM\n\n" . "text/html" . . . "Ilmu Komputer"@en . .