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primary topic: RAINFALL PREDICTION MODEL USING MACHINE LEARNING ALGORITHM

RAINFALL PREDICTION MODEL USING MACHINE LEARNING ALGORITHM
type: Thesis
type: Article
abstract: 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 bet…
date: 2024-07-29
status: published
author list: authors
type: EPrint
type: ThesisEPrint
is part of: repository
has document: RAINFALL PREDICTION MODEL USING MACHINE LEARNING ALGORITHM (Text)
subject: Ilmu Komputer

RAINFALL PREDICTION MODEL USING MACHINE LEARNING ALGORITHM (Text)
type: Document
has file: Skripsi_065119157_M.Azizan.pdf
has part: Skripsi_065119157_M.Azizan.pdf

Universitas Pakuan
type: Organization

Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Pakuan
type: Organization

Boldson Herdianto Situmorang
type: Person
given name: Boldson
family name: Herdianto Situmorang

Muhammad Azizan
type: Person
given name: Muhammad
family name: Azizan

Prihastuti Harsani
type: Person
given name: Prihastuti
family name: Harsani

Fakultas Matematika dan Ilmu Pengetahuan Alam
type: Organization

Program Studi Ilmu Komputer
type: Organization

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primary topic: RAINFALL PREDICTION MODEL USING MACHINE LEARNING ALGORITHM

Ilmu Komputer
type: Concept