The Application of Multiple Linear Regression Method for Population Estimation Gunung Malela District

Widia Ayu Lestari Sinaga, S. Sumarno, Ika Purnama Sari
{"title":"The Application of Multiple Linear Regression Method for Population Estimation Gunung Malela District","authors":"Widia Ayu Lestari Sinaga, S. Sumarno, Ika Purnama Sari","doi":"10.55123/jomlai.v1i1.143","DOIUrl":null,"url":null,"abstract":"Population growth in an area is important for development and is a benchmark for an area to develop. The way to predict population growth is to use Data Mining. Data mining is able to analyze data into information. This study will discuss the amount of population growth in the District of Gunung Malela. The estimation technique that will be used is Multiple Linear Regression. This method was chosen because it can make an estimate/prediction by utilizing old data regarding population growth so that it can produce a pattern of relationships. This Multiple Linear Regression method aims to make the best predictions. The research data used is the population in the Gunung Malela sub-district in 2016-2020. Based on the research that has been done using the Multiple Linear Regression method, the results of the population growth are 40078 residents. This means that there is an additional population of 469 people in Gunung Malela District. The results of this study can be input to the Gunung Malela Sub-District Office to anticipate the rate of population growth and it can be concluded based on this study that the Multiple Linear Regression method can be used to estimate the population.","PeriodicalId":14854,"journal":{"name":"JOMLAI: Journal of Machine Learning and Artificial Intelligence","volume":"8 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2022-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"JOMLAI: Journal of Machine Learning and Artificial Intelligence","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.55123/jomlai.v1i1.143","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2

Abstract

Population growth in an area is important for development and is a benchmark for an area to develop. The way to predict population growth is to use Data Mining. Data mining is able to analyze data into information. This study will discuss the amount of population growth in the District of Gunung Malela. The estimation technique that will be used is Multiple Linear Regression. This method was chosen because it can make an estimate/prediction by utilizing old data regarding population growth so that it can produce a pattern of relationships. This Multiple Linear Regression method aims to make the best predictions. The research data used is the population in the Gunung Malela sub-district in 2016-2020. Based on the research that has been done using the Multiple Linear Regression method, the results of the population growth are 40078 residents. This means that there is an additional population of 469 people in Gunung Malela District. The results of this study can be input to the Gunung Malela Sub-District Office to anticipate the rate of population growth and it can be concluded based on this study that the Multiple Linear Regression method can be used to estimate the population.
多元线性回归方法在古农马勒拉区人口估计中的应用
一个地区的人口增长对发展很重要,是一个地区发展的基准。预测人口增长的方法是使用数据挖掘。数据挖掘能够将数据分析成信息。本研究将讨论古农马勒拉地区的人口增长量。将使用的估计技术是多元线性回归。之所以选择这种方法,是因为它可以利用有关人口增长的旧数据进行估计/预测,从而产生关系模式。这种多元线性回归方法旨在做出最好的预测。使用的研究数据是2016-2020年Gunung Malela街道的人口。根据多元线性回归方法所做的研究,人口增长的结果是40078人。这意味着古农马勒拉县的人口增加了469人。本研究的结果可以输入到古农马勒拉街道办事处预测人口增长率,根据本研究可以得出多元线性回归方法可以用来估计人口。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信