JURTEKSI (Jurnal Teknologi dan Sistem Informas.
Vol.
X No 1.
Desember 2023, hlm.
1 - 8
DOI: https://doi.
org/10.
33330/jurteksi.
Available online at http://jurnal.
id/index.
php/jurteksi ISSN 2407-1811 (Prin.
ISSN 2550-0201 (Onlin.
APPLICATION OF MULTIPLE LINEAR REGRESSION ESTIMATING THE POPULATION OF ASAHAN REGENCY Tasya Azhara*.
Nurul Rahmadani2.
Andri Nata3 Information System.
Sekolah Tinggi Manajemen Informatika dan Komputer Royal Informatics Management.
Sekolah Tinggi Manajemen Informatika dan Komputer Royal email: *tasyaazhara721@gmail.
Abstract: Population is a group of people who live or settle in an area for six months or more.
Increasing the population in an area results in more problems being faced by the area such as high unemployment rates, poverty and food shortages which result in hunger.
BPS Asahan Regency records that there is an increase in population every year.
Asahan Regency BPS cannot predict population growth in the following year, so an application is needed to predict population growth.
The purpose of this study is to predict population growth in Asahan Regency in the following year based on previous data using the concept of data mining.
By applying data mining using multiple linear regression methods can be used to calculate population growth estimates based on previous data.
This quantitative research used population data of Asahan Regency from 2016 to 2022.
From the calculation of the multiple linear regression model using data from the previous five years, the estimated population growth of Asahan for 2023 was 824,617 people and the process of estimating the population became more systematic and calculated.
well with this population estimation system and the process of storing data becomes easier and does not require a lot of paper to print and save.
Keywords: Application.
Data Mining.
Multiple Linear Regression Abstrak: Penduduk merupakan sekumpulan orang yang tinggal atau menetap pada suatu wilayah selama enam bulan atau lebih.
Bertambahnya jumlah penduduk pada suatu daerah mengakibatkan semakin banyak pula persoalan yang di hadapi oleh daerah tersebut seperti tingkat pengangguran yang tinggi, kemiskinan dan kekurangan pangan yang mengakibatkan kelaparan.
BPS Kabupaten Asahan mencatat terjadi adanya pertambahan penduduk pada setiap tahunnya.
BPS Kabupaten Asahan tidak dapat memprediksi pertumbuhan penduduk pada tahun berikutnya sehingga dibutuhkan suatu aplikasi untuk memprediksi pertambahan penduduk tersebut.
Tujuan penelitian ini adalah untuk memprediksi pertumbuhan penduduk Kabupaten Asahan pada tahun berikutnya berdasarkan data sebelumnya menggunakan konsep data mining.
Dengan menerapkan data mining menggunakan metode regresi linier berganda dapat digunakan untuk menghitung estimasi pertumbuhan penduduk berdasarkan data sebelumnya.
Penelitian yang dilakukan secara kuantitatif ini menggunakan data penduduk Kabupaten Asahan dari tahun 2016 sampai tahun 2022.
Dari perhitungan model regresi linier berganda menggunakan data lima tahun sebelumnya didapat estimasi pertumbuhan penduduk Asahan untuk tahun 2023 sebesar 824.
617 jiwa dan proses estimasi jumlah penduduk menjadi lebih sistematis dan terkalkulasi dengan baik dengan adanya sistem estimasi jumlah penduduk ini dan proses penyimpanan data menjadi lebih mudah dan tidak memerlukan banyak kertas untuk di cetak dan di simpan.
Kata kunci: Aplikasi.
Data Mining.
Regresi Linier Berganda JURTEKSI (Jurnal Teknologi dan Sistem Informas.
Vol.
X No 1.
Desember 2023, hlm.
1 - 8
DOI: https://doi.
org/10.
33330/jurteksi.
Available online at http://jurnal.
id/index.
php/jurteksi ISSN 2407-1811 (Prin.
ISSN 2550-0201 (Onlin.
information from large data sets using algorithms and drawing techniques from statistics, machine learning and database management systems .
Data mining which is also known as Knowledge Discovery in Database (KDD) is an automatic process of searching data in a very large memory of data to find out patterns by using tools such as classification, association or clustering .
Data mining can be used to calculate population growth estimates .
The method used to calculate is Multiple Linear Regression.
Multiple Linear Regression is an analysis that has more than one independent variable .
Multiple Linear Regression Techniques are used to determine whether there is a significant effect of two or more independent variables (X1.
X2.
on the dependent variable (Y) .
In this study, the area whose population you want to know is Asahan District.
Based on data on the number of residents in the Central Statistics Agency (BPS) of Asahan Regency, it can be seen that there is a difference in the number of residents each year.
Every year the population of Asahan Regency always increases.
Because every year the population in Asahan Regency is increasing, the authors are interested in estimating the population of Asahan Regency.
Population estimation is not a forecast but a scientific calculation based on assumptions about the growth rate component .
To estimate a population or total population, it can be done by using a model whose results are close to the population data held by the Central Bureau of Statistics (BPS).
To estimate the population, one of the models used is the Multiple Linear Regression model.
In the case of this population, the Multiple Linear Regression model is used to find out INTRODUCTION Residents are a group of people who live or live in an area for six months or more and people who stay for less than six months but have the intention of settling in the area.
Residents are people who live in a place, its mean that the population is a group of people who live in an area .
The more the population of an area, the higher the changes in the area and the more problems faced by a region.
population growth rate that is too high will risk causing various problems for the area, such as high unemployment rates, poverty and food shortages which lead to Factors that influence population growth include: births .
, deaths .
and also population migration.
Continuously the population will be affected by the increase in the number of births .
, but simultaneously it will also be reduced by the number of deaths .
that occur in all age groups .
BPS has the duty and function of collecting statistical data on the population from year to year.
The use of this data collection is for country data collection for the needs of economic strategy, infrastructure, and so on.
So that the BPS institution can predict the estimated population growth which is calculated using the geometric method and calculated using Microsoft Excel so that the data is not stored in the database but in the excel file Population projection using the geometric method uses the assumption that the population will increase geometrically using a compound calculation basis with the population growth rate .
ate of growt.
considered the same for each year .
Data mining process of extracting JURTEKSI (Jurnal Teknologi dan Sistem Informas.
Vol.
X No 1.
Desember 2023, hlm.
1 - 8
DOI: https://doi.
org/10.
33330/jurteksi.
Available online at http://jurnal.
id/index.
php/jurteksi the population of Asahan Regency in The purpose of this research is to find out how to use the multiple linear regression method to estimate the population and with the concept of data mining using multiple linear regression to estimate the population in the following year using a web-based application.
Research conducted by .
with the title Application of Data Mining to Estimate Population Growth Rates Using Multiple Linear Regression Methods at BPS Deli Serdang concludes that the multiple linear regression method can help BPS to find out what attributes/criteria affect the rate of population And also found patterns that are closely related between the attributes of the number of men and the number of women to the rate of population growth.
While the research entitled Multiple Linear Regression Analysis in Estimating Rice Productivity in Karawang Regency Tesa conducted by .
concluded that the regression model obtained, amounting to 80.
46%, rice productivity factors can be explained by production, harvested area, planting area , rainfall, and rainy days.
While the remaining 19.
54% can be explained by other factors not examined in this study.
The variables that affect the increase in total productivity of rice are production and rainfall variables, while the variables that affect the decrease in total productivity are harvested area, planted area, and rainy days.
The average regression relative error obtained is or 4.
Subsequent studies examined by .
with the title Prediction of Increase in Sales Turnover Using Multiple Linear Regression Methods found that data mining using multiple linear regression methods calculates the equation and then produces the desired sales prediction.
ISSN 2407-1811 (Prin.
ISSN 2550-0201 (Onlin.
The system created can be used to predict an increase in sales turnover using multiple linear regression methods with fairly accurate results.
METHOD
This study uses quantitative research methods conducted on BPS Office Jl.
Tusam No.
Kisaran.
Mekar Baru.
Kec.
Kota Kisaran Barat.
Kabupaten Asahan.
Sumatera Utara 21216.
The data collection technique was carried out by literature study by studying journals, books and previous research, the following were interviews with BPS members and direct observation of the research The multiple linear regression model is an equation that describes the relationship between two or more independent variables (X1.
X2,AX.
and one dependent variable (Y).
The purpose of multiple linear regression analysis is to predict the value of the dependent variable (Y) if the values of the independent variables or predictors (X1.
X2, .
are known and also to find out the direction of the relationship between the dependent variables.
independent with independent variables.
Multiple linear regression equations can be calculated using the formula .
Y = a b1X1 b2X2 A bnXn .
Where :
Y = dependent variable .
alue to be predicte.
a = constant b1, b2,.
, bn = regression coefficient X1.
X2,A.
Xn = independent variable RESULTS AND DISCUSSION For the calculation of multiple linear regression, initial data is needed, namely the population data.
Here is the3 JURTEKSI (Jurnal Teknologi dan Sistem Informas.
Vol.
X No 1.
Desember 2023, hlm.
1 - 8
DOI: https://doi.
org/10.
33330/jurteksi.
Available online at http://jurnal.
id/index.
php/jurteksi ISSN 2407-1811 (Prin.
ISSN 2550-0201 (Onlin.
population data used:
No.
Subdistrict Table 1.
Population Data Population Data Asahan Regency Bandar Pasir Mandoge Bandar Pulau Aek Songsongan Rahuning Pulau Rakyat Aek Kuasan Aek Ledong Sei Kepayang Sei Kepayang Barat Sei Kepayang Timur Sei Dadap Buntu Pane Tinggi Raja Setia Janji Meranti Pulo Bandring Rawang Panca Arga Air Joman Silo Laut Kisaran Barat Kisaran Timur Asahan JURTEKSI (Jurnal Teknologi dan Sistem Informas.
Vol.
X No 1.
Desember 2023, hlm.
1 - 8
DOI: https://doi.
org/10.
33330/jurteksi.
Available online at http://jurnal.
id/index.
php/jurteksi Because in multiple linear regression calculations a lot of multiplication and exponents are carried out, to simplify the numbers will be divided by 1000 and this table determines X1 .
X2 .
and Y .
otal populatio.
so as to produce the following table:
ISSN 2407-1811 (Prin.
ISSN 2550-0201 (Onlin.
26 is obtained, the value of b1 = 277 and the value of b2 = 1,311.
And produce a regression equation y = a b1.
x1 b2.
= -968,26 .
,277 * 393,.
,311 *
= -968,26 1289,146 503,731
= 824,617 x 1000 = 824.
617 People Table 2.
Data Simplification Year Y (Total (Ma.
(Woma.
Populatio.
2018 360,901 357,817 718,718 2019 363,686 360,693 724,379 2020 366,603 363,192 729,795 2021 389,391
380,569
769,96
2022 393,392
384,234
777,626
Total 1873,973 1846,505 3720,478 Application View Then process the calculation overview based on the x1, x2 and y values divided Table 3.
Simplification of Calculation Overview
X1^2
X2^2
Y^2
13,025
12,8033
51,6556
13,226
13,0099
52,4725
13,439
13,1908
53,2601
15,162
14,4833
59,2838
15,475
14,7636
60,4702
70,330
68,251
277,142
Image 1.
Login Page A username and password are needed so you can enter the page, for example entering as an officer or admin.
Table 4.
Advance Simplification of Calculation Overview X1.
X2.
X1.
25,9386
25,717
12,913
26,3447 26,1278 13,117
26,7545 26,5056 13,314
29,9815 29,3023 14,819
30,5912
29,879
15,115
139,61
137,532 69,281
Then enter the numbers that have been obtained in Tables 3 and 4 and the values from table 2 so that the value a = - Image 2.
District Data JURTEKSI (Jurnal Teknologi dan Sistem Informas.
Vol.
X No 1.
Desember 2023, hlm.
1 - 8
DOI: https://doi.
org/10.
33330/jurteksi.
Available online at http://jurnal.
id/index.
php/jurteksi After logging in, the sub-district display appears which contains the name ISSN 2407-1811 (Prin.
ISSN 2550-0201 (Onlin.
ple linear regression calculations.
Image 3.
Officer Data Image 6.
Report After successfully logging in, an officer's view appears containing the user fields, name, address, cellphone number and gender.
The following is the result of calculating the population from 2018 to CONCLUSION Based on the research and testing that has been carried out while designing and making this population estimation system, it can be concluded that From the calculation of the multiple linear regression model using data from the previous five years, the estimated population growth of Asahan for 2023 is 824,617 , the population estimation process becomes more systematic and well calculated with this population estimation system and the data storage process becomes easier and does not require a lot of paper to be printed and stored.
Image 4.
Census Data Next, there is a display of population census data and this is a display of population data from 2018-2022.
And on the top right there is a button for residents when you want to add or take a population census.
BIBLIOGRAPHY