PREDICTION OF POPULATION GROWTH IN KARAWANG CITY USING MULTIPLE LINEAR REGRESSION ALGORITHM METHOD

Authors

  • Fatiya Hanifah Desfianthy Universitas Buana Perjuangan Karawang
  • Shofa Shofiah Hilabi Universitas Buana Perjuangan Karawang
  • Bayu Priyatna Universitas Buana Perjuangan Karawang
  • Elfina Novalia Universitas Buana Perjuangan Karawang

Abstract

Currently, Indonesia is experiencing population growth. The factors influencing this growth are the rates of births and deaths. Every year, the population in an area keeps growing. This growth can have various negative impacts on the region. That's why taking action and making predictions about population growth is crucial. The objective of this study is to use a regression algorithm to estimate how fast the population will grow in Karawang City. The data used for this research comes from population records collected by the Karawang City Statistics Agency between 2017 and 2022. To clean, transform, and analyze this data, we employ the Knowledge Discovery in Database (KDD) approach to data mining. By applying linear regression methods with assistance from RapidMiner tools, we have successfully generated predictions based on data that reveal patterns and relationships between variables that influence population growth rates. According to our predictions, there will increase of 338,011 people from 2022 to 2027. This research will assist the Karawang City government in developing plans to minimize negative impacts while optimizing resource utilization such as energy, food, water, and services.

Keywords: Multiple Linear Regression, Data Mining, BPS, Rapid Miner

References

P. Purwadi, PS Ramadhan, and N. Safitri, "Application of Data Mining to Estimate Population Growth Rates Using Multiple Linear Regression Methods at BPS Deli Serdang," J. SAINTIKOM (Journal of Information and Computer Science Management), vol. 18, no. 1, p. 55, 2019, doi: 10.53513/jis.v18i1.104.

T. Praja Utama et al., "Application of Naïve Bayes and Forward Selection Algorithms for Stroke Prediction," vol. 17, no. 2, pp. 351–357, 2023, [Online]. Available: https://ejurnal.teknokrat.ac.id/index.php/teknoinfo/index

D. Sekar Seruni, M. Tanzil Furqon, and R. Cahya Wihandika, "Population Growth Prediction System for Malang City Using the K-Nearest Neighbor Regression Method," J. Pememb. Technol. Inf. and Computer Science., vol. 4, no. 4, pp. 1075–1082, 2020.

West Java Central Statistics Agency, Latest Statistical Indicators for West Java Province September 2023, vol. 17, no. 2. 2023. doi: 10.25104/mtm.v17i2.1325.

RA Margolang, SR Andani, and MR Lubis, "Implementation of Data Mining in Grouping Slum Households in Urban Areas Based on Province Using the K-Means Algorithm," Pros. Semin. Nas. Ris. Inf. Sci., vol. 1, no. September, p. 602, 2019, doi: 10.30645/senaris.v1i0.66.

S. Widaningsih, "Comparison of Data Mining Methods for Predicting Grades and Graduation Times of Informatics Engineering Study Program Students Using the C4.5, Naïve Bayes, Knn and Svm Algorithms," J. Tekno Incentive, vol. 13, no. 1, pp. 16–25, 2019, doi: 10.36787/jti.v13i1.78.

SS Hilabi and . P., "ANALYSIS OF USER SATISFACTION TOWARDS WhatsApp MOBILE ONLINE SOCIAL MEDIA APPLICATION SERVICES," Buana Ilmu, vol. 3, no. 1, pp. 119–136, 2018, doi: 10.36805/bi.v3i1.461.

M. Mona, J. Kekenusa, and J. Prang, “Using Multiple Linear Regression to Analyze Coconut Farmers' Income. Case Study: Coconut Farmers in Beo Village, Beo District, Talaud Regency," d'CARTESIAN, vol. 4, no. 2, p. 196, 2015, doi: 10.35799/dc.4.2.2015.9211.

A. Agung and A. Putri, "Application of Data Mining to Estimate the Rate of Data Mining Usage to Estimate Civil Growth in Denpasar," Jbase, vol. 6, no. 1, pp. 37–44, 2023.

E. Dewi et al., "Estimation of Population Growth in Tasikmalaya Regency Using Multiple Linear Regression Methods," vol. 6 No. 1, pp. 1–11, 2021.

CA Rahmat, Kurniabudi, and Y. Novianto, "Application of the Multiple Linear Regression Method to Estimate the Population Growth Rate of Musi Banyuasin Regency Journal of Informatics and Computer Engineering (JAKAKOM)," vol. 3, no. April, pp. 359–369, 2023.

I. Sukmawati1, S. Oktania, and Terttiaavini, "Application of Data Mining to Estimate Population Growth in South Sumatra Province Using the Multiple Linear Regression Method.pdf." 2023.

N. Mahmudah, N. Khoiriyah, PS Statistics, U. Nahdlatul, and U. Sunan, "IMPLEMENTATION OF GEOGRAPHICALLY WEIGHTED LOGISTIC REGRESSION ON POPULATION GROWTH RATE IN BOJONEGORO," vol. 4, no. 3, pp. 1856–1866, 2023.

SZ Luki Ardiantoro, "Utilization of Knowledge Data Discovery (KDD) in Badminton Athletes' Game Patterns," Explor. IT J. Science and Applications. Tech. Inform., vol. 11, no. 1, pp. 1–6, 2019, doi: 10.35891/explorit.v11i1.1467.

A. Agneresa, AL Hananto, SS Hilabi, A. Hananto, and T. Tukino, "Strategy for Promoting the Implementation of Data Mining for New Students Using the K-Means Clustering Method," Dirgamaya J. Manaj. and Sis. Inf., vol. 2, no. 2, pp. 25–34, 2022, doi: 10.35969/dirgamaya.v2i2.275.

MD Kurniawan, B. Priyatna, and F. Nurapriani, "Implementation of the K-Means Algorithm for Clustering Drug Data at the Kotabaru Health Center," vol. 7, no. September, pp. 882–890, 2023.

E. Novalia and A. Voutama, "Prediction of Rice Field Planted Area with CRISP-DM Using Classification and Regression Tree (Cart) Algorithms," vol. 5, no. 1, p. 578, 2023.

A. Octa Fadilah, B. Huda, and A. Hananto, "Promotional Strategy to Increase Decimal Coffee Shop Sales Using the K-Medoids Clustering Algorithm," J. Ris. Computer), vol. 10, no. 1, pp. 2407–389, 2023, doi: 10.30865/jurikom.v10i1.5561.

ES Ompusunggu, W. Sinaga, M. Siahaan, and J. Winata, "Implementation of Data Mining To Predict the Value of Indonesian Oil and Non-Oil and Gas Import Exports Using the Linear Regression Method," J. Sist. Inf. and Computer Science. Prima(JUSIKOM PRIMA), vol. 7, no. 1, pp. 168–176, 2023, doi: 10.34012/jurnalsisteminformationdanilmukomputer.v7i1.4081.

H. Wijana and A. Finandhita, "APPLICATION OF DATA MINING USING ASSOCIATION RULE METHOD ON SALES DATA IN KOPI CILIK CAFE," 2019.

I. Indriani, D. Siregar, and AP Windarto, "Application of the Linear Regression Method in Estimating Population Numbers," JURIKOM (Journal of Computer Research), vol. 9, no. 4, p. 1112, 2022, doi: 10.30865/jurikom.v9i4.4676.

Aprilla Dennis, “Learning Data Mining with RapidMiner,” Innov. Knowl. Manag. Bus. Globe. Theory Pract. Vols 1 2, vol. 5, no. 4, pp. 1–5, 2013, [Online]. Available: http://esjournals.org/journaloftechnology/archive/vol1no6/vol1no6_6.pdf%5Cnhttp://www.airccse.org/journal/nsa/5413nsa02.pdf

BG Sudarsono, MI Leo, A. Santoso, and F. Hendrawan, "Data Mining Analysis of Netflix Data Using the Rapid Miner Application," JBASE - J. Bus. Audit Inf. Syst., vol. 4, no. 1, pp. 13–21, 2021, doi: 10.30813/jbase.v4i1.2729.

Downloads

Published

2024-03-13

How to Cite

[1]
F. H. Desfianthy, S. S. Hilabi, B. . Priyatna, and E. . Novalia, “PREDICTION OF POPULATION GROWTH IN KARAWANG CITY USING MULTIPLE LINEAR REGRESSION ALGORITHM METHOD”, JUSIKOM PRIMA, vol. 7, no. 2, pp. 90-103, Mar. 2024.