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APLIKASI FORECASTING MENGENAI ANGKA KELAHIRAN DI KOTA KENDARI MENGGUNAKAN METODE REGRESI LINEAR BERGANDA (STUDI KASUS: DINAS KESEHATAN KOTA KENDARI)

SemanTIK: Teknik Informasi · Vol. 4 No. 1 (2018) · pp. 31–38

DOI
10.55679/semantik.v4i1.4242
ISSN
2502-8928, 2460-1446
Published
2018-07-09
Publisher
Informatics Engineering Department of Halu Oleo University
Language
eng

Abstract

Birth rate is a very useful indicator not only of health status but also of overall population status, and the economic conditions in which the population lives. Population density in Kendari city in 2015 reached 1,174 people / km2. Population density in 10 sub-districts is quite diverse with the highest population density located in Kadia with the lowest density of 6,180 people / km2 in Baruga of 472 people/ km2. The purpose of this application is to predict the birth rate in Kendari by using multiple linear regression method.The Multiple Linear Regression Method is the most appropriate approach to predict future birth rates. An analysis that has more than one independent variable is called multiple linear regression analysis. The result of this system in the form of forecasting applications that can predict the birth rate in every month from all sub-districts in the Kendari and provide information about how strong the relationship between the factors that affect the birth rate.Keywords— Forecasting, Birth Rate, Multiple Linear Regression, Correlation DOI: 10.5281/zenodo.1402398

Collaboration network of the authors

40 authors · 118 co-authorship links · 4 across different organisations · showing the 40 most connected

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • Universitas Halu Oleo
  • Institut Teknologi Sepuluh Nopember
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Bambang PramonoUniversitas Halu Oleo7
Statiswaty StatiswatyUniversitas Halu Oleo5
Jumadil NangiUniversitas Halu Oleo4
LM. Fid AksaraUniversitas Halu Oleo3
Isnawaty IsnawatyAffiliation unknown3
Rahmat RamadhanUniversitas Halu Oleo3
Nur Fajriah MuchlisUniversitas Halu Oleo2
LM Bahtiar AksaraUniversitas Halu Oleo2
Ika Purwanti NingrumUniversitas Halu Oleo2
Natalis RansiUniversitas Halu Oleo2
Sari Fatiqah AyuAffiliation unknown2
Muh YaminUniversitas Halu Oleo2
Rizal Adi SaputraUniversitas Halu Oleo2
Lirna Frischa Dwi PutriAffiliation unknown1
Afriyadin AfriyadinAffiliation unknown1
Niarma NiarmaUniversitas Halu Oleo1
Mulatifa LalihaUniversitas Halu Oleo1
Rahmayunita RahmayunitaAffiliation unknown1
Yuwanda Purnamasari PasrunInstitut Teknologi Sepuluh Nopember1
Muh Iskandar Z. AAffiliation unknown1
Nur Arifah SyafitriAffiliation unknown1
Anita Puspita DewiAffiliation unknown1
Resti IdayaniAffiliation unknown1
Ramadhin ZaidUniversitas Halu Oleo1
Adnan HidayatUniversitas Halu Oleo1
Rosmila RosmilaAffiliation unknown1
Na’firul Hasna AriyaniAffiliation unknown1
Risna RisnaAffiliation unknown1
Sri Wayun WardaniAffiliation unknown1
Mutmainnah MuchtarInstitut Teknologi Sepuluh Nopember1
Annisyah JanuartiAffiliation unknown1
Muhammad HattaAffiliation unknown1
Adha Mashur SajiahUniversitas Halu Oleo1
Chindy Elfiana Suci NatasiyaAffiliation unknown1
Saskia Randawula SilondaeUniversitas Halu Oleo1
Edwin SaputraAffiliation unknown1
Muh FachrulUniversitas Halu Oleo1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://ojs.uho.ac.id/index.php/semantik/oai, first harvested 29 Okt 2026, last seen 1 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">APLIKASI FORECASTING MENGENAI ANGKA KELAHIRAN DI KOTA KENDARI MENGGUNAKAN METODE REGRESI LINEAR BERGANDA  (STUDI KASUS : DINAS KESEHATAN KOTA KENDARI)</dc:title>
	<dc:creator>Syafitri, Nike</dc:creator>
	<dc:creator>Sutardi, Sutardi</dc:creator>
	<dc:creator>Tajidun, LM</dc:creator>
	<dc:description xml:lang="en-US">Birth rate is a very useful indicator not only of health status but also of overall population status, and the economic conditions in which the population lives. Population density in Kendari city in 2015 reached 1,174 people / km2. Population density in 10 sub-districts is quite diverse with the highest population density located in Kadia with the lowest density of 6,180 people / km2 in Baruga of 472 people/ km2. The purpose of this application is to predict the birth rate in Kendari by using multiple linear regression method.The Multiple Linear Regression Method is the most appropriate approach to predict future birth rates. An analysis that has more than one independent variable is called multiple linear regression analysis. The result of this system in the form of forecasting applications that can predict the birth rate in every month from all sub-districts in the  Kendari and provide information about how strong the relationship between the factors that affect the birth rate.Keywords— Forecasting, Birth Rate, Multiple Linear Regression, Correlation DOI : 10.5281/zenodo.1402398</dc:description>
	<dc:publisher xml:lang="en-US">Informatics Engineering Department of Halu Oleo University</dc:publisher>
	<dc:contributor xml:lang="en-US"></dc:contributor>
	<dc:date>2018-07-09</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
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	<dc:type xml:lang="en-US">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://ojs.uho.ac.id/index.php/semantik/article/view/4242</dc:identifier>
	<dc:identifier>10.55679/semantik.v4i1.4242</dc:identifier>
	<dc:source xml:lang="en-US">SemanTIK : Teknik Informasi; Vol 4, No 1 (2018): semanTIK; 31-38</dc:source>
	<dc:source>2502-8928</dc:source>
	<dc:source>2460-1446</dc:source>
	<dc:source>10.55679/semantik.v4i1</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ojs.uho.ac.id/index.php/semantik/article/view/4242/3285</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2018 semanTIK</dc:rights>
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