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All Journal Majalah Ilmiah Matematika dan Statistika (MIMS) Eksponensial
Nohe, Darnah Andi
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Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Mathematics Other

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Pengelompokan Puskesmas Berdasarkan Kasus Balita Stunting di Kabupaten Paser Menggunakan Metode K-Medoids Puspita, Ika; Hayati, Memi Nor; Nohe, Darnah Andi
EKSPONENSIAL Vol. 14 No. 1 (2023)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/eksponensial.v14i1.1089

Abstract

The number of cases of stunting toddler in Paser Regency increased by 6.66% from 2018 to 2019%. The increased in the number of stunting toddler in Paser Regency shows that the efforts made by the Paser Regency Government have not been effective in reducing the prevalence of stunting toddler because the stunting toddler rate in Paser Regency is still above the threshold set by the World Health Organization (WHO), which is a maximum of 20%. Therefore, an appropriate strategy is needed to find out which areas receive special attention and treatment, one of method to be used is cluster analysis. Cluster analysis is divided into two methods, namely the hierarchical method and the non-hierarchical method. The non-hierarchical method begins by establishing the number of groups. One of the methods included in the non-hierarchical method is K-medoids. In this study, clustering will be carried out in cases of stunting toddlers in Paser Regency using the K-medoids method. This study aims to determine the optimal cluster formed by selecting the smallest Davies Buoldin Index (DBI) value from the 2019 Community Health Center grouping in Paser Regency. The clusters formed for the K-medoids method in this study were 2 clusters, 3 clusters, and 4 clusters. Based on the results of the analysis, the K-medoids method for 2 clusters, 3 clusters and 4 clusters was based on the DBI values ​​of 0.977, 1.470, and 1.670, respectively. The optimal group for classifying stunting toddler cases in Paser Regency in 2019 is 2 cluster using K-medoids method.
Bayes' approach of linear regression to modeling the human development index in Indonesia Fazriwanandi, Achmad; Nohe, Darnah Andi; Wasono, Wasono
Majalah Ilmiah Matematika dan Statistika Vol 23 No 1 (2023): Majalah Ilmiah Matematika dan Statistika
Publisher : Jurusan Matematika FMIPA Universitas Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19184/mims.v23i1.31641

Abstract

Regression analysis is one of the data analysis techniques that can be used to examine the correlation between two variables, namely dependent variable and independent variable. It’s can be used to determine the parameter estimation of linear regression models are; the method of least squares or ordinary least square (OLS), Maximum likelihood estimation (MLE), and the Bayes method. Bayes' method defines the parameter as a random variable that describes the initial comprehension of the parameter before the observation was initiated and elucidated in an initial distribution refer as the prior distribution. The prior distribution used in this study is the pseudo prior distribution. The Data used in this study is secondary data, namely human development index (HDI) data in 2020, which was obtained from the website of the Central Statistics Agency (BPS). This study aims to estimate the regression model parameters using the Bayes method on the HDI data and the population data which adepts with information and communication technology (ICT) in Indonesia in 2020. The results of the specimen and analysis showed that population variables with ICT adepts have a significant effect on HDI variables. The results of the determination coefficient showed that 78.42% of HDI variables are affected by the population variables with ICT adepts while the remaining 21.58% are affected by other factors that have not been studied. Keywords: Bayes method, human development index, information communication and technology, linear regression, pseudo prior.MSC2020: 62F15
Co-Authors Fazriwanandi, Achmad Ika Puspita, Ika Memi Nor Hayati Wasono, Wasono