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All Journal JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI UNEJ e-Proceeding BAREKENG: Jurnal Ilmu Matematika dan Terapan Jurnal Kreativitas PKM Jurnal Abdi Insani ESTIMASI: Journal of Statistics and Its Application Journal of Social Outreach Journal of Research Applications in Community Services Eksponensial STATISTIKA
Darnah, Darnah
Mulawarman University
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Journal : Eksponensial

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Penerapan Algoritma K-Medoids pada Pengelompokan Wilayah Provinsi di Indonesia Berdasarkan Indikator Pendidikan Septian, Rama; Darnah, Darnah
EKSPONENSIAL Vol. 14 No. 2 (2023)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/eksponensial.v14i2.1150

Abstract

Cluster analysis has the goal of grouping data that has the same characteristics into the same cluster and data that has different properties will enter into different clusters. K-Medoids is a grouping method using a representative object as the center point (medoids). The k-medoids method was developed to overcome the weakness of the k-means method which is sensitive to outliers because an object with a large value allows it to deviate from the data distribution in size. After grouping using k-medoids, the results of the grouping were validated. The cluster validation method using the Silhoutte Coefficient (SC) is a method that can be used to see the quality and strength of clusters that combine cohesion and separation methods. This study aims to obtain the optimal cluster from the largest SC value and determine the grouping results of the optimal clusters that are formed. This grouping method is applied to data on education indicators in Indonesia in 2020. Based on the results of the analysis, it is found that the optimal cluster is 2 clusters with a SC value of 0.464, where cluster 1 has 14 provinces and cluster 2 has 20 provinces. Keywords: K-Medoids, Silhoutte Coefficient, Educational Indicators
Model Regresi Nonparametrik Spline Truncated Pada Indeks Pembangunan Manusia di Indonesia Ramadhan, M. Rizky; Darnah, Darnah; Wahyuningsih, Sri
EKSPONENSIAL Vol. 14 No. 2 (2023)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/eksponensial.v14i2.1090

Abstract

Truncated spline nonparametric regression is a nonparametric regression analysis using a segmented polynomial model. This segmented nature provides flexibility so that the regression model can adapt more effectively to the local characteristics of the data. The purpose of this study was to obtain a regression model and determine the factors that influence the Human Development Index (HDI) in all provinces in Indonesia using multivariable truncated spline nonparametric regression. The Human Development Index is an important indicator in measuring success in efforts to build the quality of human life. The Human Development Index can determine the rank or level of development of a region and a country. In development, a high Human Development Index is something that is expected to be achieved, especially for developing countries. The Human Development Index data used in this study is based on BPS data published in 2020 from all provinces in Indonesia. In this study, based on the results of the analysis, the best nonparametric truncated spline regression was obtained using 1 knot point, 2 knot point and 3 knot point. Based on the minimum Generalized Cross Validation (GCV) value, the best truncated spline regression model is 3 knots with an R2 value of 83.70%. The factors that influence the Human Development Index are the variables expected length of schooling, life expectancy at birth, and population
Mengatasi Multikoliniearitas Dalam Regresi Linier Berganda Menggunakan Principal Component Analysis Chairunnisa, Niken Harel; Darnah, Darnah; Syaripuddin, Syaripuddin
EKSPONENSIAL Vol. 16 No. 1 (2025): Jurnal Eksponensial
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/eksponensial.v16i1.1155

Abstract

Multiple linear regression analysis has assumptions that must be met, one of which is multicollinearity. Multicollinearity occurs when the independent variables correlate with each other, resulting in the regression coefficient produced by multiple linear regression analysis being very weak or unable to provide analysis results that represent the nature or influence of the independent variable concerned. The detection of multicollinearity can be known through the VIF value. In this study, human development index data on Kalimantan Island in 2019 detected multicollinearity because some independent variables have a VIF value of more than 10 so that the method used to overcome multicollinearity in this study is Principal Component Analysis (PCA). Based on the results of research using the Principal Component Regression method, There are five independent variables that influence the IPM that is Percentage of Poor Population, Number of Health Workers, Number of Workforce, Number of High Schools, and Number of High School Teachers.
Co-Authors Alfina Damayanti Chairunnisa, Niken Harel Dani, Andrea Tri Rian Dini Elizabeth Eka Nur Amaliah Fauziyah, Meirinda Ghony N Nurhuda Ika Purnamasari Khairun Nida Kosasih, Raditya Arya Kriesniati, Prastika Krisna Rendi Awalludin M. Fathurahman M. RIZKY RAMADHAN Meiliyani Siringoringo Memi Nor Hayati Memi Norhayati Nadia Serena Rito Goejantoro, Rito Rut Esra Sekar Nur Utami Septian, Rama Sifriyani, Sifriyani Surya Prangga Syaripuddin Syaripuddin Wasono Wasono Yuniarti, Desi