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All Journal JINAV: Journal of Information and Visualization VARIANSI: Journal of Statistics and Its Application on Teaching and Research
Muhammad Nusrang
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Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering Library & Information Science Mathematics

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PEMODELAN FAKTOR-FAKTOR YANG BERPENGARUH TERHADAP ANGKA BUTA HURUF DI PROVINSI SULAWESI SELATAN DENGAN GEOGRAPHICALLY WEIGHTED LOGISTIC REGRESSION (GWLR) Nurul Era Natasyah Beddu Solo; Muhammad Nusrang; Zakiyah Mar'ah
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol. 6 No. 01 (2024)
Publisher : Program Studi Statistika Fakultas MIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/variansiunm141

Abstract

Geographically Weighted Logistic Regression (GWLR) is the development of a logistic regression model applied to spatial data from non-stationary processes with categorical response variables. The high rate of illiteracy is one of the crucial problems in the field of education that has not been resolved to date. South Sulawesi is the 4th province with the highest percentage of illiteracy in Indonesia in 2022. This research aims to obtain the GWLR model and the factors that have a significant influence on the illiteracy rate in South Sulawesi in 2022. In this research, we compare three functions Kernel weightings are Adaptive Gaussian Kernel, Adaptive Bisquare Kernel, and Adaptive Tricube Kernel. Selection of the best model uses the smallest AIC value. The results of this research are that the GWLR model with the Adaptive Tricube Kernel weighting function is the best model in modeling cases of illiteracy in South Sulawesi in 2022 which is obtained based on the smallest AIC value and the factor that has a significant influence on the illiteracy rate is the Open Unemployment Rate (X1), percentage of poor population (X2), Elementary School Enrollment Rate (X3), and area with city status (X4).
Application of Cluster Analysis of Self Organizing Map (SOM) Method in the Community Literacy Development Index in Indonesia Sanra Ariani; Muhammad Nusrang; Muhammad Kasim Aidid
JINAV: Journal of Information and Visualization Vol. 6 No. 1 (2025)
Publisher : PT Mattawang Mediatama Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.asci1571

Abstract

Self Organizing Map (SOM) is a method with a form of unsupervised learning, with Artificial Neural Network (ANN) training techniques that use a winner takes all basis, where only the neuron that is the winner will be updated. This study applies the cluster analysis of the SOM method in grouping provinces in Indonesia based on the characteristics of the Community Literacy Development Index (IPLM). The selection of the best cluster is based on internal validation i.e. connectivity, index Dunn and Silhouette. Based on the cluster validation results, 3 clusters were obtained that group provinces based on IPLM characteristics. of the 7 (seven) elements that make up the IPLM, 2 of them, namely energy and community visits, are shown in cluster 1. 5 other elements such as libraries, collections, SNP libraries, community involvement and library members are shown in cluster 3. Meanwhile, cluster 2 does not show significant IPLM-forming elements.
Co-Authors Muhammad Kasim Aidid Nurul Era Natasyah Beddu Solo Sanra Ariani Zakiyah Mar'ah