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All Journal Majalah Ilmiah Matematika dan Statistika (MIMS) Limits: Journal of Mathematics and Its Applications
Fahriya, Andina
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Computer Science & IT Mathematics

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Penggerombolan provinsi di Indonesia berdasarkan instrumen akreditasi satuan pendidikan jenjang SMK menggunakan K-means dan average linkage Fahriya, Andina; Sembiring, Febryna; Susetyo, Budi
Majalah Ilmiah Matematika dan Statistika Vol. 24 No. 2 (2024): 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.v24i2.40822

Abstract

Improvement and updates need to be done in order to maintain the existence of a school. Accreditation is one of the references to assess the excellence of a school. There are several components used in the accreditation assessment included in the IASP, namely Graduate Quality, Learning Process, Teacher Quality, and School Management. Additionally, to determine which provinces have low, medium, or high IASP scores, clustering is performed on the IASP scores of those provinces. Cluster analysis is a method used to group research objects based on similarities in their characteristics. In this study, clustering was performed using the K-means and average linkage methods on the average IASP scores of vocational high schools (SMK) in 34 provinces in Indonesia. With the Elbow Criterion approach, four clusters were formed for each method. The results of Dunn Index showed that the average linkage method performed better in clustering compared to the K-Means method. Keywords: IASP, Cluster Analysis, K-Means, Average LinkageMSC2020: 62H30
Perbandingan Metode GWR, MGWR, dan MGWR-SAR pada Data Persentase Penduduk Miskin di Pulau Jawa Fahriya, Andina; Susetyo, Budi; Sumertajaya, I Made
Limits: Journal of Mathematics and Its Applications Vol. 22 No. 2 (2025): Limits: Journal of Mathematics and Its Applications Volume 22 Nomor 2 Edisi Ju
Publisher : Pusat Publikasi Ilmiah LPPM Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/limits.v22i2.3057

Abstract

The primary goal of Sustainable Development Goals (SDGs) is to end poverty everywhere in all its forms. Poverty is defined as the inability to meet basic needs, such as food, clothing, shelter, education, and healthcare. In Indonesia, the poor population has reached 26.36 million people, with half of them residing on Java Island. Extensive research has been conducted on poverty, particularly using a spatial approach. Spatial regression is a statistical method that explicitly incorporates geographical aspects into a model framework. In spatial regression, two main challenges arise: spatial dependence and heterogeneity. These two effects are inherently interconnected and must be considered simultaneously. Mixed Geographically Weighted Regression with Spatial Autoregressive (MGWR-SAR) is a combination of Mixed Geographically Weighted Regression (MGWR) and Spatial Autoregressive (SAR). MGWR-SAR effectively addresses both spatial dependence and spatial heterogeneity simultaneously. This study aims to determine the best method for modeling the percentage of poor population on Java. The variables used included PPM, BPJSPBI, PPKM, PLSMP, PPTB, BPNT, NCPR, and IPM. The kernel function was selected based on the smallest cross-validation (CV) value, which was a Fixed Gaussian with a CV of 603.8268. Based on the GWR model, the global variables identified were PPTB, BPNT, and IPM, whereas the remaining variables were local. The MGWR-SAR method was found to be the best model for predicting the percentage of poor population, with an AIC = 448.9645, RMSE = 1.9075, and  = 75.23%.
Co-Authors Budi Susetyo I Made Sumertajaya Sembiring, Febryna