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All Journal Journal of Mathematics, Computation and Statistics (JMATHCOS) Seminar Nasional Riset dan Teknologi (SEMNAS RISTEK)
Rio Aristiarto
Universitas Islam Indonesia
Civil Engineering, Building, Construction & Architecture Computer Science & IT Education Engineering Mathematics Other

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ANALISIS REGRESI ROBUST ESTIMASI GM PADA INDEKS KEPARAHAN KEMISKINAN PROVINSI-PROVINSI DI INDONESIA Aristiarto, Rio; Susanti, Yuliana; Susanto, Irwan
Semnas Ristek (Seminar Nasional Riset dan Inovasi Teknologi) Vol 7, No 1 (2023): SEMNAS RISTEK 2023
Publisher : Universitas Indraprasta PGRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/semnasristek.v7i1.6273

Abstract

Indeks keparahan kemiskinan merupakan indikator yang dapat digunakan untuk melihat perkembangan kemiskinan. Indeks ini memberikan gambaran mengenai penyebaran pengeluaran di antara penduduk miskin. Kemiskinan di Indonesia selama tiga tahun terakhir terjadi peningkatan. Penelitian ini bertujuan untuk mengetahui faktor-faktor yang mempengaruhi indeks keparahan kemiskinan provinsi-provinsi di Indonesia. Data indeks keparahan kemiskinan tahun 2021 mengandung pencilan di dalamnya sehingga asumsi normalitas tidak terpenuhi. Salah satu metode yang dapat digunakan dalam mengatasi pencilan yaitu analisis regresi robust. Estimasi yang digunakan adalah Generalized M (GM) yang merupakan pengembangan dari estimasi M ketika estimasi M kurang sensitif terhadap pencilan. Hasil penelitian menunjukkan bahwa faktor-faktor yang berpengaruh signifikan terhadap indeks keparahan kemiskinan provinsi-provinsi di Indonesia tahun 2021 adalah persentase penduduk miskin, indeks pembangunan manusia, dan proporsi rumah tangga dengan status rumah milik sendiri.
Block-Based K-Medoids Clustering For Profiling Regions in Central Java Based On Employment Indicators Rio Aristiarto; Kariyam Kariyam
Journal of Mathematics, Computations and Statistics Vol. 9 No. 3 (2026): Volume 09 Issue 03 (September 2026)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/hw6z1b10

Abstract

Employment is an important dimension of regional development because it reflects job opportunities, labour market participation, and social welfare. Single indicators cannot fully capture employment conditions, especially in regions with significant labour market disparities such as Central Java. This study groups regencies and cities in Central Java using multiple employment indicators and the Block-Based K-Medoids clustering algorithm. The study uses 2024 secondary data from the Central Java Statistics Agency, comprising the open unemployment rate, labour force participation rate, average monthly wage, proportion of youth aged 15–24 classified as NEET (not in education, employment or training), and number of industries. All variables were normalized with Min-Max normalization before clustering to ensure comparability across indicators with different measurement scales. The optimal number of clusters was determined using the Silhouette, Calinski-Harabasz, and Hartigan indices, all of which indicated that three clusters were optimal. The results show that Cluster 1 is characterized by relatively high unemployment and NEET rates and low wages, Cluster 2 represents regions with the most favourable employment conditions, and Cluster 3 has the highest average wages but the lowest labour force participation rate. These findings provide a useful basis for more targeted employment policy formulation in accordance with regional labour market characteristics, and they may support local governments in identifying priority areas for intervention and development planning.
Co-Authors Irwan Susanto Kariyam Kariyam Yuliana Susanti, Yuliana