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All Journal Indonesian Journal of Statistics and Its Applications Jurnal Litbang Sukowati : Media Penelitian dan Pengembangan
Andrew Donda Munthe
Badan Pusat Statistik (BPS)
Humanities Computer Science & IT Economics, Econometrics & Finance Education Environmental Science Mathematics Social Sciences Other

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PENGGEROMBOLAN DESA/KELURAHAN BERDASARKAN INDIKATOR KEMISKINAN DENGAN MENERAPKAN ALGORITMA TSC DAN K-PROTOTYPES Andrew Donda Munthe; I Made Sumertajaya; Utami Dyah Syafitri
Indonesian Journal of Statistics and Applications Vol 2 No 2 (2018)
Publisher : Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v2i2.169

Abstract

Statistic Indonesia (BPS) noted that in 2014 there were 3.270 villages in Nusa Tenggara Timur Province. Most of them have a high percentage of poverty. Therefore, the village clustering based on poverty indicators is very important. The clustering algorithm that can be used on large data size and with mixed variables are Two Step Cluster (TSC) and K-Prototypes. The purpose of this research is to compare of TSC and K-Prototypes algorithm for village clustering in Nusa Tenggara Timur Province based on poverty indicators. The data were taken from 2014 village potential data (PODES 2014) collected by BPS. The best selection criteria for the cluster is the minimum ratio between variance within groups and variance between groups. The result showed that the best clustering algorithm was TSC which had the smallest ratio (2.6963). The best clustering showed that villages in Nusa Tenggara Timur Province divided into six groups with different characteristics.
PENERAPAN CLUSTERING TIME SERIES UNTUK MENGGEROMBOLKAN PROVINSI DI INDONESIA BERDASARKAN NILAI PRODUKSI PADI Andrew Donda Munthe
Jurnal Litbang Sukowati : Media Penelitian dan Pengembangan Vol 2 No 2 (2019): Vol. 2 No. 2, Mei 2019
Publisher : Badan Perencanaan Pembangunan Daerah, Penelitian dan Pengembangan Kabupaten Sragen

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (559.572 KB) | DOI: 10.32630/sukowati.v2i2.61

Abstract

In fulfilling national food needs, rice plants are an important main commodity. The characteristics of various regions that differ from one another, greatly affect the production of rice produced. The purpose of this research is to cluster provinces in Indonesia by applying clustering time series analysis. The application of clustering time series analysis is based on rice production data for each province in Indonesia in the period 1968 – 2015. Clustering time series analysis with hierarchical and non-hierarchical methods results in the distribution of the same cluster members in the 3 optimal groups. On average, from the 3 clusters formed, the silhouette coefficient value is 0.64, so the clustering is categorized in Good Classification. Keyword: cluster, rice, silhouette, time series.
Co-Authors I Made Sumertajaya Utami Dyah Syafitri