Latent Class Cluster for Clustering Villages Based on Socio-economic Indicators in 2018

A. Riyanto; H. Kuswanto; D. D. Prastyo

doi:10.1088/1742-6596/1821/1/012041

Latent Class Cluster for Clustering Villages Based on Socio-economic Indicators in 2018

A. Riyanto^*
, H. Kuswanto
, D. D. Prastyo

^*Corresponding author for this work

Institut Teknologi Sepuluh Nopember
BPS-Statistics Indonesia

Research output: Contribution to journal › Conference article › peer-review

2 Citations (Scopus)

Abstract

Latent class analysis (LCA) is a statistical method used to classify units into unobserved (latent) variable classes, so-called clusters. In this article, we introduce LCA to demonstrate its use for village groupings. We applied LCA on data processed from the census conducted by BPS-Statistics Indonesia, namely the village potential (Podes) data 2018. The Podes data is used to extract the socio-economic characteristics of a village. The empirical results of the LCA application show that the villages in the Banyumas Regency are grouped into three classes/clusters.

Original language	English
Article number	012041
Journal	Journal of Physics: Conference Series
Volume	1821
Issue number	1
DOIs	https://doi.org/10.1088/1742-6596/1821/1/012041
Publication status	Published - 29 Mar 2021
Event	6th International Conference on Mathematics: Pure, Applied and Computation, ICOMPAC 2020 - Surabaya, Virtual, Indonesia Duration: 24 Oct 2020 → …

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title = "Latent Class Cluster for Clustering Villages Based on Socio-economic Indicators in 2018",

abstract = "Latent class analysis (LCA) is a statistical method used to classify units into unobserved (latent) variable classes, so-called clusters. In this article, we introduce LCA to demonstrate its use for village groupings. We applied LCA on data processed from the census conducted by BPS-Statistics Indonesia, namely the village potential (Podes) data 2018. The Podes data is used to extract the socio-economic characteristics of a village. The empirical results of the LCA application show that the villages in the Banyumas Regency are grouped into three classes/clusters.",

author = "A. Riyanto and H. Kuswanto and Prastyo, \{D. D.\}",

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T1 - Latent Class Cluster for Clustering Villages Based on Socio-economic Indicators in 2018

AU - Riyanto, A.

AU - Kuswanto, H.

AU - Prastyo, D. D.

N1 - Publisher Copyright: © Published under licence by IOP Publishing Ltd.

PY - 2021/3/29

Y1 - 2021/3/29

N2 - Latent class analysis (LCA) is a statistical method used to classify units into unobserved (latent) variable classes, so-called clusters. In this article, we introduce LCA to demonstrate its use for village groupings. We applied LCA on data processed from the census conducted by BPS-Statistics Indonesia, namely the village potential (Podes) data 2018. The Podes data is used to extract the socio-economic characteristics of a village. The empirical results of the LCA application show that the villages in the Banyumas Regency are grouped into three classes/clusters.

AB - Latent class analysis (LCA) is a statistical method used to classify units into unobserved (latent) variable classes, so-called clusters. In this article, we introduce LCA to demonstrate its use for village groupings. We applied LCA on data processed from the census conducted by BPS-Statistics Indonesia, namely the village potential (Podes) data 2018. The Podes data is used to extract the socio-economic characteristics of a village. The empirical results of the LCA application show that the villages in the Banyumas Regency are grouped into three classes/clusters.

UR - https://www.scopus.com/pages/publications/85103909788

U2 - 10.1088/1742-6596/1821/1/012041

DO - 10.1088/1742-6596/1821/1/012041

M3 - Conference article

AN - SCOPUS:85103909788

SN - 1742-6588

VL - 1821

JO - Journal of Physics: Conference Series

JF - Journal of Physics: Conference Series

IS - 1

M1 - 012041

T2 - 6th International Conference on Mathematics: Pure, Applied and Computation, ICOMPAC 2020

Y2 - 24 October 2020

ER -

Latent Class Cluster for Clustering Villages Based on Socio-economic Indicators in 2018

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