Penerapan Latent Class Cluster Analysis (LCCA) Pada Pengelompokan Kabupaten/Kota Di Provinsi Papua Barat Berdasarkan Indikator Kesejahteraan Rakyat

Onti Sundari (Jurusan Matematika, Fakultas Matematika dan Ilmu Pengetahuan Alam, UNIPA)
Surianto Bataradewa (Jurusan Matematika, Fakultas Matematika dan Ilmu Pengetahuan Alam, UNIPA)
Esther Ria Matulessy (Jurusan Matematika, Fakultas Matematika dan Ilmu Pengetahuan Alam, UNIPA)

Jurnal Natural
Vol. 17 No. 2 (2021): JURNAL NATURAL

Latent class cluster analysis (LCCA) is one of the techniques for latent variable. Clustering of objects is done by using probability opportunities and based on statistical models. The latent variable used in this study is the welfare of the people of West Papua which is described by 15 indicator variables of public welfare in 2019. The purpose of this study is to determine the number of groups based on the best models and characteristics formed and thus obtain an early picture of the welfare conditions in the Regency/City in West Papua Province. The research was conducted based on the smallest Bayesian Information Criteria (BIC) value of 1836.9406, the best model was three clusters. The results showed that Manokwari Regency, Sorong Selatan Regency, Sorong Regency, Raja Ampat Regency, Tambrauw Regency, Manokwari Selatan Regency, and Pegunungan Arfak Regency were in first cluster; Fakfak Regency, Kaimana Regency, Maybrat Regency and Teluk Wondama Regency are in second cluster and Bintuni Regency and Sorong City are in third cluster.