Open AccessRecognizing poverty pattern in Central Java using Biclustering Analysis
Author(s) -
Candy Putri,
Rachmatika Irfani,
Bagus Sartono
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1863/1/012068
Subject(s) - java , poverty , biclustering , dimension (graph theory) , quality (philosophy) , computer science , cluster analysis , cluster (spacecraft) , data mining , artificial intelligence , economics , economic growth , mathematics , cure data clustering algorithm , philosophy , correlation clustering , epistemology , pure mathematics , programming language
Poverty is a complex and multidimensional problem and becoming a development priority. In analyzing the pattern of poverty in a region, one of the statistical procedures that is usually used is the cluster analysis. However, it does not consider the different levels of performance by region in different characteristics at a particular time. In this study, an alternative approach, namely Cheng and Church’s biclustering algorithm, was used to simultaneously identify the poverty pattern in Central Java by region and poverty dimension variables. Using this algorithm, we found two biclusters with different characteristics. The first bicluster represents the general condition of poverty in Central Java, but they are better in labor and housing quality indicators. While the second bicluster is poorer in some indicators of labor, education, and housing quality than Central Java.