Recognizing poverty pattern in Central Java using Biclustering Analysis

Bi-clustering is a clustering development that aims to group data simultaneously from two directions. The Iterative Signature Algorithm (ISA) is one of the bi-clustering algorithms that work iteratively to find the most correlated bi-cluster. Detecting economic and pandemic vulnerability using bi-cluster analysis is essential to get spatial patterns and an overview of Indonesia's economic and pandemic vulnerability characteristics. Bi-clustering using ISA requires setting the row and column threshold to form seventy combinations of thresholds. The best is chosen based on the average value of mean square residue to volume ratios. In addition, the similarity of the best bi-cluster with the other is also seen based on the Liu and Wang index values. The -1.0 row and -1.0 column threshold combinations were selected and produced the best bi-cluster with the smallest average value of mean square residue to volume ratios (0.00141). Based on Liu and Wang index values, it has more than 95% similarity with the combination of -1.0 row and -0.9 column thresholds and the -0.9 row and -1.0 column thresholds. These selected threshold combinations produce three bi-clusters with five types of spatial patterns and different characteristics because of the overlap between these three bi-clusters.

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“…) [18]. We select the optimal bicluster group with the smallest average value of MSR to volume ratios.…”

Section: B Research Stagesmentioning

confidence: 99%

Pattern Detection of Economic and Pandemic Vulnerability Index in Indonesia Using Bi-Cluster Analysis

Ningsih

Sumertajaya²,

Saefuddin³

2022

JUITA

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“…Rahmadatul and Rani [6] built a poverty index with a multidimensional approach for children under five in NTT Province, using factor analysis as a method for building the poverty index. On the other hand, Christiana, Irfani, and Sartono [7]…”

Section: Introductionmentioning

confidence: 99%

Implementation of Factor Analysis and BiClustering in Classifying Multidimensional Under-Five Poverty in East Nusa Tenggara

Wisdawati¹,

Nooraeni

Laksono

et al. 2022

Indonesian J. Comput. Cybern. Syst.

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Under-five poverty is a condition where the needs of toodlers are not met, resulting in undernourished children and unable to reach their full potential in the social sphere. East Nusa Tenggara is a province that still faces the biggest nutritional problems in Indonesia in 2019. This study aims to explain the variables that form toodlers multidimensional poverty in East Nusa Tenggara (ENT), form the Multidimensional Under-Five Poverty Index (MUPI), and compare the results of index formed with the results of bicluster. Data source used in this study is SUSENAS KOR 2019. The analytical method used is a factor and bicluster analysis. The results shows that 11 multidimensional poverty indicators form three dimensions, namely the Adequate Food and Beverage Facility Factor, Health Protection Factor, and Housing and Nutrition Factor, which is used to form the index. Based on regional grouping, there are five areas with low MUPI scores, fourteen areas with medium MUPI scores, and three areas with high MUPI scores. However, biclustering results show that there are two areas with low poverty category, thirteen regions with moderate poverty category, and seven regions with high poverty category. The result of the comparison of MUPI grouping with the biclustering method obtained different results based on the composition of the resulting area.

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“…However, [6] is the first to be applied to gene expression data. Biclustering works by looking for submatrixes and identifying rows and columns with similar characteristics [7]. This technique is done by simultaneously looking for a subset of rows with the same behavior along a subset of columns [8].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Bicluster Analysis Results in Capture Fisheries Using the BCBimax Algorithm

Wulandari¹,

Sumertajaya²,

Aidi³

2023

JUITA

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Biclustering is a simultaneous clustering technique by finding sub-matrixes that have the same similarity between rows and columns. One of the biclustering algorithms that is relatively fast and can be used as a reference for the comparison of several algorithms is the BCBimax algorithm. The BCBimax algorithm works by finding a sub-matrix containing element 1 of the formed binary data matrix. The selection of thresholds in the binarization process and the minimum combination of rows and columns are essential in finding the optimal bicluster. Capture fisheries have an important role in supporting sustainable growth in Indonesia, so information on the potential of fish species that have similarities in several provinces is needed in optimally mapping the potential. The BCBimax algorithm found 11 optimal biclusters in grouping capture fisheries data. The median of each variable is used as a threshold in the binarization process, and the minimum combination of row 2 and maximum column 2 is chosen to find the optimal bicluster result. The optimal average value of Mean Square Residual bicluster obtained is 0.405403 with the similarity of bicluster results (Liu and Wang index) which is different for each bicluster combination produced. All the bicluster results grouped the provinces and types of fish that had the same potential simultaneously.

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Recognizing poverty pattern in Central Java using Biclustering Analysis

Cited by 6 publications

References 5 publications

Pattern Detection of Economic and Pandemic Vulnerability Index in Indonesia Using Bi-Cluster Analysis

Pattern Detection of Economic and Pandemic Vulnerability Index in Indonesia Using Bi-Cluster Analysis

Implementation of Factor Analysis and BiClustering in Classifying Multidimensional Under-Five Poverty in East Nusa Tenggara

Evaluation of Bicluster Analysis Results in Capture Fisheries Using the BCBimax Algorithm

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