Bicluster CC Algoritm Analysis to Identify Patterns of Food Insecurity in Indonesia

Novidianto, Raditya; Irfani, Rini

doi:10.20956/jmsk.v17i2.12057

Cited by 3 publications

(4 citation statements)

References 7 publications

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“…represents the variation associated with the interaction between rows and columns in the bi-cluster [13], [14]. According to [15], the quality of a bi-cluster will be better as the residual value decreases and/or the volume (rows × columns) of the bi-cluster increases.…”

Section: B Research Stages 1)mentioning

confidence: 99%

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Bicluster Analysis of Cheng and Church's Algorithm to Identify Patterns of People's Welfare in Indonesia

Marifni,

Sumertajaya,

Syafitri

2023

JUITA

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Biclustering is a method of grouping numerical data where rows and columns are grouped simultaneously. The Cheng and Church (CC) algorithm is one of the bi-clustering algorithms that try to find the maximum bi-cluster with a high similarity value, called MSR (Mean Square Residue). The association of rows and columns is called a bi-cluster if the MSR is lower than a predetermined threshold value (delta). Detection of people's welfare in Indonesia using Bi-Clustering is essential to get an overview of the characteristics of people's interest in each province in Indonesia. Bi-Clustering using the CC algorithm requires a threshold value (delta) determined by finding the MSR value of the actual data. The threshold value (delta) must be smaller than the MSR of the actual data. This study's threshold values are 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, and 0.8. After evaluating the optimum delta by considering the MSR value and the bi-cluster formed, the optimum delta is obtained as 0.1, with the number of bi-cluster included as 4.

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Section: B Research Stages 1)mentioning

confidence: 99%

“…Ref. [13] also conducted a bicluster analysis study using the Cheng and Church algorithm to identify patterns of food insecurity in Indonesia. This study produced five biclusters with the best parameter tuning based on the smallest MSR/V (0.017), namely δ=0.1.…”

Section: Introductionmentioning

confidence: 99%

Bicluster Analysis of Cheng and Church's Algorithm to Identify Patterns of People's Welfare in Indonesia

Marifni,

Sumertajaya,

Syafitri

2023

JUITA

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“…Various studies on biclustering have been performed, but most of them just focus on bioinformatics such as gene expression data (Babichev et al, 2018;Cao et al, 2023;Huang et al, 2020). Recently, biclustering can be implemented in various fields (Silva et al, 2022;Candelise et al, 2021;Kaban et al, 2019;Lin et al, 2019;Novidianto & Irfani, 2020;Wang et al, 2016). Furthermore, biclustering involves a diverse range of algorithms.…”

Section: A Introductionmentioning

confidence: 99%

Pattern Recognition of Food Security in Indonesia Using Biclustering Plaid Model

Hikmah,

Sumertajaya,

Afendi

2023

JTAM

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Biclustering come in various algorithms, selecting the most suitable biclustering algorithm can be a challenging task. The performance of algorithms can vary significantly depending on the specific data characteristics. The Plaid model is one of popular biclustering algorithms, has gained recognition for its efficiency and versatility across various applications, including food security. Indonesia deals with complex food security challenges. The nation's unique geographic and socioeconomic diversity demands region-specific food security solutions. Identifying province-specific food security patterns is crucial for effective policymaking and resource allocation, ultimately promoting food sufficiency and stability at the regional level. This study assesses the performance of the Plaid model in identifying food security patterns at the provincial level in Indonesia. To optimize biclusters, we explore various parameter tuning scenarios (the choice of model, the number of layers, and the threshold value for row and column releases). The selection criteria are based on the change ratio of the initial matrix's mean square residue to the mean square residue of the Plaid model, the average mean square residue, and the number of biclusters. The constant column model was selected with a mean square residue change ratio of 0.52, an average mean square plaid model residue of 4.81, and it generates 6 overlapping biclusters. The results show each bicluster has unique characteristics. Notably, Bicluster 1 that consist of 2 provinces, exhibits the lowest food security levels, marked by variables X1, X2, X4, and X7. Furthermore, the variables X1, X4, and X7 consistently appear across several biclusters. This highlights the importance of prioritizing these three variables to improve the food security status of the regions.

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“…[5] and [6] applied the CC algorithm to poverty cases in East Java (38 regions × 15 variables) and Central Java (35 regions × 15 variables). In addition, [7] applies the CC algorithm to food insecurity cases with a data matrix measuring 34 regions × 12 variables. The CC algorithm application in this previous study only used the intra-bicluster evaluation function.…”

Section: Introductionmentioning

confidence: 99%

Biclustering Application in Indonesian Economic and Pandemic Vulnerability

Ningsih

Sumertajaya

Saefuddin

2022

BAREKENG: J. Math. & App.

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Biclustering is an analytical tool to group data from two dimensions simultaneously. The analysis was first introduced by Hartigan (1972) and applied by Cheng and Church (2000) to the gene expression matrix. The Cheng and Church (CC) algorithm is a popular biclustering algorithm and has been widely applied outside the field of biological data in recent years. This algorithm application in economic and Covid-19 pandemic vulnerability cases is exciting and essential to do in order to get an overview of the spatial pattern and characteristics of the bicluster of economic and COVID-19 pandemic vulnerability in Indonesia. This study uses secondary data from some ministries. Forming a bicluster using the CC algorithm requires determining the delta threshold so that several types of delta thresholds are formed to choose the best (optimum) using the evaluation of the average value of mean square residue (MSR) to volume ratios. The similarity of the optimum bi-cluster with the other is also seen based on the Liu and Wang index values. The 0.01 delta threshold is chosen as the optimum threshold because it produces the smallest average value of MSR to volume ratios (0.00032). Based on Liu and Wang Index values, the optimum threshold has a similarity level below 50% with other types of delta thresholds, so the threshold is the best unique threshold. The optimum threshold resulted in six biclusters (six spatial patterns). Most regions in Indonesia (11 provinces) tend to have low economic and COVID-19 pandemic vulnerability in the first spatial pattern characteristic variables.

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Bicluster CC Algoritm Analysis to Identify Patterns of Food Insecurity in Indonesia

Cited by 3 publications

References 7 publications

Bicluster Analysis of Cheng and Church's Algorithm to Identify Patterns of People's Welfare in Indonesia

Bicluster Analysis of Cheng and Church's Algorithm to Identify Patterns of People's Welfare in Indonesia

Pattern Recognition of Food Security in Indonesia Using Biclustering Plaid Model

Biclustering Application in Indonesian Economic and Pandemic Vulnerability

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