Clustering Provinces in Indonesia based on Community Welfare Indicators

Dini, Sekti Kartika; Fauzan, Achmad

doi:10.20885/eksakta.vol1.iss1.art9

Cited by 13 publications

(9 citation statements)

References 11 publications

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“…The number of clusters obtained with k -means clustering was the same as that determined with Ward’s method. All the assumptions of k -means clustering (i.e., the lack of collinearity among the grouping variables and the lack of outliers) were also verified [ 70 , 74 , 77 , 78 , 79 ].…”

Section: Methodsmentioning

confidence: 96%

Estimation of Dairy Cow Survival in the First Three Lactations for Different Culling Reasons Using the Kaplan–Meier Method

Grzesiak

Adamczyk

Zaborski

et al. 2022

Animals

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The aims of the study were: (i) to compare survival curves for cows culled for different reasons over three successive lactations using the Kaplan–Meier estimator; (ii) to determine the effects of breeding documentation parameters on cow survival; (iii) to investigate the similarity between culling categories. The survival times for a subset of 347,939 Holstein-Friesian cows culled between 2017 and 2018 in Poland were expressed in months from calving to culling or the end of lactation. The survival tables were constructed for each culling category and lactation number. The survival curves were also compared. The main culling categories were reproductive disorders—40%, udder diseases—13 to 15%, and locomotor system diseases—above 10%. The survival curves for cows from individual culling categories had similar shapes. A low probability of survival curves for metabolic and digestive system diseases and respiratory diseases was observed in each of the three lactations. The contagious disease category was almost non-existent in the first lactation. The greatest influence on the relative culling risk was exerted by age at first calving, lactation length, calving interval, production subindex, breeding value for longevity, temperament, and average daily milk yield. A more accurate method of determining culling reasons would be required.

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Section: Methodsmentioning

confidence: 96%

Estimation of Dairy Cow Survival in the First Three Lactations for Different Culling Reasons Using the Kaplan–Meier Method

Grzesiak

Adamczyk

Zaborski

et al. 2022

Animals

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show abstract

“…As all of the variables incorporated were numerical variables (measured in interval or ratio scale), the K-Means clustering method was selected. As a method that was extensively administered due to its simplicity, K-Means clustering had been employed in profiling conditions utilizing variables such as the risk of spreading infectious disease (19), community welfare indicators (20), and crime rate (21)(22)(23). This study then became the first to administer the WASH availability variables derived from school sanitation indicators encompassing all regencies/cities in Indonesia.…”

Section: Methodsmentioning

confidence: 99%

Profiling the Inequality of School Water, Sanitation, and Hygiene Facilities Among Indonesian Regions Using Cluster Analysis

Unggul

Ainy²,

Jannah³

2023

JKL

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Introduction: Humans rely heavily on Water, Sanitation, and Hygiene (WASH) facilities. Goal 6 of the Sustainable Development Goals (SDGs) emphasizes ensuring communities possess universal access to clean water and sanitation. Because WASH is tremendously crucial in schools, the objective of this study is to provide a comprehensive profile of regional inequalities based on the availability of WASH indicators through cluster analysis. Methods: This study administered cross-sectional data from 514 regencies/cities in Indonesia with three variables, i.e. percentage of access to water, sanitation, and hygiene at public and private elementary schools. The profiling was performed by conducting K-means clustering method. Results and Discussion: Public and private schools were examined separately as the p-value in the difference test was less than 0.05. In accordance with the silhouette plot, the optimal number of clusters was two for each category. For the public-school category, the number of regencies/cities in Cluster 1 was 380 regencies/cities and 134 regencies/cities were in Cluster 2. For the private school category, Cluster 1 incorporated 418 regencies/cities and Cluster 2 merely encompassed 96 regencies/cities. Conclusion: Two clusters for each type of school had been established with Cluster 1 consisting of areas with high availability of WASH facilities while areas in Cluster 2 possessed a relatively low percentage in the three WASH indicators. There were 66 regencies/cities, generally located in eastern Indonesian provinces, grouped in Cluster 2 for both types of schools.

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“…This study uses the Euclidean Distance formula. This calculation calculates the quantitative value of the proximity measure, which can produce the distance from the object to the centroid [14]. The following is the Euclidean Distance formula which is used to calculate the distance between objects and the center of the cluster (4).…”

Section: Methodsmentioning

confidence: 99%

Pillar Algorithm in K-Means Method for Identification Health Human Resources Availability Profile in Central Java

2021

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Based on data from the Ministry of Health, the distribution ratio between health workers and patients in Indonesia is still not equal distributed. It influenced by the distribution of health human resources that are not in accordance with the ideal needs of health services. This results need to identify the profile of the availability of health human resources in Indonesia. In this study, an approach will be implemented to identify the profile of health human resources availability using K-Means Clustering with a combination of pillar algorithms in optimizing the selection of the initial cluster centroid. Chi-square analysis is used to determine the disparity in the needs of health human resources with the conditions of the availability of health human resources in the Central Java region. The data collection method used in this research is the observation method, while the scientific method used in this research is the K-Means Clustering method. The results showed that the application has been generated can dynamically determine the health human resource cluster based on the disparity category of health human resource availability in the Central Java region. In addition, the labeling of the Pillar K-Means cluster based on the Chi-square test has a high degree of accuracy, namely 80%.

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Clustering Provinces in Indonesia based on Community Welfare Indicators

Cited by 13 publications

References 11 publications

Estimation of Dairy Cow Survival in the First Three Lactations for Different Culling Reasons Using the Kaplan–Meier Method

Estimation of Dairy Cow Survival in the First Three Lactations for Different Culling Reasons Using the Kaplan–Meier Method

Profiling the Inequality of School Water, Sanitation, and Hygiene Facilities Among Indonesian Regions Using Cluster Analysis

Pillar Algorithm in K-Means Method for Identification Health Human Resources Availability Profile in Central Java

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