Cluster Application with K-Means Algorithm on the Population of Trade and Accommodation Facilities in Indonesia

Munawar, Aang; Gendalasari, Gen Gen; Kurniawan, I Made Gede Ariestova; Purnomo, D; Ependi, Nur Haris; Rulinawaty, .; Indrawan, Muhammad Isa; Sadri, Muhammad

doi:10.1088/1742-6596/1933/1/012027

Cited by 3 publications

(2 citation statements)

References 12 publications

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“…Cluster analysis is an essential method in statistical data processing to perform data analysis [12]- [14]. Cluster analysis can be used to group objects or data into a group based on their similarity [15], [16]. There are two methods of grouping contained in cluster analysis, hierarchical and nonhierarchical clustering [17], [18].…”

Section: Introductionmentioning

confidence: 99%

Mapping the Provincial Food Security Conditions in Indonesia Using Cluster Ensemble-Based Mixed Data Clustering-Robust Clustering with Links (CEBMDC-ROCK)

Ratnasari¹,

Dani²

2023

Int. J. Adv. Sci. Eng. Inf. Technol.

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Problems related to food are indeed an issue that continues to be discussed by the government, both imports, self-sufficiency, the issue food security. Food security conditions have become one of the biggest problems in Indonesia, even though Indonesia is an agricultural country with abundant resources. The problem is not only the availability but also the affordability. It happens due to the social inequality between the rich and the poor, which means the rich can easily relish food. People with low incomes experience food insecurities. Thus, an appropriate strategy and policies can be done for each province in Indonesia to make it equal. Cluster analysis is used to map the provincial profile based on the condition of food security. However, the variable types in this research are numerical and categorical data, which makes general cluster analysis insufficient. This study used the Cluster Ensemble Based Mixed Data Clustering-Robust Clustering Using Links (CEBMDC-ROCK) method to cluster provinces in Indonesia based on food security conditions. The analysis process starts with numerical clustering data using Agglomerative Hierarchical Clustering (AHC) and then with categorical data using Robust Clustering Using Links (ROCK). The result shows that the province in Indonesia is divided into five groups based on the quality of food security, which is from very low to excellent. Based on the clustering results, which provinces need special attention from the government regarding food security can be seen.

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Section: Introductionmentioning

confidence: 99%

Mapping the Provincial Food Security Conditions in Indonesia Using Cluster Ensemble-Based Mixed Data Clustering-Robust Clustering with Links (CEBMDC-ROCK)

Ratnasari¹,

Dani²

2023

Int. J. Adv. Sci. Eng. Inf. Technol.

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“…Considering that the predicted result data is not very large, the result will also be imported into MySQL database, and users can access it through the Web interface. If the amount of analyzed data is large, you can also use HBase heavyweight database[10].…”

mentioning

confidence: 99%

Application of k-means algorithm in the design of smart health data analysis platform

Hu,

2023

Third International Conference on Advanced Algorithms and Neural Networks (AANN 2023)

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In order to improve the function of smart health data analysis platform, this paper puts forward the application research of k-means algorithm in the design of smart health data analysis platform. A smart health service platform based on data mining is designed, and the platform is divided into three parts: data acquisition module, data storage module and data mining module. After that, the function and implementation method of each module are introduced in detail. Among them, the data acquisition module realizes the acquisition of data of Bluetooth electronic blood pressure meter; The data storage module adopts the combination of traditional database storage and cloud storage to solve the storage problem of massive health data; The data mining module uses the improved k-means algorithm and GM (1,1) grey prediction algorithm to analyze and predict the preprocessed data.

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Smart service quality in hospitality – A quantitative assessment using MCDM and clustering methods

Ayvaz-Çavdaroğlu,

Iyanna,

Foster

2024

International Journal of Hospitality Management

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Cluster Application with K-Means Algorithm on the Population of Trade and Accommodation Facilities in Indonesia

Cited by 3 publications

References 12 publications

Mapping the Provincial Food Security Conditions in Indonesia Using Cluster Ensemble-Based Mixed Data Clustering-Robust Clustering with Links (CEBMDC-ROCK)

Mapping the Provincial Food Security Conditions in Indonesia Using Cluster Ensemble-Based Mixed Data Clustering-Robust Clustering with Links (CEBMDC-ROCK)

Application of k-means algorithm in the design of smart health data analysis platform

Smart service quality in hospitality – A quantitative assessment using MCDM and clustering methods

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