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Suharyanti , Nining
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Computer Science & IT

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Implementation of K-Means Clustering in Food Security by Regency in East Java Province in 2022 Tuslaela, Tuslaela; Rusdiansyah, Rusdiansyah; Supendar , Hendra; Suharyanti , Nining
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 1 (2024): Articles Research Volume 8 Issue 1, January 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i1.13169

Abstract

Food is the main need that society must fulfill. If food security is disrupted, it will have a negative impact on the nation's life. The agricultural sector has an important role in West Java Province. This province has a large area of agricultural land, so it has high potential to produce abundant agricultural production. However, knowing the adequate number of farmers is very important. Therefore, the implementation of K-Means Clustering can make a significant contribution to the East Java Provincial Agriculture Service in grouping farmers by district. To achieve optimal results, determining the best K value needs to be considered carefully. K-Means Cluster Analysis is a method of non-hierarchical Cluster Analysis that groups data into one or more groups. Data with the same characteristics is grouped into one cluster and data with different characteristics is grouped into another cluster. The data used in this research are land area and rice production in the Regency of East Java Province in 2022. Based on the results of research with the object of Food Security, it can be concluded that, the results of the analysis of the application of manual data mining calculations in Excel Software using the K-Means Clustering method, resulted in two types of clustering in the form of C0, namely the Highest Land Area and Production group with 4 districts: Jember Regency, Ngawi Regency, Bojonegoro Regency and Lamongan Regency, for C1 clustering, namely the Lowest Land Area and Production group with 25 districts in East Java Province
Cluster Analysis of Food Social Assistance in DKI Jakarta: K-Means Approach to Identify Expenditure Patterns and Beneficiaries Suharyanti , Nining; Rusdiansyah, Rusdiansyah; Supendar, Hendra; Tuslaela, Tuslaela
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 4 (2024): Article Research Volume 8 Issue 4, October 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i4.14095

Abstract

This study aims to evaluate the effectiveness of the K-Means algorithm in grouping social assistance recipients in DKI Jakarta based on various demographic and economic factors, such as income, number of family members, and living conditions. The main objective of this study is to optimize resource allocation in social assistance programs by identifying different recipient clusters, so that aid distribution becomes more targeted. In this study, the K-Means algorithm was used with an optimal number of clusters of 3, and produced an accuracy rate of 85%, indicating that this algorithm is effective in grouping large-scale and complex data. However, there are challenges related to the sensitivity of K-Means to outliers and data imbalances that affect the results of the analysis. The results also show that areas such as Central Jakarta and South Jakarta receive more social assistance compared to other areas such as North Jakarta and East Jakarta, reflecting differences in needs in various regions. These findings emphasize the importance of selecting the right variables, such as access to health facilities and economic conditions, in producing more accurate groupings. Overall, this study provides valuable insights into efforts to optimize the distribution of social assistance in DKI Jakarta and recommends further research to address the limitations that exist in the use of the K-Means algorithm, especially in the context of data that is imbalanced or has large variations.
Co-Authors Rusdiansyah Rusdiansyah Supendar , Hendra Tuslaela