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Syahputra, Suria Alam
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Automotive Engineering Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering

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Clustering of Deleted Binjai City Government Asset Data Using the K-Means Algorithm Lestari, Chintiya Wahyuni Indah; Buaton, Relita; Syahputra, Suria Alam
Journal of Engineering, Technology and Computing (JETCom) Vol. 4 No. 2 (2025): Journal of Engineering, Tecnology and Computing (JETCom)
Publisher : Yayasan Bina Internusa Mabarindo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63893/jetcom.v4i2.306

Abstract

Regional assets are a crucial component in managing local government resources. However, their management often encounters various obastacles, such as the accumulation of unproductive assets and the difficulty of mapping assets that must be written off. The Binjai City Government, through the Regional Finance, Revenue, and Asset Management Agency (BPKPAD), is obliigated to manage its assets, including those that have reached the end of their useful life. However, without in-depth analysis, the management of written-off asset data can become disorganized, potentially hampering the transparency and efficiency of overall asset management. To address these issuses, this study applied the K-Means algorithm with 3, 4, and 5 clusters as a method for grouping deleted asset data. The data characteristics used included the type of item, year of acquisition, and method of acquisition. The test results showed that grouping with 3 clusters resulted in a cluster variance value of 208,6587, indicating a high level of data diversity. With 4 clusters, the cluster variance value decreased to 110,5156, resulting in a better and more compact grouping. Meanwhile, testing with 5 clusters provided the most optimal results, with a cluster variance value of 79,2477. This shows that the use of 5 clusters can minimize the spread of data within each cluster, resulting in higher similarity between data compared to 3 and 4 clusters. Therefore, the application of the K-Means Algorithm to deleted Binjai City Government asset data can assist the data analysis and grouping process, where the best results were obtained in testing with 5 clusters. Keywords: Asset Data, K-Means Clustering, MATLAB, Binjai City Government
A decision support system for determining Corporate Social Responsibility (CSR) fund recipients using the dead method (Case study: PT. Ukindo Blankahan Estate) Andika, Rio; Maulita, Yani; Syahputra, Suria Alam
Journal of Engineering, Technology and Computing (JETCom) Vol. 4 No. 2 (2025): Journal of Engineering, Tecnology and Computing (JETCom)
Publisher : Yayasan Bina Internusa Mabarindo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63893/jetcom.v4i2.312

Abstract

Corporate Social Responsibility (CSR) is a form of a company’s social responsibility to the community. PT. UKINDO Blankahan Estate faces challenges in determining CSR aid recipients because the selection process is still conducted manually, making it prone to subjectivity.This study aims to develop a Decision Support System (DSS) for determining CSR aid recipients using the Multi-Attribute Utility Theory (MAUT) method with five main criteria: location distance, proposal type, amount of funds, impact scale, and proposal value. The system was designed as a web application using PHP and MySQL.The results show that the system can automatically perform normalization, weighting, and ranking processes. In the case study of proposals for the Maulid Nabi commemoration, the highest rank was obtained by Desa Blankahan with a score of 0.71460, while the lowest rank was obtained by PRM Al-Amin with a score of 0.19170. This system proves effective in assisting the company to select CSR aid recipients more objectively, efficiently, and accurately.
Co-Authors Andika, Rio Buaton, Relita Lestari, Chintiya Wahyuni Indah Yani Maulita