Published: 2024-08-01
Grouping Production Goods Requirements Using the K-Means Clustering Method
DOI: 10.35870/ijsecs.v4i2.2863
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Abstract
The inventory management of production goods presents several challenges, including difficulties in distinguishing between necessary and unnecessary items, leading to overstocking and manual data processing issues. Additionally, the risk of data loss can impede the data processing workflow. Data testing is conducted to evaluate the accuracy of calculations and the functionality of the applied methods. The objective is to optimize production results and inventory levels in warehouses. The K-means algorithm, known for its simplicity and effectiveness, is utilized to identify clusters within the data. The first cluster (C0) has centroids at (60.33, 70.33) and includes stock data categorized as having no potential. This cluster comprises 35 records. The second cluster (C1) has centroids at (10.94, 7.11) and includes stock data categorized as available, consisting of 15 records. Testing with the RapidMiner Studio application confirms similar insights, with each cluster containing members that are divided into two clusters, each having optimal centroid values of (60.33, 70.33) for Cluster 1 (C0) and (10.94, 7.14) for Cluster 2 (C1), and a Davies-Bouldin Index evaluation score of 0.666.
Keywords
Data Mining ; Stock Requirements ; Production Results ; Clustering ; K-Means
Article Information
This article has been peer-reviewed and published in the International Journal Software Engineering and Computer Science (IJSECS). The content is available under the terms of the Creative Commons Attribution 4.0 International License.
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Issue: Vol. 4 No. 2 (2024)
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Section: Articles
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Published: %750 %e, %2024
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License: CC BY 4.0
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Copyright: © 2024 Authors
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DOI: 10.35870/ijsecs.v4i2.2863
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Dani Yuda Dwi Setiawan
Informatics Engineering Study Program, Faculty of Engineering, Universitas Pelita Bangsa, Deli Serdang Regency, North Sumatra, Indonesia
Wahyu Hadikristanto
Informatics Engineering Study Program, Faculty of Engineering, Universitas Pelita Bangsa, Deli Serdang Regency, North Sumatra, Indonesia
Edora
Informatics Engineering Study Program, Faculty of Engineering, Universitas Pelita Bangsa, Deli Serdang Regency, North Sumatra, Indonesia
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