Article Per Year (5 Year)
p-Index From 2020 - 2025
0.23
P-Index
This Author published in this journals
All Journal International Journal Software Engineering and Computer Science (IJSECS)
Setiawan, Dani Yuda Dwi
Unknown Affiliation
Computer Science & IT

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Grouping Production Goods Requirements Using the K-Means Clustering Method Setiawan, Dani Yuda Dwi; Hadikristanto, Wahyu; Edora
International Journal Software Engineering and Computer Science (IJSECS) Vol. 4 No. 2 (2024): AUGUST 2024
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v4i2.2863

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.
Co-Authors Edora Wahyu Hadikristanto