Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal Jurnal Ilmiah Informatika dan Komputer
Aliya Dwi Ardiyanti
Universitas Muhammadiyah Bengkulu
Computer Science & IT

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

Found 1 Documents
Search

 1 
Optimizing iPhone Spare Parts Inventory Using K-Medoid Clustering Naurah Atikah Nurpadhilah; Aliya Dwi Ardiyanti; M. Aufa Rafiqi; Nur Ayu Siti Hardianti; Yusa Virginiawan Guntara
Jurnal Ilmiah Informatika dan Komputer Vol. 3 No. 1 (2026): Juni 2026 (In Progress)
Publisher : CV.RIZANIA MEDIA PRATAMA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69533/informatech.volume3number1.555

Abstract

MR. GADGET store in Bengkulu faces significant challenges in managing iPhone spare parts inventory due to a manual recording system. This study proposes a data science-based solution using the K-Medoid Clustering algorithm to group data based on characteristic similarity. Utilizing a dataset of 471 products, this study compares K-Medoid with conventional partitioning methods (like K-Means), demonstrating its superior robustness against outliers by using actual data points as cluster centers. The clustering quality is evaluated using the Silhouette Score and Davies-Bouldin Index (DBI), yielding best-performing results with a Silhouette Score of 0.681 and a DBI of 0.798. The algorithm generates three main clusters: Fast-Moving, Medium-Moving, and Slow-Moving. The system's functionality is validated through Black Box Testing. The results indicate that this approach provides more accurate procurement recommendations, optimizes inventory turnover, and reduces the risk of inventory imbalance, offering a practical data-driven framework for local retail businesses.
Co-Authors M. Aufa Rafiqi Naurah Atikah Nurpadhilah Nur Ayu Siti Hardianti Yusa Virginiawan Guntara