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All Journal Techno Nusa Mandiri : Journal of Computing and Information Technology Journal of Big Data Analytic and Artificial Intelligence
Syafiqoh, Ummi
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Computer Science & IT

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MULTILEVEL MODAL VALUE ANALYSIS FOR INTERPRETING CATEGORICAL K-MEDOIDS CLUSTERS DATA Fitriyanto, Rachmad; Syafiqoh, Ummi
Jurnal Techno Nusa Mandiri Vol. 21 No. 2 (2024): Techno Nusa Mandiri : Journal of Computing and Information Technology Period o
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/techno.v21i2.5796

Abstract

Consumer segmentation plays a crucial role for business owners in developing their enterprises. K-Medoid is commonly used for segmentation functions due to its low computational complexity. However, K-Medoid has limitations, such as the variability in cluster sizes across different iterations and the challenge of determining the optimal number of clusters. The Davies-Bouldin Index (DBI) is a metric used to evaluate the number of clusters by calculating the ratio between the within-cluster distance and the between-cluster distance. Most segmentation studies typically stop at the formation of clusters without further interpretation, particularly when dealing with categorical data. This study aims to modify the use of K-Medoid and propose a method for interpreting clusters with categorical data. The research began with questionnaire design and the data collecting from 100 respondents, which was normalized in the second stage. Clustering used K-Medoid with variations K values from K=2 to K=10, with each K value tested 10 times. The clustering results were evaluated using the DBI to select the optimal clusters. Data interpretation conducted using modal values, calculated as the ratio of the number of times a specific attribute variable was selected by respondents to the total number of data points in the cluster. Utilization and hierarchical visualization of modal values proposed in this study offer insights into the dominant variables within an attribute and also depict the relationships between attributes based on the ranking of modal values. These advantages facilitate business analysts in labeling clusters for developing consumer-driven business strategies.
Pengaturan Tata Letak Produk Fashion dengan FP-Growth untuk Peningkatan Penjualan UMKM Widyasari; Syafiqoh, Ummi; Rahmadania, Nova Tari; Hartono, Lies
Journal of Big Data Analytic and Artificial Intelligence Vol 8 No 1 (2025): JBIDAI Juni 2025
Publisher : STMIK PPKIA Tarakanita Rahmawati

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.71302/jbidai.v8i1.69

Abstract

The application of data mining techniques in the business sector contributes significantly to strategic decision-making. This study implements the FP-Growth algorithm to analyze consumer purchasing patterns at Zaynthary Store, a fashion retail shop located in Tarakan City. A total of 161 sales transaction records were collected and processed to identify frequent itemsets and association rules that represent relationships between products. The findings reveal that certain item combinations are frequently purchased together, such as {Blouse → Jeans} with a confidence value of 55%, suggesting that these items should be placed near each other in the store display layout. FP-Growth has proven effective in exploring customer purchase patterns and providing layout recommendations that can support increased sales. These results can serve as a strategic reference for designing data-driven store layouts in the fashion retail industry.
Co-Authors Fitriyanto, Rachmad Hartono, Lies Rahmadania, Nova Tari Widyasari