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All Journal International Journal of Advances in Applied Sciences Journal of Applied Data Sciences
Elquthb, Jundi Nourfateha
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Environmental Science Materials Science & Nanotechnology Mathematics Physics

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Mapping of Warehouse Radio Frequency Identification Research: A Bibliometric Analysis Auliana, Windi; Qurtubi, Qurtubi; Setiawan, Danang; Elquthb, Jundi Nourfateha
Journal of Applied Data Sciences Vol 5, No 2: MAY 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i2.246

Abstract

Warehouses play a vital role as an intermediary between entities in supply chains, connecting upstream and downstream entities. Implementing Radio Frequency Identification (RFID) technology as a warehouse management system enables data collection with more accuracy, speed, and reliability. This research was motivated by the limited bibliometric perspective and visualization of research on warehouse RFID. The use of bibliometric methods aimed to find basic patterns and an overview of the direction of research related to warehouse RFID. This research utilized the Publish or Perish and VOSviewer tools for analyzing purposes. This study comprised 172 Scopus journals that provide an extensive overview of the developmental progress over 2003-2023 period. Bibliometric visualization was conducted to investigate the outcomes from later publications connected to warehouse RFID. The visualization displayed the leading publishers, yearly patterns, prominent publication titles, top authors, most referenced papers, distribution of keywords, most influential journals, and areas of research that require more investigation.
Designing Planograms for Retail Shelves: A Visual Merchandising Approach Using Apriori Algorithm and K-Means Clustering of Customer Preferences Elquthb, Jundi Nourfateha; Mansur, Agus
Journal of Applied Data Sciences Vol 6, No 1: JANUARY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i1.375

Abstract

The development of retail store businesses in Indonesia is widespread across various regions, positively impacting the increase in shopping activities among the community. Alongside the intense competition among retailers, fundamental issues are found regarding the shopping experience of customers who feel uncomfortable and dissatisfied with retail services. Customers are an essential aspect that needs attention, and their desires must be fulfilled to maintain the existence of a retail store. This study attempts to implement the concept of visual merchandising to enhance service quality through the planogram method aimed at improving the visual arrangement of sales shelfs. Regarding the layout of retail stores, shelf arrangement plays a significant role visually in influencing and attracting customer attention while shopping. In this study, two data mining techniques are used. The first method is association rule mining using the Apriori algorithm, which reveals the association rules formed between two or more product items, utilizing a total of 6,325 customer transaction records. The results indicate 12 rules formed based on product categories and 17 rules based on product sub-categories. The second technique is k-means clustering, which is used to identify differences in customer preferences in retail stores based on several variables regarding customers using 104 data customers. In practice, both the apriori and k-means algorithms face challenges, especially when handling large and complex data. Differences in preferences were found among the clusters, particularly regarding product arrangement on the sales shelf, such as grouping products by brand and price. A two-dimensional planogram design was developed to integrate the results from the previous stages. This design considers the availability and specifications of the shelves in the retail store. A planogram adjusted to customer purchasing patterns and preferences is expected to provide good service and positively impact store operations and stock management.
Bibliometric study of association rule-market basket analysis Yanti, Roaida; Elquthb, Jundi Nourfateha; Rachmadewi, Ira Promasanti; Qurtubi, Qurtubi
International Journal of Advances in Applied Sciences Vol 13, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v13.i2.pp282-290

Abstract

Association rule-market basket analysis (AR-MBA) is a data mining technique for finding distinguished relationship patterns from a collection of items. The application of AR-MBA is also increasingly widespread, starting from retail and hotels to hospitals. So, bibliometrics related to AR-MBA needs to be done to reveal what research opportunities can be later carried out by reviewing and analyzing publications about AR-MBA. 91 bibliographies in 1 decade from 2012-2022 were collected using Harzing's Publish or Perish (PoP). VOSviewer is also employed to map authorship and publication topic trends. This paper is innovative because it identifies trends and future research directions in data mining, specifically in association with AR-MBA. The findings show publication productivity, top authors, types of publications, annual topic trends within a decade, term distribution, most cited and most influential articles, and research gaps that can be opportunities for further research.
Co-Authors Agus Mansur, Agus Danang Setiawan Qurtubi Qurtubi, Qurtubi Rachmadewi, Ira Promasanti Windi Auliana Yanti, Roaida