Abstract
Design for Business Analysis of Product Sales Data is a combination of theory and real-world experience to educate on how to choose a dynamic mix of procedures and techniques to do business successfully and efficiently. This study focuses on local retail company named Syarikat Daya Maju Bintang (DMB). The main issue that this retailer face is that some items are not sold out. In other words, some products are purchased less frequently by customers. So overcome these issues, business analysis approach is suggested. The main aim of this analysis is to identify the categories of products and sales for DMB. Besides that, a prediction model using product sales data technique and to visualize the result based on the proposed model. Cross Industry Standard Process for Data Mining, or CRISP-DM, is an industry-recognized method of directing data mining activities. These CRISP-DM include modeling, evaluation, and deployment as well as business understanding, data preparation, and data understanding in solving those issues. As a result, with is design business analysis, DMB may can improve its company performance and compete with other retail stores.
Metadata
Item Type: | Article |
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Creators: | Creators Email / ID Num. Mohamad Suzuki, Muhamad Sayfiq 2020821934@student.uitm.edu.my Mohd Mydin, Azlina azlin143@uitm.edu.my Mohamed Yusoff, Syarifah Adilah syarifah.adilah@uitm.edu.my Wan Mohammad, Wan Anisha wannan122@uitm.edu.my Johan, Elly Johana ellyjohana@uitm.edu.my |
Contributors: | Contribution Name Email / ID Num. Advisor Kadar, Rozita UNSPECIFIED Chief Editor Othman, Jamal UNSPECIFIED |
Subjects: | H Social Sciences > HF Commerce > Business H Social Sciences > HF Commerce > Business > General works, treatises, and textbooks |
Divisions: | Universiti Teknologi MARA, Pulau Pinang > Permatang Pauh Campus |
Journal or Publication Title: | Enhancing Innovations In e-Learning For Future Preparation |
ISSN: | 978-967-25608-8-3 |
Volume: | 5 |
Page Range: | pp. 62-67 |
Keywords: | Business Analysis, Business Intelligence, AI, Dashboards, Data Mining |
Date: | April 2023 |
URI: | https://ir.uitm.edu.my/id/eprint/83490 |