Wan Nor Munirah Ariffin
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Preferred name
Wan Nor Munirah Ariffin
Official Name
Wan Nor Munirah, Ariffin
Alternative Name
Munirah, Wan Nor
Ariffin, Wan Nor Munirah
Ariffin, W. N.M
Main Affiliation
Scopus Author ID
56442390400
Researcher ID
FMB-9023-2022

Research Output
Now showing 1 - 2 of 2
  • Publication
    Bioeconomy sustainability integrating circular economy principles with big data and IoT for sustainable farming in agriculture 4.0
    ( 2024-07)
    Ahmad Tajudin Baharin
    ;
    Noormaizatul Akmar Ishak
    ;
    Nurulisma Hj Ismail
    ;
    Mohd Fisol Osman
    ;
    Evawaynie Valquis Md. Isa
    ;
    Wan Nor Munirah Ariffin
    This concept paper explores the synergy between Bioeconomy sustainability and advanced technologies, specifically the integration of circular economy principles with big data and the Internet of Things (IoT), in the context of sustainable farming within Agriculture 4.0 in Malaysia. Despite limited understanding, the study aims to unveil the potential benefits of this integration and assess the current state of technology adoption, bioeconomic practices, and sustainable farming in Malaysia. Challenges faced by Malaysian farmers, such as awareness gaps and resistance to change, are identified, and strategies, including targeted education and financial incentives, are proposed to overcome these barriers. While acknowledging potential limitations in universality due to data access constraints and the dynamic nature of technology and agriculture, the study emphasizes the importance of integrating these innovative approaches to propel Malaysian agriculture toward sustainability within the Agriculture 4.0 framework.
      34  5
  • Publication
    Product pairing selection for promotion using partitioning method
    (Semarak Ilmu Publishing, 2023)
    Wan Nor Munirah Ariffin
    ;
    Raveena Subramaniam
    ;
    Erni Puspanantasari Putri
    ;
    Muhammad Shahar Jusoh
    ;
    Muhamad Hafiz Masran
    ;
    Mohamad Nur Khairul Hafizi Rohani
    ;
    Yussof Hussin
    ;
    Noormaizatul Akmar Ishak
    ;
    Emy Aizat Azimi
    ;
    Siti Sharina Mohd Shukri
    Slow-moving product is harmful to the business. The slow-moving products take up space and tie up the company’s capital and leave the company with fewer funds to invest in its business. Several factors can cause this issue. There are several methods ranging from statistics to heuristic methods for a company to identify slow-moving inventory but all of them rely on data. In this paper, a partitioning technique from the graph network is proposed to partition the inventories or products into a few clusters. It can help the company to identify what group does the product belongs to and at the same time suggest to the company which product can be paired up or bundled up together to clear up aging and slow-moving products. The partition technique is proposed, and the algorithm is coded using the Visual C++ programming language. The simulation results show that the proposed method can partition the task graph onto smaller subgraphs. The subgraphs called cluster consists of the nodes or products with similar purchase volume (the strong connection between the two nodes). Implementing the partitioning technique could help the companies or managers select the appropriate product to be paired together when doing the promotion.
      1  15