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Utilizing Random Forest with iForest-Based Outlier Detection and SMOTE to Detect Movement and Direction of RFID Tags

Author

Listed:
  • Ganjar Alfian

    (Department of Electrical Engineering and Informatics, Vocational College, Universitas Gadjah Mada, Yogyakarta 55281, Indonesia)

  • Muhammad Syafrudin

    (Department of Artificial Intelligence, Sejong University, Seoul 05006, Republic of Korea)

  • Norma Latif Fitriyani

    (Department of Data Science, Sejong University, Seoul 05006, Republic of Korea)

  • Sahirul Alam

    (Department of Electrical Engineering and Informatics, Vocational College, Universitas Gadjah Mada, Yogyakarta 55281, Indonesia)

  • Dinar Nugroho Pratomo

    (Department of Electrical Engineering and Informatics, Vocational College, Universitas Gadjah Mada, Yogyakarta 55281, Indonesia)

  • Lukman Subekti

    (Department of Electrical Engineering and Informatics, Vocational College, Universitas Gadjah Mada, Yogyakarta 55281, Indonesia)

  • Muhammad Qois Huzyan Octava

    (Department of Electrical Engineering and Informatics, Vocational College, Universitas Gadjah Mada, Yogyakarta 55281, Indonesia)

  • Ninis Dyah Yulianingsih

    (Department of Electrical Engineering and Informatics, Vocational College, Universitas Gadjah Mada, Yogyakarta 55281, Indonesia)

  • Fransiskus Tatas Dwi Atmaji

    (Industrial and System Engineering School, Telkom University, Bandung 40257, Indonesia)

  • Filip Benes

    (Department of Economics and Control Systems, Faculty of Mining and Geology, VSB—Technical University of Ostrava, 70800 Ostrava, Czech Republic)

Abstract

In recent years, radio frequency identification (RFID) technology has been utilized to monitor product movements within a supply chain in real time. By utilizing RFID technology, the products can be tracked automatically in real-time. However, the RFID cannot detect the movement and direction of the tag. This study investigates the performance of machine learning (ML) algorithms to detect the movement and direction of passive RFID tags. The dataset utilized in this study was created by considering a variety of conceivable tag motions and directions that may occur in actual warehouse settings, such as going inside and out of the gate, moving close to the gate, turning around, and static tags. The statistical features are derived from the received signal strength (RSS) and the timestamp of tags. Our proposed model combined Isolation Forest (iForest) outlier detection, Synthetic Minority Over Sampling Technique (SMOTE) and Random Forest (RF) has shown the highest accuracy up to 94.251% as compared to other ML models in detecting the movement and direction of RFID tags. In addition, we demonstrated the proposed classification model could be applied to a web-based monitoring system, so that tagged products that move in or out through a gate can be correctly identified. This study is expected to improve the RFID gate on detecting the status of products (being received or delivered) automatically.

Suggested Citation

  • Ganjar Alfian & Muhammad Syafrudin & Norma Latif Fitriyani & Sahirul Alam & Dinar Nugroho Pratomo & Lukman Subekti & Muhammad Qois Huzyan Octava & Ninis Dyah Yulianingsih & Fransiskus Tatas Dwi Atmaji, 2023. "Utilizing Random Forest with iForest-Based Outlier Detection and SMOTE to Detect Movement and Direction of RFID Tags," Future Internet, MDPI, vol. 15(3), pages 1-16, March.
    • Handle: RePEc:gam:jftint:v:15:y:2023:i:3:p:103-:d:1091017
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    References listed on IDEAS

    as
    1. Luiz Henrique A. Salazar & Valderi R. Q. Leithardt & Wemerson Delcio Parreira & Anita M. da Rocha Fernandes & Jorge Luis Victória Barbosa & Sérgio Duarte Correia, 2021. "Application of Machine Learning Techniques to Predict a Patient’s No-Show in the Healthcare Sector," Future Internet, MDPI, vol. 14(1), pages 1-21, December.
    2. Okiemute Roberts Omasheye & Samuel Azi & Joseph Isabona & Agbotiname Lucky Imoize & Chun-Ta Li & Cheng-Chi Lee, 2022. "Joint Random Forest and Particle Swarm Optimization for Predictive Pathloss Modeling of Wireless Signals from Cellular Networks," Future Internet, MDPI, vol. 14(12), pages 1-26, December.
    3. Sendova, Kristina P. & Yang, Chen & Zhang, Ruixi, 2018. "Dividend barrier strategy: Proceed with caution," Statistics & Probability Letters, Elsevier, vol. 137(C), pages 157-164.
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