Cited 16 time in
Traceability system using IoT and forecasting model for food supply chain
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Alfian, G. | - |
| dc.contributor.author | Syafrudin, M. | - |
| dc.contributor.author | Fitriyani, N.L. | - |
| dc.contributor.author | Rhee, J. | - |
| dc.contributor.author | Ma'arif, M.R. | - |
| dc.contributor.author | Riadi, I. | - |
| dc.date.accessioned | 2023-04-28T00:41:12Z | - |
| dc.date.available | 2023-04-28T00:41:12Z | - |
| dc.date.issued | 2020-11-08 | - |
| dc.identifier.uri | https://scholarworks.dongguk.edu/handle/sw.dongguk/7122 | - |
| dc.description.abstract | Nowadays, customer's health awareness is of extreme significance. Food can become contaminated at any point during production, preparation and distribution. Therefore, it is of key importance for the perishable food supply chain to monitor the food quality and safety. Traceability system offers complete food information and therefore, it guarantees food quality and safety. The current study proposes IoT-based traceability system that utilized RFID and raspberry pi based sensors. The RFID reader is utilized to track and trace the product, while the raspberry pi is used to measure temperature and humidity during storage and transportation. In addition, the machine learning based forecasting model is utilized to predict future temperature, so that early warning can be presented by system if the predicted temperature exceeding the normal range. The results displayed that compared to the traditional methods, the proposed system is capable of tracking products as well as predicting sensor data accurately and effectively. © 2020 IEEE. | - |
| dc.format.extent | 5 | - |
| dc.language | 영어 | - |
| dc.language.iso | ENG | - |
| dc.publisher | Institute of Electrical and Electronics Engineers Inc. | - |
| dc.title | Traceability system using IoT and forecasting model for food supply chain | - |
| dc.type | Article | - |
| dc.identifier.doi | 10.1109/DASA51403.2020.9317011 | - |
| dc.identifier.scopusid | 2-s2.0-85100533279 | - |
| dc.identifier.bibliographicCitation | 2020 International Conference on Decision Aid Sciences and Application, DASA 2020, pp 903 - 907 | - |
| dc.citation.title | 2020 International Conference on Decision Aid Sciences and Application, DASA 2020 | - |
| dc.citation.startPage | 903 | - |
| dc.citation.endPage | 907 | - |
| dc.type.docType | Conference Paper | - |
| dc.description.isOpenAccess | N | - |
| dc.description.journalRegisteredClass | scopus | - |
| dc.subject.keywordAuthor | artificial neural network | - |
| dc.subject.keywordAuthor | forecasting | - |
| dc.subject.keywordAuthor | IoT | - |
| dc.subject.keywordAuthor | RFID | - |
| dc.subject.keywordAuthor | sensor | - |
| dc.subject.keywordAuthor | traceability | - |
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