## 3 Methodology
### iForest Outlier Detection
## 5 Conclusions and Future Works

The proposed RF model with iForest outlier detection and SMOTE was able to identify the movement and direction of tags using RFID technology. It was tested on various types of tag movement, including entering and exiting the gate, moving close to the gate, turning back, and remaining stationary. The results showed that the proposed model outperformed other models, such as MLP, LR, KNN, DT, NB, RF, XGBoost, and Adaboost, by up to 94.251%, 93.751%, 98.612%, 93.502%, and 93.332% in terms of accuracy, precision, specificity, recall, and f-score, respectively. The trained model can be used in an RFID gate to detect whether a product is entering or leaving and can also filter out false positive readings like static tags, tags turning back, and tags moving close to the gate. This information can be combined with product information and stored in a database. In the future, the performance of readers, tags, and IoT sensors will be evaluated under various conditions. It is also possible that the comparison with other classification models and the use of machine learning to identify miss-reads will be presented in the near future. Conceptualization, M.S. and N.L.F. ; methodology, G.A. ; software, G.A. ; validation, M.Q.H.O., N.D.Y. and F.T.D.A. ; formal analysis, M.S. ; investigation, N.L.F. ; resources, D.N.P. ; data curation, S.A.; writing--original draft preparation, G.A. ; writing--review and editing, S.A. and M.S. ; visualization, D.N.P. ; supervision, F.B. ; project administration, F.B. ; funding acquisition, L.S. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The data that support the findings of this study is publicly available at [[https://github.com/ganjar87/RFID_reading_dataset](https://github.com/ganjar87/RFID_reading_dataset)]([https://github.com/ganjar87/RFID_reading_dataset](https://github.com/ganjar87/RFID_reading_dataset)) (accessed on 16 January 2023). The authors declare no conflict of interest. ## References

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