PENINGKATAN PERFORMA PENDETEKSIAN GPS FAKE DRIVER GO-JEK MENGGUNAKAN METODE ENSEMBLE LEARNING
Kata Kunci:
Fake GPS, Driver, Random Forest, Adaboost, XGBoost, deteksiAbstrak
Fake GPS membawa dampak negatif pada stabilitas sistem dan keadilan di antara pengguna layanan lainnya dan terjadinya pemalsuan GPS sendiri karena Sifatnya terbuka, dari struktur sinyal GPS membuatnya rentan terhadap spoofing (pemalsuan) GPS, yang dapat dilakukan secara terang-terangan atau terselubung menjadi masalah yang sangat berpengaruh terhadap aktifitas bisnis bahkan memberi dampak yang sangat merugikan bagi Gojek, Dataset yang digunakan adalah data aktivitas Go-Ride dan GO-Food. Dalam penelitian ini berbasis esembel learning menggunakan Algoritma Random Forest, adaboost dan XGBoost. Ansambel learning mampu meningkatkan performa pendeteksian FAKE GPS dengan sangat baik.
Referensi
Aberson, C. L. (2015). Statistical Power Analysis. Emerging Trends in the Social and Behavioral Sciences, 1–14. https://doi.org/10.1002/9781118900772.etrds0319
Alfaro, A. (2018). Package ‘ adabag .’
Benatti, N., & Central Bank, E. (2018). A machine learning approach to outlier detection and imputation of missing data. August, 30–31.
Boinee, P., Angelis, A. De, & Foresti, G. L. (2008). Meta Random Forests. World Academy of Science, Engineering and Technology, 18(6), 1148–1157.
Budiawan, T., Santoso, I., Zahra, A. A., Elektro, J. T., Teknik, F., & Diponegoro, U. (n.d.). Mobile tracking gps ( global positioning system ) melalui media sms ( short message service ).
Chang, Y. (2018). Fake GPS Defender : A Server-side Solution to Detect Fake GPS. c, 36–41.
Eckstrand, E., Hill, B., Vidrio, S., Wang, A., & Peck, R. (2021). R topics documented :
Forest, M. D. (2020). MultiClass Decision Forest Machine Learning Artificial Intelligence. 4(1), 1–7.
Gao, X., Wen, J., & Zhang, C. (2019). An Improved Random Forest Algorithm for Predicting Employee Turnover. Mathematical Problems in Engineering, 2019. https://doi.org/10.1155/2019/4140707
Hijmans, R. J., Williams, E., & Vennes, C. (2019). Spherical Trigonometry: Package “geosphere.” Cran, 1–45. http://www.movable-type.co.uk/scripts/latlong.html
Krotov, V. (2017). A Quick Introduction to R and RStudio ® Tutorial. November. https://doi.org/10.13140/RG.2.2.10401.92009
Mar’i, F., & Pangestu, G. (2021). Classification of Fake GPS in GOJEK Application using Logistic Regression.
Matt, A., Vaughan, D., & Dancho, M. M. (2020). Package ‘ anomalize .’ 1.
Meng, Q., Hsu, L. T., Xu, B., Luo, X., & El-Mowafy, A. (2019). A GPS spoofing generator using an open sourced vector tracking-based receiver. Sensors (Switzerland), 19(18). https://doi.org/10.3390/s19183993
Rodrigues, C. F. de S., Lima, F. J. C. de, & Barbosa, F. T. (2017). Importance of using basic statistics adequately in clinical research. Brazilian Journal of Anesthesiology (English Edition), 67(6), 619–625. https://doi.org/10.1016/j.bjane.2017.01.011
Sanjaya, J., Renata, E., Budiman, V. E., Anderson, F., & Ayub, M. (2020). Prediksi Kelalaian Pinjaman Bank Menggunakan Random Forest dan Adaptive Boosting. Jurnal Teknik Informatika Dan Sistem Informasi, 6(1), 50–60. https://doi.org/10.28932/jutisi.v6i1.2313
Setiawan, A. (2015). Pengantar Teori Probabilitas. June 2015, 215. https://www.researchgate.net/publication/301202850
Sharma, S., & Berger, P. D. (2017). a Predictive Workforce-Analytics Model for Voluntary Employee Turnover in the Banking/ Financial-Service Industry. Global Journal of Human Resource Management, 5(1), 47–59. www.eajournals.org
Shinde, P. P., Oza, K. S., & Kamat, R. K. (2017). Big data predictive analysis: Using R analytical tool. Proceedings of the International Conference on IoT in Social, Mobile, Analytics and Cloud, I-SMAC 2017, February, 839–842. https://doi.org/10.1109/I-SMAC.2017.8058297
statisctical power.pdf. (n.d.).
Sudiyarno, R., Setyanto, A., & Luthfi, E. T. (2021). Peningkatan Performa Pendeteksian Anomali Menggunakan Ensemble Learning dan Feature Selection. Creative Information Technology Journal, 7(1), 1. https://doi.org/10.24076/citec.2020v7i1.238
Szapkiw, R.-. (2013). Statistics Guide.
Triyanto, W. A. (2014). Association Rule Mining Untuk Penentuan Rekomendasi Promosi Produk. Journal SIMETRIS, Vol.5(No.2), 121–126.
Wang, X., & Lu, X. (2020). A host-based anomaly detection framework using XGBoost and LSTM for IoT devices. Wireless Communications and Mobile Computing, 2020. https://doi.org/10.1155/2020/8838571
Xu, R. (2013). Improvements to random forest methodology. Dissertation (Doctor of Philosophy) Iowa State University, 1–88. http://lib.dr.iastate.edu/etd/13052/
Yedida, R., Reddy, R., Vahi, R., Jana, R., GV, A., & Kulkarni, D. (2018). Employee Attrition Prediction. http://arxiv.org/abs/1806.10480
Zhang, S., Zhang, C., & Yang, Q. (2003). Data preparation for data mining. Applied Artificial Intelligence, 17(5–6), 375–381. https://doi.org/10.1080/713827180
Zhao, Y., Hryniewicki, M. K., Cheng, F., Fu, B., & Zhu, X. (2018). Employee turnover prediction with machine learning: A reliable approach. In Advances in Intelligent Systems and Computing (Vol. 869). Springer International Publishing. https://doi.org/10.1007/978-3-030-01057-7_56
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