Implementasi Moving Average dan Kalman Filter pada Wireless Odometer untuk Informasi Service Kendaraan Bermotor
DOI:
https://doi.org/10.30865/json.v4i1.4899Keywords:
Kalman Filter, Mileage measurement, Moving Average Filter, Wireless OdometerAbstract
Regular maintenance is one step to keep your car looking good, but many people still underestimate the importance of regular maintenance. A tool called Wireless Odometer has been created which can notify vehicle owners through the application if the vehicle mileage has entered the recommended criteria for periodic servicing. Based on previous research, Wireless Odometer still has weaknesses. sensor readings on this tool still have an error of 13.2% so it is still less accurate if applied to measure mileage on vehicles. It is necessary to have a digital filter that must be added to the sensor readings so that the sensor is resistant to noise that occurs due to mechanical or electrical. In this research, we implement the Moving Average Filter and Kalman Filter methods for sensor readings on the Wireless Odometer. After several tests were carried out, it was found that the percentage of reading errors when filtered reached 0.80% using the Kalman filter method and 1.81% using the Moving Average Filter method. It can be concluded that the filter that is suitable for use on this Wireless Odometer is the kalman filter.
References
A. Purwadi, “Electric Vehicle Research Implementation in Indonesia,†pp. 44–51, 2020.
A. López-Arquillos, J. C. Rubio-Romero, M. Súarez-Cebador, and M. del C. Pardo-Ferreira, “Comparative risk assessment of vehicle maintenance activities: Hybrid, battery electric, and hydrogen fuel cell cars,†Int. J. Ind. Ergon., vol. 47, pp. 53–60, 2015, doi: 10.1016/j.ergon.2015.02.005.
K. Chen, M. Lu, X. Fan, M. Wei, and J. Wu, “Road condition monitoring using on-board three-axis accelerometer and GPS sensor,†Proc. 2011 6th Int. ICST Conf. Commun. Netw. China, CHINACOM 2011, no. 2009, pp. 1032–1037, 2011, doi: 10.1109/ChinaCom.2011.6158308.
A. Wahyudi, “Rancang Bangun Informasi Service Pada Kendaraan Bermotor Dengan Wireless Odometer Menggunakan Sensor Accelerometer.,†Institut Teknologi Adhi Tama Surabaya, 2021.
W. S. Pambudi, I. Suhendra, J. Teknik, E. Universitas, and I. Batam, “Perbaikan Respon Output Menggunakan Implementasi Kalman Filter Pada Simulasi,†no. October 2015, pp. 141–150, 2015.
Zulharbi, Firdaus, and Y. Antonisfia, “Implementasi Moving Average Filter Pada Mikrokontroler Sebagai Peredam Noise Sensor Piezo Elektrik Untuk Mendeteksi Gelombang Seismik (Gempa Bumi),†Pros. Semnastek, no. PROSIDING SEMNASTEK 2014, pp. 1–8, 2014, [Online]. Available: http://jurnal.ftumj.ac.id/index.php/semnastek/article/view/300.
S. Azaz, : “IMPLEMENTASI SENSOR ACCELEROMETER MMA 7361 SEBAGAI PENGAMAN PADA SEPEDA MOTOR MATIC UNTUK MEMINIMALISIR DAMPAK KECELAKAAN,†Tek. Elektro Univ. Brawijaya, pp. 1–9, 2014.
J. T. Elektro and U. D. Semarang, “Kalibrasi Sensor Multigain Akselerometer Dengan Acuan Percepatan Gravitasi Bumi,†Transmisi, vol. 17, no. 3, pp. 122–128, 2015.
M. Seminar and T. Akhir, “Aplikasi Sensor.â€
A. Susanto, S. P. Hadi, and W. Widada, “Accelerometer Mma7260Q Pada Ketiga Sumbu,†J. Tek. Elektro, vol. 1, no. 11, pp. 16–22, 2009.
M. N. Qomarudin, “[Terjemahan] Pengantar Kalman Filter Diskrit,†no. February, 2014, [Online]. Available: https://www.researchgate.net/publication/272740539_Terjemahan_Pengantar_Kalman_Filter_Diskrit.
