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Model sistem pendukung keputusan menggunakan FIS Mamdani untuk penentuan tekanan udara ban

Decision support system model using FIS Mamdani for determining tire pressure

Department of Information System, Universitas Teknokrat Indonesia. Jl. Zainal Abidin No. 9-11 Kedaton Bandar Lampung 12940, Indonesia

Received: 9 Jun 2020; Revised: 18 Oct 2020; Accepted: 21 Oct 2020; Available online: 26 Nov 2020; Published: 31 Jan 2021.
Open Access Copyright (c) 2021 The Authors. Published by Department of Computer Engineering, Universitas Diponegoro
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Citation Format:
Tire air pressure is very important in driving, providing comfort, safety, and efficiency in fuel consumption. This study aims to create a model that can determine the measurement of tire air pressure. The model was developed based on the Mamdani FIS with five input parameters: load weight (load capacity), weather, mileage, rim diameter, and tire thickness. Mamdani inference generates front and rear tire air pressure. The calculation of tire pressure using the system was compared with a manual that only considers the vehicle load. This comparison shows the difference in the mean size of 1.24% for the front tire pressure and 2.17% for the rear tire. The system can provide recommendations for tire air pressure by considering several parameters in addition to vehicle load.
Keywords: FIS Mamdani model; fuzzy logic; tire pressure; decision support system
Funding: Universitas Teknokrat Indonesia

Article Metrics:

  1. M. M. Muttaqin, F. Kristianta, and H. Arbiantara, “Pengaruh tekanan udara (inflation preassure) pada tipe radial ply terhadap rolling resistance,” Jurnal Rotor, vol. 8, no. 2, pp. 26-28, 2015
  2. Jenner, “Motorcycle industry council tire guide,” in Guide Motorcycle Industry Council Tire, 2nd ed., vol. 2, no. 1, Motorcycle, Ed. Irvine, California: Motorcycle Industry Council, 2011, pp. 1–20
  3. E. N. Setyawan, S. Winardi, and K. Eko, “Pendeteksi tekanan udara ban pada kendaraan bermotor untuk safety riding,” in Seminar Nasional Informatika, Surabaya, Indonesia, Sept. 2019, pp. 68–73
  4. J. C. Lee and M. S. Liou, “Accurate calculation of the pressure and temperature of water, steam, and ice: Formulation for CFD,” Journal of Mechanical Science and Technology, vol. 24, no. 11, pp. 2333–2340, 2010. doi: 10.1007/s12206-010-0906-2
  5. S. K. Purwar, “Automatic tire inflation systems,” International Research Journal of Engineering and Technology, vol. 4, no. 4, pp. 2384–2387, 2017
  6. Z. Chen, Z. Xie, and J. Zhang, “Measurement of Vehicle-Bridge-Interaction force using dynamic tire pressure monitoring,” Mechanical Systems and Signal Processing, vol. 104, pp. 370–383, 2018. doi: 10.1016/j.ymssp.2017.11.001
  7. M. I. Pasaribu, G. Putra, F. A. Anugerah, and Junaidi, “Mengukur tekanan udara pada ban secara otomatis dengan kecepatan anemometer,” Jurnal Teknologi, vol. 15, no. 2, pp. 1-10, 2018
  8. -, Manual Book Avanza Indonesia, 6th ed. Jakarta: Toyota Indonesia, 2020
  9. -, Tire maintenance , safety and warranty manual, Bridgestone, 2015
  10. Y. Zhou, Y. Chai, Y. Lin, and K. Wang, “An application of multi-sensor information fusion in tire pressure monitoring system,” in International Conference on Intelligent Systems and Knowledge Engineering, Hangzhou, China, Nov. 2010, pp. 187–190. doi: 10.1109/ISKE.2010.5680820
  11. D. Garcia-Pozuelo, O. Olatunbosun, J. Yunta, X. Yang, and V. Diaz, “A novel strain-based method to estimate tire conditions using fuzzy logic for intelligent tires,” Sensors (Switzerland), vol. 17, no. 2, pp. 1–16, 2017. doi: 10.3390/s17020350
  12. H. Taghavifar and A. Mardani, “Fuzzy logic system based prediction effort: A case study on the effects of tire parameters on contact area and contact pressure,” Applied Soft Computing, vol. 14, Part C, pp. 390–396, 2014. doi: 10.1016/j.asoc.2013.10.005
  13. A. Wantoro, “Komparasi metode perhitungan klasik dengan logika fuzzy (Mamdani dan Sugeno ) pada perhitungan pemilihan mahasiswa terbaik,” Jurnal Pendidikan Teknologi dan Kejuruan, vol. 15, no. 1, pp. 42-49, 2018. doi: 10.23887/jptk-undiksha.v15i1.13000
  14. A. Elfasakhany, “Tire pressure checking framework: A review study,” Reliability Engineering and Resilience, vol. 1, no. 1, pp. 12–28, 2019. doi: 10.22115/RER.2019.86929
  15. C. Mathworks, Fuzzy Logic Toolbox, 2nd ed. US: The MathWorks, Inc, 2010
  16. V. Žuraulis, G. Garbinčius, P. Skačkauskas, and O. Prentkovskis, “Experimental study of winter tyre usage according to tread depth and temperature in vehicle braking performance,” Iranian Journal of Science and Technology, Transactions of Mechanical Engineerin, vol. 44, no. 1, pp. 83–91, 2020. doi: 10.1007/s40997-018-0243-0
  17. L. A. Zadeh, “The concept of a linguistic variable and its application to approximate reasoning—I,” Information Sciences, vol. 8, no. 3, pp. 199–249, 1975. doi: 10.1016/0020-0255(75)90036-5
  18. S. K. Dewi, Aplikasi logika fuzzy untuk pendukung keputusan, Yogyakarta: Graha Ilmu, 2010

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