IMPLEMENTATION OF FUZZY LOGIC ON SPEED PREDICTION ON INCLUDING ROADS

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Published Mar 1, 2023
https://doi.org/10.31943/teknokom.v6i1.107
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Vol. 6 No. 1 (2023): TEKNOKOM

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Alghi Sawaludin
Universitas Singaperbangsa Karawang
Rahmat Hidayat
Universitas Singaperbangsa Karawang
Reni Rahmadewi
Universitas Singaperbangsa Karawang

Abstract

Going through a road with a high slope has a risk of an accident due to a miscalculation by the driver. Therefore, detecting the speed limit to pass it is the solution. The aim of this research is to create a precise and minimal error system that predicts the uphill speed that must be used by the driver. By using fuzzy logic, of course you can predict what speed should be used. For tilt detection, a gyroscope is used and speed detection is being used using a GPS sensor. For the fuzzy inference system itself, it uses the Takagi Sugeno-Kang, method with a linear and constant output. In addition, the calculation of the crisp value is carried out using the manual calculation method and using a simulation application, namely MATLAB. The results of subsequent calculations are compared to calculate the precision value of the system in detecting uphill speed. The calculation uses MAPE. So the results of this study are speed detection systems that must be used on inclines that predict precisely. The implication of this research is that driving safety on high-slope roads can be improved and accidents that occur can be minimized. In addition, the driver can estimate the speed that must be used.

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Creative Commons License

This work is licensed under a Creative Commons Attribution-NoDerivatives 4.0 International License.

How to Cite
Sawaludin, A. ., Hidayat, R., & Rahmadewi, R. (2023). IMPLEMENTATION OF FUZZY LOGIC ON SPEED PREDICTION ON INCLUDING ROADS. TEKNOKOM, 6(1), 29–35. https://doi.org/10.31943/teknokom.v6i1.107

References

  1. V. A. Dihni, "Angka Kecelakaan Lalu Lintas di Indonesia Meningkat di 2021, Tertinggi dari Kecelakaan Motor," Kadata Media Network, 22 March 2022. [Online]. Available: https://databoks.katadata.co.id/datapublish/2022/03/24/angka-kecelakaan-lalu-lintas-di-indonesia-meningkat-di-2021-tertinggi-dari-kecelakaan-motor. [Accessed 08 September 2022].
  2. A. H. Mukti, "Detik-detik Kecelakaan di Tuntang Semarang Libatkan 3 Truk dan 1 Mobil hingga Ringsek Parah," AyoSemarang.com, 1 April 2022. [Online]. Available: https://www.ayosemarang.com/semarang-raya/pr-773107314/detik-detik-kecelakaan-di-tuntang-semarang-libatkan-3-truk-dan-1-mobil-hingga-ringsek-parah. [Accessed 8 September 2022].
  3. H. Ahmadi, M. Gholamzadeh, L. Shahmoradi , M. Nilashi and P. Rashvand, "Disease Diagnosis Using Fuzzy Logic Methods: A Systematic and Meta-Analysis Review," Computer Methods and Programs in Biomedicine, pp. 145-172, 2018.
  4. M. Bulgakov, S. Shuklynov, A. Uzhva, D. Leontiev, V. Verbitskiy, M. Amelin and O. Volska, "Mathematical model of the vehicle initial rectilinear motion during moving uphill," in IOP Conference Series: Materials Science and Engineering, 2020.
  5. Z. Feng, M. Yang, W. Kumfer, W. Zhang, Y. Du and H. Bai, "Effect of longitudinal slope of urban underpass tunnels on drivers’ heart rate and speed: A study based on a real vehicle experiment," Tunnelling and Underground Space Technology, pp. 525 - 533, 2018.
  6. M. Gallab, H. Bouloiz, Y. L. Alaoui and M. Tkiouat, "Risk Assessment of Maintenance activities using Fuzzy Logic," Procedia Computer Science, pp. 226 - 235, 2019.
  7. K. Iqbal, M. A. Khan, S. Abbas, Z. Hasan and A. Fatima, "Intelligent Transportation System (ITS) for Smart-Cities using Mamdani Fuzzy Inference System," International Journal of Advanced Computer Science and Application, vol. 9, pp. 94 - 105, 2018.
  8. H. Iskandar, "Geometrik Jalan Pada Terowongan," Kementrian Pekerjaan Umum dan Perumahan Rakyat.
  9. R. W. Pratiwi, "Implementasi Logika Fuzzy Sugeno Dalam Menganalisis Ketersediaan Beras Saat Pademi COVID-19 di Perum Bulog Sumatera Utara," Skripsi S.Mat Universitas Islam Negeri Sumatera Utara, 2021.
  10. M. J. Ritonga, "Sistem Peringatan Jarak Aman Sepeda Motor Menggunakan Sensor Ultrasonik Dengan Metode Fuzzy Logic Berbasis Mikrokontroller," Skripsi Sarjana Komputer Universitas Islam Negeri Sumatera Utara, 2019.
  11. A. Wanto and A. P. Windarto, "Analisis Prediksi Indeks Harga Konsumen Berdasarkan Kelompok Kesehatan Dengan Menggunakan Metode Backpropagation," Sinkron-Jurnal dan Penelitian Teknik Informatika, vol. 2, pp. 37 - 45, 2017.
  12. R. Shreedharkumar and N. K. Verma, "Developing Deep Fuzzy Network with Takagi Sugeno Fuzzy Inference System," IEEE, 2017.
  13. P. Harliana and R. Rahim, "Comparative Analys of Membership Function on Mamdani Fuzzy Inference System for Decision Making," International Conference on Information and Communication Technology, vol. 930, pp. 1 - 6, 2017.
  14. F. Cavallro, "A Takagi Sugeno Fuzzy Inference System for Developing a Sustainability Index of Biomas," Sustainability, vol. 7, pp. 12359 -12372, 2015.
  15. N. Sabri, S. A. Aljunid, M. S. Salim, R. B. Badlishah, R. Kamaruddin and M. F. Abd Malek, "Fuzzy Inference System: Short Review and Design," Internatioanl Review of Automatic Control, vol. 6, pp. 441 - 450, 2013.
  16. Ummul Khair et al, “Forecasting Error Calculation with Mean Absolute Deviation and Mean Absolute Percentage Error,” International Conference on Information and Communication Technology, pp. 1 – 6, 2017

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