Smart System for Diagnosing Motorcycle Damage Using Adaptive Neuro-Fuzzy Inference System for Future Transportation
- 10.2991/assehr.k.200204.008How to use a DOI?
- ANFIS, sound identification, motorcycle damage, mobile, android
Motorcycle is one of popular vehicles in Indonesia and predicted the use will continually increase. Indonesian Motorcycle Industries Association states that in 2016, the total sales of motorcycle nationally was 5.931.285 units. The more sophisticated the motorcycle machine, the more precise maintenance needed. One of the ways is damage diagnosing ability on motorcycle. The motorcycle damage may be diagnosed based on the motor sounds heard. The aims of the current research were (1) producing the smart system product of ANFIS (Adaptive Neuro Fuzzy Inference System) to diagnose the motorcycle damage through the motor sound. (2) Analyzing the accuracy level of smart system using ANFIS. Smart system application was built using prototype development model. ANFIS built for this application consisted of 5 inputs and 1 output, with 243 rules. The lowest RSME score was 1.1056e-08, obtained from the evaluation result of Data Training and Data Testing using MF Triangular (trimf) and Trapezoidal (trapmf), Hybrid optimization method, error tolerance 0.0001 and epoh was 30. The result of Alpha Test was 4.7 and including into Very Good category. Meanwhile, the total score obtained from the Beta Test was 4.6 and including into the Very Good category.
- © 2020, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Fatchul Arifin AU - Nur Hasanah AU - Dessy Irmawati AU - Zainal Arifin PY - 2020 DA - 2020/02/12 TI - Smart System for Diagnosing Motorcycle Damage Using Adaptive Neuro-Fuzzy Inference System for Future Transportation BT - Proceedings of the International Conference on Educational Research and Innovation (ICERI 2019) PB - Atlantis Press SP - 37 EP - 43 SN - 2352-5398 UR - https://doi.org/10.2991/assehr.k.200204.008 DO - 10.2991/assehr.k.200204.008 ID - Arifin2020 ER -