ONLINE CAR MONITORING SYSTEM WITH IBM BLUEMIX AND DECISION OF ATTITUDE DRIVER USING FUZZY TSUKAMOTO
Internet usage in the community has become a common thing, not like before the internet must be accessed through a computer, now with a cell phone internet can be accessed easily. One development of the Internet is the Internet of Things (IoT). Internet of Things itself is a concept that aims to ext...
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| Format: | Theses |
| Language: | Indonesia |
| Online Access: | https://digilib.itb.ac.id/gdl/view/81390 |
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| Institution: | Institut Teknologi Bandung |
| Language: | Indonesia |
| Summary: | Internet usage in the community has become a common thing, not like before the internet must be accessed through a computer, now with a cell phone internet can be accessed easily. One development of the Internet is the Internet of Things (IoT). Internet of Things itself is a concept that aims to extend the benefits of Internet connectivity that connected continuously. Based on the utilization of the concept of the Internet of Things, appeared an idea to create a car online monitoring system and can analyze the percentage of driver attitude such as good or bad using fuzzy method.
This study use a device called On Board Diagnostics 2 (OBD2). OBD2 functions take raw data from the car and then data will be sent to the server IBM Bluemix intermediary with smart phone using bluetooth connection. Raw data that located on the server IBM Bluemix, then processed by a program that previously planted on IBM Bluemix. Programs produce the output of the average speed, rpm, engine temperature, air fuel ratio, the accelerator and the percentage of drivers' attitude are good and bad. Percentage good and bad of drivers' attitude are calculated using fuzzy function where input from a variable speed and rpm, meanwhile output are the percentage good and bad of driver's attitude. Both Speed and rpm have each three parameters, namely high, medium and low, meanwhile the output are percentage of the driver’s attitude are good and bad. Based on 3 parameters from each input variable, gained 9 rules on fuzzy logic. To calculate the final results, using formula Center Average Defuzzyfier.
The output data form recent data location of car and calculate average value of engine temperature, speed, rpm, accelerator and the air fuel ratio, successfully implemented in the program using query of database. The output percentage of good value or bad value driver are tested using four test scenarios. Based on four test scenarios show, for the first test scenario, driver good value percentage is 66.59% and the percentage of driver bad value is 33.41%. The results of the second test scenario, percentage driver good value is 88.04% and the percentage of driver bad value is 11.96%. The results of the third test scenario, percentage driver good value is 82.31% and the percentage of driver bad value is 17.69%. Results of fourth test scenarios, percentage driver good vallue is 87.22% and the percentage of driver bad value is 12.78%.
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