Smartphone Motion Sensor Data Processing for Driving Characteristics Classification
Driving behavior significantly influences road safety. Unsafe driving behaviors, such as driving under the influence, speeding, and using mobile phones, can lead to serious accidents and fatalities. This research aims to observe driving characteristics by utilizing smartphone motion sensor data. The...
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| Format: | Article |
| Language: | English |
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Universitas Gadjah Mada
2023-08-01
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| Series: | Jurnal Nasional Teknik Elektro dan Teknologi Informasi |
| Subjects: | |
| Online Access: | https://jurnal.ugm.ac.id/v3/JNTETI/article/view/6050 |
| _version_ | 1797688461807845376 |
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| author | Lisa Dinda Yunita Ema Utami Ainul Yaqin |
| author_facet | Lisa Dinda Yunita Ema Utami Ainul Yaqin |
| author_sort | Lisa Dinda Yunita |
| collection | DOAJ |
| description | Driving behavior significantly influences road safety. Unsafe driving behaviors, such as driving under the influence, speeding, and using mobile phones, can lead to serious accidents and fatalities. This research aims to observe driving characteristics by utilizing smartphone motion sensor data. The data collection method involved recording the driver’s smartphone motion sensor during trips. The data were then exported from the system for further processing. The main objective of this study is to process the data by creating a classification model with the best performance in handling smartphone motion sensor data. The results of this research are expected to be implementable models to address road safety issues in the future. Additionally, by utilizing driver characteristic detection technology, awareness of safe driving practices can be enhanced. The research methodology used data mining with machine learning classification modeling using random forest (RF), support vector machine (SVM), and decision tree (DT) methods. The test results indicate that the RF model performed the best with an accuracy of 91.22%. Furthermore, this study found that speed was the most influential factor in identifying safe or unsafe driving behavior. The developed classification model shows the potential to improve traffic management efficiency and contribute to safer transportation. By leveraging driver characteristic detection technology, it is hoped that awareness of safe driving practices will increase, leading to a safer road environment. |
| first_indexed | 2024-03-12T01:31:19Z |
| format | Article |
| id | doaj.art-775be20e2ddc451fbb2405dc358bb4c9 |
| institution | Directory Open Access Journal |
| issn | 2301-4156 2460-5719 |
| language | English |
| last_indexed | 2024-03-12T01:31:19Z |
| publishDate | 2023-08-01 |
| publisher | Universitas Gadjah Mada |
| record_format | Article |
| series | Jurnal Nasional Teknik Elektro dan Teknologi Informasi |
| spelling | doaj.art-775be20e2ddc451fbb2405dc358bb4c92023-09-12T03:32:29ZengUniversitas Gadjah MadaJurnal Nasional Teknik Elektro dan Teknologi Informasi2301-41562460-57192023-08-0112318118910.22146/jnteti.v12i3.60506050Smartphone Motion Sensor Data Processing for Driving Characteristics ClassificationLisa Dinda Yunita0Ema Utami1Ainul Yaqin2Universitas Amikom YogyakartaUniversitas Amikom YogyakartaUniversitas Amikom YogyakartaDriving behavior significantly influences road safety. Unsafe driving behaviors, such as driving under the influence, speeding, and using mobile phones, can lead to serious accidents and fatalities. This research aims to observe driving characteristics by utilizing smartphone motion sensor data. The data collection method involved recording the driver’s smartphone motion sensor during trips. The data were then exported from the system for further processing. The main objective of this study is to process the data by creating a classification model with the best performance in handling smartphone motion sensor data. The results of this research are expected to be implementable models to address road safety issues in the future. Additionally, by utilizing driver characteristic detection technology, awareness of safe driving practices can be enhanced. The research methodology used data mining with machine learning classification modeling using random forest (RF), support vector machine (SVM), and decision tree (DT) methods. The test results indicate that the RF model performed the best with an accuracy of 91.22%. Furthermore, this study found that speed was the most influential factor in identifying safe or unsafe driving behavior. The developed classification model shows the potential to improve traffic management efficiency and contribute to safer transportation. By leveraging driver characteristic detection technology, it is hoped that awareness of safe driving practices will increase, leading to a safer road environment.https://jurnal.ugm.ac.id/v3/JNTETI/article/view/6050data miningcrisp-dmdriving behaviormachine learningclassification |
| spellingShingle | Lisa Dinda Yunita Ema Utami Ainul Yaqin Smartphone Motion Sensor Data Processing for Driving Characteristics Classification Jurnal Nasional Teknik Elektro dan Teknologi Informasi data mining crisp-dm driving behavior machine learning classification |
| title | Smartphone Motion Sensor Data Processing for Driving Characteristics Classification |
| title_full | Smartphone Motion Sensor Data Processing for Driving Characteristics Classification |
| title_fullStr | Smartphone Motion Sensor Data Processing for Driving Characteristics Classification |
| title_full_unstemmed | Smartphone Motion Sensor Data Processing for Driving Characteristics Classification |
| title_short | Smartphone Motion Sensor Data Processing for Driving Characteristics Classification |
| title_sort | smartphone motion sensor data processing for driving characteristics classification |
| topic | data mining crisp-dm driving behavior machine learning classification |
| url | https://jurnal.ugm.ac.id/v3/JNTETI/article/view/6050 |
| work_keys_str_mv | AT lisadindayunita smartphonemotionsensordataprocessingfordrivingcharacteristicsclassification AT emautami smartphonemotionsensordataprocessingfordrivingcharacteristicsclassification AT ainulyaqin smartphonemotionsensordataprocessingfordrivingcharacteristicsclassification |