PATH SMOOTHING WITH SUPPORT VECTOR REGRESSION
<p align="justify">One of moving objects problems is the incomplete data acquired by geo-tracking technology, this phenomenon can be found in aircraft tracking with tracking loss reach out 5 minutes, it needs path smoothing process to complete the data. General solution of path smoot...
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| 主要作者: | |
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| 格式: | Theses |
| 語言: | Indonesia |
| 在線閱讀: | https://digilib.itb.ac.id/gdl/view/21821 |
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| 機構: | Institut Teknologi Bandung |
| 語言: | Indonesia |
| 總結: | <p align="justify">One of moving objects problems is the incomplete data acquired by geo-tracking technology, this phenomenon can be found in aircraft tracking with tracking loss reach out 5 minutes, it needs path smoothing process to complete the data. General solution of path smoothing is using physics of motion, while this research performs path smoothing process using machine learning algorithm that is support vector regression. Support Vector Regression will predict at intervals of data lost from aircraft tracking data. The prediction process will be done after the training process by optimizing the SVR configuration parameters such as kernel, common, gamma, epsilon and degree. Each SVR parameter will be tested in a closed experiment to see the effect of parameters on prediction results. To obtain more representative accuration semantic, we use combination of mean absolute error (MAE) and mean absolute percentage error (MAPE) in calculating error. MAE will explain the average value of error that occurs, while MAPE will explain the error persetase to the data. In the experiment, the best error value MAE 0.52 and MAPE 2.07, which means that the error data ± 0.52 that is equal to 2.07% of the overall data value <p align="justify"><br />
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