Indoor Positioning System in Learning Approach Experiments
The positioning system research strongly supports the development of location-based services used by related business organizations. However, location-based services with user experience still have many obstacles to overcome, including how to maintain a high level of position accuracy. From the lite...
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Journal of Electrical and Computer Engineering |
| Online Access: | http://dx.doi.org/10.1155/2021/6592562 |
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| author | Dodo Zaenal Abidin Siti Nurmaini Erwin Errissya Rasywir Yovi Pratama |
| author_facet | Dodo Zaenal Abidin Siti Nurmaini Erwin Errissya Rasywir Yovi Pratama |
| author_sort | Dodo Zaenal Abidin |
| collection | DOAJ |
| description | The positioning system research strongly supports the development of location-based services used by related business organizations. However, location-based services with user experience still have many obstacles to overcome, including how to maintain a high level of position accuracy. From the literature studies reviewed, it is necessary to develop an indoor positioning system using fingerprinting based on Received Signal Strength (RSS). So far, the testing of the indoor positioning system has been carried out with an algorithm. But, in this research, with the proposed parameters, we will conduct experiments with a learning approach. The data tested is the signal service data on the device in the Dinamika Bangsa University building. The test was conducted with a deep learning approach using a deep neural network (DNN) algorithm. The DNN method can estimate the actual space and get better position results, whereas machine learning methods such as the DNN algorithm can handle more effectively large data and produce more accurate data. From the results of comparative testing with the learning approach between DNN, KNN, and SVM, it can be concluded that the evaluation with KNN is slightly better than the use of DNN in a single case. However, the results of KNN have low consistency; this is seen from the fluctuations in the movements of the R2 score and MSE values produced. Meanwhile, DNN gives a consistent value even though it has varied hidden layers. The Support Vector Machine (SVM) gives the worst value of these experiments, although, in the past, SVM was known as one of the favorite methods. |
| format | Article |
| id | doaj-art-d7d4652a72874cd4860760c9e44b3a80 |
| institution | Kabale University |
| issn | 2090-0147 2090-0155 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Electrical and Computer Engineering |
| spelling | doaj-art-d7d4652a72874cd4860760c9e44b3a802025-02-03T07:24:03ZengWileyJournal of Electrical and Computer Engineering2090-01472090-01552021-01-01202110.1155/2021/65925626592562Indoor Positioning System in Learning Approach ExperimentsDodo Zaenal Abidin0Siti Nurmaini1Erwin2Errissya Rasywir3Yovi Pratama4Department of Engineering, Sriwijaya University, Palembang, IndonesiaDepartment of Informatics Engineering, Sriwijaya University, Palembang, IndonesiaDepartment of Informatics Engineering, Sriwijaya University, Palembang, IndonesiaDepartment of Informatics Engineering, Dinamika Bangsa University, Jambi, IndonesiaDepartment of Informatics Engineering, Dinamika Bangsa University, Jambi, IndonesiaThe positioning system research strongly supports the development of location-based services used by related business organizations. However, location-based services with user experience still have many obstacles to overcome, including how to maintain a high level of position accuracy. From the literature studies reviewed, it is necessary to develop an indoor positioning system using fingerprinting based on Received Signal Strength (RSS). So far, the testing of the indoor positioning system has been carried out with an algorithm. But, in this research, with the proposed parameters, we will conduct experiments with a learning approach. The data tested is the signal service data on the device in the Dinamika Bangsa University building. The test was conducted with a deep learning approach using a deep neural network (DNN) algorithm. The DNN method can estimate the actual space and get better position results, whereas machine learning methods such as the DNN algorithm can handle more effectively large data and produce more accurate data. From the results of comparative testing with the learning approach between DNN, KNN, and SVM, it can be concluded that the evaluation with KNN is slightly better than the use of DNN in a single case. However, the results of KNN have low consistency; this is seen from the fluctuations in the movements of the R2 score and MSE values produced. Meanwhile, DNN gives a consistent value even though it has varied hidden layers. The Support Vector Machine (SVM) gives the worst value of these experiments, although, in the past, SVM was known as one of the favorite methods.http://dx.doi.org/10.1155/2021/6592562 |
| spellingShingle | Dodo Zaenal Abidin Siti Nurmaini Erwin Errissya Rasywir Yovi Pratama Indoor Positioning System in Learning Approach Experiments Journal of Electrical and Computer Engineering |
| title | Indoor Positioning System in Learning Approach Experiments |
| title_full | Indoor Positioning System in Learning Approach Experiments |
| title_fullStr | Indoor Positioning System in Learning Approach Experiments |
| title_full_unstemmed | Indoor Positioning System in Learning Approach Experiments |
| title_short | Indoor Positioning System in Learning Approach Experiments |
| title_sort | indoor positioning system in learning approach experiments |
| url | http://dx.doi.org/10.1155/2021/6592562 |
| work_keys_str_mv | AT dodozaenalabidin indoorpositioningsysteminlearningapproachexperiments AT sitinurmaini indoorpositioningsysteminlearningapproachexperiments AT erwin indoorpositioningsysteminlearningapproachexperiments AT errissyarasywir indoorpositioningsysteminlearningapproachexperiments AT yovipratama indoorpositioningsysteminlearningapproachexperiments |