Comparison of K-Nearest Neighbor, Support Vector Machine, Random Forest, and C 4.5 Algorithms on Indoor Positioning System
Today’s most common Positioning System applied is the Global Positioning System (GPS). Positioning System is considered accurate when outdoors, but it becomes a problem when indoors making it difficult to read the GPS signal. Many academics are actively working on indoor positioning solutions to add...
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| Format: | Article |
| Language: | English |
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State Islamic University Sunan Kalijaga
2023-06-01
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| Series: | IJID (International Journal on Informatics for Development) |
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| Online Access: | https://ejournal.uin-suka.ac.id/saintek/ijid/article/view/3991 |
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| author | M Rizky Astari Muhammad Taufiq Nuruzzaman Bambang Sugiantoro |
| author_facet | M Rizky Astari Muhammad Taufiq Nuruzzaman Bambang Sugiantoro |
| author_sort | M Rizky Astari |
| collection | DOAJ |
| description | Today’s most common Positioning System applied is the Global Positioning System (GPS). Positioning System is considered accurate when outdoors, but it becomes a problem when indoors making it difficult to read the GPS signal. Many academics are actively working on indoor positioning solutions to address GPS's drawbacks. Because WiFi Access Point signals are frequently employed in multiple studies, they are used as research material. This study compares the classification algorithms KNN, SVM, Random Forest, and C 4.5 to see which algorithm provides more accurate calculations. The fingerprinting method was employed in the process of collecting signal strength data in each room of the Terpadu Laboratory Building at UIN Sunan Kalijaga using 30 rooms and a total dataset of 5,977 data. The data is utilized to run experiments to determine the location using various methods. According to the experimental data, the Random Forest algorithm achieves an accuracy rate of 83%, C4.5 81%, and KNN 80%, while the SVM method achieves the lowest accuracy rate of 57%. |
| format | Article |
| id | doaj-art-4e65ee46fef64745b5aeb9f15fb55ec7 |
| institution | OA Journals |
| issn | 2252-7834 2549-7448 |
| language | English |
| publishDate | 2023-06-01 |
| publisher | State Islamic University Sunan Kalijaga |
| record_format | Article |
| series | IJID (International Journal on Informatics for Development) |
| spelling | doaj-art-4e65ee46fef64745b5aeb9f15fb55ec72025-08-20T02:17:40ZengState Islamic University Sunan KalijagaIJID (International Journal on Informatics for Development)2252-78342549-74482023-06-0112130231310.14421/ijid.2023.39913617Comparison of K-Nearest Neighbor, Support Vector Machine, Random Forest, and C 4.5 Algorithms on Indoor Positioning SystemM Rizky Astari0Muhammad Taufiq Nuruzzaman1https://orcid.org/0000-0002-4348-6552Bambang Sugiantoro2UIN Sunan KalijagaUIN Sunan Kalijaga YogyakartaUIN Sunan KalijagaToday’s most common Positioning System applied is the Global Positioning System (GPS). Positioning System is considered accurate when outdoors, but it becomes a problem when indoors making it difficult to read the GPS signal. Many academics are actively working on indoor positioning solutions to address GPS's drawbacks. Because WiFi Access Point signals are frequently employed in multiple studies, they are used as research material. This study compares the classification algorithms KNN, SVM, Random Forest, and C 4.5 to see which algorithm provides more accurate calculations. The fingerprinting method was employed in the process of collecting signal strength data in each room of the Terpadu Laboratory Building at UIN Sunan Kalijaga using 30 rooms and a total dataset of 5,977 data. The data is utilized to run experiments to determine the location using various methods. According to the experimental data, the Random Forest algorithm achieves an accuracy rate of 83%, C4.5 81%, and KNN 80%, while the SVM method achieves the lowest accuracy rate of 57%.https://ejournal.uin-suka.ac.id/saintek/ijid/article/view/3991gpswifi access point signalsclassification algorithmsaccuratefingerprinting |
| spellingShingle | M Rizky Astari Muhammad Taufiq Nuruzzaman Bambang Sugiantoro Comparison of K-Nearest Neighbor, Support Vector Machine, Random Forest, and C 4.5 Algorithms on Indoor Positioning System IJID (International Journal on Informatics for Development) gps wifi access point signals classification algorithms accurate fingerprinting |
| title | Comparison of K-Nearest Neighbor, Support Vector Machine, Random Forest, and C 4.5 Algorithms on Indoor Positioning System |
| title_full | Comparison of K-Nearest Neighbor, Support Vector Machine, Random Forest, and C 4.5 Algorithms on Indoor Positioning System |
| title_fullStr | Comparison of K-Nearest Neighbor, Support Vector Machine, Random Forest, and C 4.5 Algorithms on Indoor Positioning System |
| title_full_unstemmed | Comparison of K-Nearest Neighbor, Support Vector Machine, Random Forest, and C 4.5 Algorithms on Indoor Positioning System |
| title_short | Comparison of K-Nearest Neighbor, Support Vector Machine, Random Forest, and C 4.5 Algorithms on Indoor Positioning System |
| title_sort | comparison of k nearest neighbor support vector machine random forest and c 4 5 algorithms on indoor positioning system |
| topic | gps wifi access point signals classification algorithms accurate fingerprinting |
| url | https://ejournal.uin-suka.ac.id/saintek/ijid/article/view/3991 |
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