Citation

Chia, Zi Yang and Goh, Pey Yun (2024) Real Time 3D Internal Building Directory Map. Journal of Informatics and Web Engineering, 3 (2). pp. 37-56. ISSN 2821-370X

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Abstract

Global Positioning System (GPS) is a famous technology around the world in identifying the real time precise location of any object with the assistanceof satellites. The most common application of GPS is the use of outdoor maps. GPS offers efficient, scalable and cost-effective location services. However, this technology is not reliable when the position is in an indoor environment. The signal is very weak or totally lost due to signal attenuation and multipath effects. Among the indoor positioning technologies, WLAN is the most convenient and cost effective. In recent research, machine learning algorithmshave become popular and utilized in wireless indoor positioning to achieve better performance. In this paper, different machine learning algorithmsareemployed to classify different positionsin the real-world environment (e.g., Ixora Apartment -House and Multimedia University Malacca –FIST building). Received Signal Strength Indication (RSSI) is collected at each reference point. This data is then used to train the model with hyperparameter tuning. Based on the experiment result, Random Forest achieved 82% accuracy in Ixora Apartment and 84% accuracy in one of the buildings in Multimedia University Malacca. These results outperformed the othermodels, i.e., K-Nearest Neighbors (KNN) and Support Vector Machine (SVM

Item Type:	Article
Uncontrolled Keywords:	Global Positioning System
Subjects:	G Geography. Anthropology. Recreation > G Geography > G1-922 Geography (General)
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	10 Jul 2025 09:16
Last Modified:	10 Jul 2025 09:16
URII:	http://shdl.mmu.edu.my/id/eprint/14235

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