Rajoo, SSRajooMagalingam, PPMagalingamIdris, NBNBIdrisSamy, GNGNSamyMaarop, NNMaaropShanmugam, BBShanmugamPerumal, SSPerumal2024-05-292024-05-2920181936-73171936-661210.1166/asl.2018.11789WOS:000432354700199https://oarep.usim.edu.my/handle/123456789/11999In this research, a false report identification algorithm for mobile application is developed using a text classification technique. This algorithm is proposed to be applied to a reporting service application in an IoT environment. The algorithm is aimed to distinguish reports into true and false information. Support Vector Machine (SVM) is used as the text classifier because it has proven to be the most popularly used due to its good performance and higher accuracy compared to the other techniques such as Naive Bayes, Decision Tree and K-Nearest Neighbours. The algorithm is designed and developed in R Studio and we built a framework to show how the algorithms can be adapted into a reporting service application. The results show that the algorithm has successfully classified the reports.en-USFalse Report identification AlogorithmSupport Vector MachineAlogorithmProceedings Paper690694241