Increasing Smoke Classifier Accuracy using Naïve Bayes Method on Internet of Things
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Increasing Smoke Classifier Accuracy using Naïve Bayes Method on Internet of Things

Alieja Muhammad Putrada, Maman Abdurohman, Aji Gautama Putrada

Abstract

This paper proposes fire alarm system by implementing Naïve Bayes Method for increasing smoke classifier accuracy on Internet of Things (IoT) environment. Fire disasters in the building of houses are a serious threat to the occupants of the house that have a hazard to the safety factor as well as causing material and non-material damages. In an effort to prevent the occurrence of fire disaster, fire alarm system that can serve as an early warning system are required. In this paper, fire alarm system that implementing Naïve Bayes classification has been impelemented. Naïve Bayes classification method is chosen because it has the modeling and good accuracy results in data training set. The system works by using sensor data that is processed and analyzed by applying Naïve Bayes classification to generate prediction value of fire threat level along with smoke source. The smoke source was divided into five types of smoke intended for the classification process. Some experiments have been done for concept proving. The results show the use of Naïve Bayes classification method on classification process has an accuracy rate range of 88% to 91%. This result could be acceptable for classification accuracy.

Keywords

Fire Disaster, Fire Alarm System, Internet of Things, Naïve Bayes Classifier

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References

[1] A. K. Mohan. 2013. Integrating Wireless Sensor Network and Internet of Things for Detecting Fire using Fuzzy Logic. International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering. 2:12 6431-6438.

[2] M. S. P. Patange and M. S. V. Yadav. 2015. Design and Implementation of Automatic Fire Alarm System based on Wireless Sensor Networks. Journal of Emerging Technologies and Innovative Research. 2:9 49-51

[3] Abdurohman, M., Herutomo, A., Suryani, V., Elmangoush, A. and Magedanz, T., 2013, October. Mobile tracking system using OpenMTC platform based on event driven method. In Local Computer Networks Workshops (LCN Workshops), 2013 IEEE 38th Conference on (pp. 856-860). IEEE.

[4] Herutomo, A., Abdurohman, M., Suwastika, N.A., Prabowo, S. and Wijiutomo, C.W., 2015, May. Forest fire detection system reliability test using wireless sensor network and OpenMTC communication platform. In Information and Communication Technology (ICoICT), 2015 3rd International Conference on(pp. 87-91). IEEE.

[5] Besari, P.A.L., Abdurohman, M. and Rakhmatsyah, A., 2015, May. Application of M2M to detect the air pollution. In Information and Communication Technology (ICoICT), 2015 3rd International Conference on (pp. 304-309). IEEE.

[6] Daniela XHEMALI, Christopher J. HINDE and Roger G. STONE. 2009. Naïve Bayes vs. Decision Trees vs. Neural Networks in the Classification of Training Web Pages. International Journal of Computer Science. 4:1 16-23.

[7] L. Zhang and G. Wang. 2009. Design and Implementation of Automatic Fire Alarm System based on Wireless Sensor Networks. International Symposium on Information Processing. 410-413.

[8] S. Prayogi, M. Yamin and R. Ramadhan. 2016. Perancangan dan Implementasi Prototipe Sistem Pendeteksi Asap dan Panas Pada Ruangan Tertutup Menggunakan Logika Fuzzy Sugeno. semanTIK. 2:2 167-176.

[9] A. Saleh. 2015. Impelementasi Metode Klasifikasi Naïve Bayes Dalam Memprediksi Besarnya Penggunaan Listrik Rumah Tangga. Citec Journal. 2:3 207-217.

[10] F. Xia, L. T, Yang, L. Wang, and A. Vinel. 2012. Editorial Internet of Things. International Journal of Communication Systems. 25:1101-1102.

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