Analisis Data Pada Jaringan Sensor Nirkabel Menggunakan Metode Support Vector Machine

  • Caroline Layadi STMIK KHARISMA Makassar
  • Mohammad Fajar STMIK KHARISMA Makassar
  • Hasniati Hasniati STMIK KHARISMA Makassar
  • Izmy Alwiah Musdar STMIK KHARISMA Makassar
Keywords: wireless sensor network, support vector machine, kernel, data mining, accuracy

Abstract

The aims of this research are to implement Support Vector Machine for analyze abnormal data on sensor network and evaluate the implementation result. The data collection in the research were done through the searching of related libraries and software evaluate/testing. In this research, temperature, wind speed, and humidity tested using three kernels (linear, Gaussian, and polynomial). Evaluation result show that the implementation of Support Vector Machine can perform the best data validity analysis using Gaussian Kernel with the percentage of average accuracy, temperature 97.83%, humidity 94.5325%, and wind speed 96.93% for weather data 20-28 May and July 28-August 10, 2015. Meanwhile, for weather data June 5-6, 2017 obtained the percentage of average accuracy of temperature 92.855% and humidity 92.43%.

Downloads

Download data is not yet available.

References

[1] A. B. Wiratma, R. Munadi, and R. Mayasari, “Implementasi dan Analisis Jaringan Sensor Nirkabel Sebagai Alat Pendeteksi Kebocoran Tabung Gas Elpiji Menggunakan Topologi Cluster Tree Dengan 7 Titik,” in E-Proceeding of Engineering, 2016, vol. 3, no. 2, pp. 1779–1786.

[2] B. Yasin, M. Fajar, and A. Halid, “Deteksi Anomaly Data Pada Jaringan Sensor Menggunakan Bayesian Network Model,” STMIK KHARISMA Makassar, 2015.

[3] V. Chandani, R. S. Wahono, and . Purwanto, “Komparasi Algoritma Klasifikasi Machine Learning Dan Feature Selection pada Analisis Sentimen Review Film,” J. Intell. Syst., vol. 1, no. 1, pp. 55–59, 2015.

[4] S. Kumar Shrivastava and P. Jain, “Effective Anomaly based Intrusion Detection using Rough Set Theory and Support Vector Machine,” Int. J. Comput. Appl., vol. 18, no. 3, pp. 35–41, 2011.

[5] A. Jacobus and E. Winarko, “Penerapan Metode Support Vector Machine pada Sistem Deteksi Intrusi secara Real-time,” Ijccs, vol. 8, no. 1, pp. 13–24, 2014.

[6] M. Fajar, A. Halid, and S. Rahman, “Desain dan Evaluasi Prototipe Jaringan Sensor Nirkabel untuk Monitoring Lahan Persawahan di Kabupaten Gowa,” J. Sisfo, vol. 6, no. 3, pp. 319–330, 2017.

[7] E. Susilowati, M. K. Sabariah, and A. A. Gozali, “Implementasi Metode Support Vector Machine Untuk Melakukan Klasifikasi Kemacetan Lalu Lintas Pada Twitter Implementation Support Vector Machine Method for Traffic Jam Classification on Twitter,” E-Proceeding Eng., vol. 2, no. 1, pp. 1–7, 2015.
Published
2018-04-21
How to Cite
Layadi, C., Fajar, M., Hasniati, H., & Musdar, I. A. (2018). Analisis Data Pada Jaringan Sensor Nirkabel Menggunakan Metode Support Vector Machine. Jurnal RESISTOR (Rekayasa Sistem Komputer), 1(1), 8-15. https://doi.org/10.31598/jurnalresistor.v1i1.196
Abstract viewed = 175 times
FULL TEXT downloaded = 107 times