DOI: https://doi.org/10.21456/vol7iss1pp17-23

Implementasi Metode Fuzzy Time Series Cheng untuk prediksi Kosentrasi Gas NO2 Di Udara

*M Yoka Fathoni  -  Politeknik Harapan Bersama Tegal, Indonesia
Open Access Copyright (c) 2017 JURNAL SISTEM INFORMASI BISNIS

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Abstract

The forecasting process is essential for determining air quality to monitor NO2 gas in the air. The research aims to develop prediction information system of NO2 gas in air. The method used is Fuzzy Time Series Cheng method. The process of acquiring NO2 gas data is integrated with Multichannel-Multistasion. The data acquisition process uses Wireless Sensor Network technology via broadband internet that is sent and stored in an online database form on the web server. Recorded data is used as material for prediction. Acquisition result of  NO2 gas data is obtained from the sensor which is sent to the web server in the data base in the network by on line, then for futher it is predicted using fuzzy time series Cheng applying re-divide to the results of intervals the first partition of the value of the universe of discourse by historical data fuzzification to determine Fuzzy Logical Relationship dan Fuzzy Logical Relationship Group, so that is obtained result value prediction of NO2 gas concentration. By using 36 sample data of NO2 gas, it is obtained that the value of root of mean squared error of 2.08%. This result indicates that the method of Fuzzy Time Series Cheng is good enough to be used in predicting the NO2 gas.

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Keywords: Fuzzy Time Series Cheng; Fuzzy Time Series Cheng; gas NO2

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Section: Research Articles
Language : EN
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  1. Aguilera, I., Basagana, X., Pay, M.T., Agis, D., Bouso, L., Foraster, M., Rivera, M., Baldasano, J.M., and Kunzli, N., 2014. “Evaluation of the CALIOPE air quality forecasting system for epidemiological research : The example of NO2 in the Province of Girona (Spain)” International Journal of Atmospheric Environment Vol. 72 (2013) 134-141
  2. Chen, S.M., 1996. “Forecasting enrollments based on fuzzy time series - Fuzzy Sets and Systems” International Journal of Applied Science and Engineering Vol. 81 (1996) 311-319
  3. Chen, T.L., Cheng, H.C., dan Teoh, H.J., 2007. “Fuzzy time series based on sequence fo stock price forecasting” International Journal of Physica A Vol. 380, (2007) 3777-390
  4. Cheng, C.H., Chen, T.L., dan Teoh, H.J., 2008. “Fuzzy time series based on adaptive expectation model for TAIEX forecasting” International Journal of Expert System with Application Vol. 34 (2008) 1126-1132
  5. Juhos, I., Makra, L., dan Toth B., 2008. “Forecasting of traffic origin NO and NO2 concentration by Support Vector Machine and neural network using Principal Component Analysis,” International Journal of Simulation Modelling Practice and Theory, Vol.16 (2008) 1488-1502
  6. Stojic, A., Maletic, D., Stojic S.S., Mijic,Z., dan Sostaric, A., 2015. “Forecasting of VOC emission from traffic and industry using classification and regression multivariate methods” International Journal of Science of the Total Environment, Vol.521-522 (2015) 19-26
  7. Song, Q., dan Chissom, B.S., 1993. Forecasting enrollments with fuzzy time series” International Journal of Fuzzy Set and System Vol. 54 (1993) 1-9
  8. Spiegel, M.R., 1988. Teori dan Soal-soal Statistik Versi SI (Metrik), Erlangga, Jakarta
  9. Vanalakar, S., Patil, V., Harale, N.S., Vhanalakar, S.A., Gang, M.G., Kim, J.Y., Patil, P.S dan Kim, J.H., 2015. “Controlled growth of ZnO nanorod arrays via wet chemical route for NO2 gas sensor application“. International Journal of Sensor and Actuator B:Chemical Vol.B221 (2015). 1195-1201
  10. Xihao, S., dan Yimin, L., 2008 Average-based fuzzy time series models for forecasting Shanghai compound index. International Journal of Modelling and Simulation Vol.4 pp (2008). 104-111
  11. Zhang, Y., ocquet, M., Mallet, V., Seigneur, C., dan Baklanov, A., 2012. “Real-time air quality forecasting, part II: State of the science, current research needs, and future prospects” International Journal of Atmospheric Environment Vol. 60 (2012) 656-676

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