Forecasting Inflation In Indonesia Using The Modified Fuzzy Time Series Cheng


  • Indi Ria Al Kadry Universitas Hasanuddin
  • Jusmawati Massalesse Universitas Hasanuddin
  • Muh. Nur Universitas Hasanuddin



Inflation,, Forecasting, Modified Fuzzy Time Series Cheng and MAPE


Inflation is one of the most important indicators to analyze a country’s economy. Therefore, it is necessary to forecast the inflation rate. Forecasting can be done by various methods, one of which is Fuzzy Time Series Cheng. In this study, several modifications were made to the method used. The purpose of this study is to forecast using the Modified Fuzzy Time Series (FTS) Cheng method and determine the accuracy of the forecasting results obtained. The results of this study indicate that the Modified FTS Cheng method can be used in forecasting, either by determining the interval average-based or using the Sturges equation. Based on the results of the calculation of forecasting accuracy using Mean Absolute Percentage Error (MAPE), the accuracy for Modified FTS Cheng by determining the average-based interval for forecasting based on the current state and next state is 11.58% and 5.78%, respectively. Furthermore, the Modified FTS Cheng by determining the interval using the Sturges equation resulted in a MAPE value of 9.61% and a FTS Cheng of 7.54%. The MAPE value of each method is less than 10%, which means that the method has a very good performance, except for Modified FTS Cheng by determining the average-based interval for forecasting based on current state has good performance with MAPE values ​​between 10 % and 20%.  


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Alyousifi, Y., dkk. (2020). Markov Weighted Fuzzy Time-Series Model Based on an Optimum Partition Method for Forecasting Air Pollution. International Journal of Fuzzy System.

Ariyanto, R., Tjahjana, R. H. & Udjiani, T. (2020). Forecasting Retail Sales Based on Cheng Fuzzy Time Seies and Particle Swarm Optimization Clustering Algorithm. Journal of Physic: Conference Series.

Arnita, N. A. & Marpaung, F. (2020). A Comparison of The Fuzzy Time Series Methods of Chen, Cheng and Markov Chain in Predicting Rainfall in Medan. Journal of Physics: Conference Series.

Arroyo, D. O. & Jens, R. P. (2018). A Weighted Fuzzy Time Series Forecasting Model. Indian Journal of Science and Technology, 11(27): 0947-5645.

Badan Pusat Statistik. (2021). Inflasi. Diakses pada tanggal 26 April 2021.

Guo, H., Witold, P. & Xiaodong, L. (2018). Fuzzy Time Series Forecasting Based on Axiomatic Fuzzy Set Theory. Neural Computing and Applications.

Iqbal, S., dkk. (2020). A New Fuzzy Time Series Forecasting Method Based on Clustering and Weighted Average Approach. Journal of Intelligent & Fuzzy System, 38(5): 6089-6098.

Wismarini, N. R. & Untung, K. (2020). Pemodelan Inflasi di Kota Surakarta Tahun 2000-2019. Prosiding Sendika, 6(1).

Xihao, S. & Li, Y. (2008). Average-Based Fuzzy Time Series Models for Forecasting Shanghai Compound Index. World of Modelling and Simulation, 4(2): 104-111.






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