METODE FUZZY TIME SERIES UNTUK PERAMALAN DATA RUNTUN WAKTU (studi kasus: Produk Domestik Bruto Indonesia); FUZZY TIME SERIES METHOD FOR TIME SERIES PREDICTION (case study: Indonesia’s Gross Domestic Product)

MARINUS IGNASIUS JAWAWUAN LAMABELAWA, Subanar

2011 | Disertasi | PROGRAM STUDI S2 ILMU KOMPUTER

Fuzzy time series is a the dynamical process of a linguistic variable which fuzzy set is as linguistic value.The specialty of fuzzy time series modeling is able to formulate a problem based on expert knowledge or empirical data. In this research, empirical analysis have developed with fuzzy time series classical methods first-order and modify the fuzzy time series based on the repartition the universe of discourse, the determination of the value defuzzify and use the year to year percentage change as the universe of discourse. Analysis of empirical data used are time series data of the Indonesia’s Gross Domestic Product. Fuzzy time series forecasting using Chen-Hsu method modifying the classical method of fuzzy time series with repartitioning the universe of discourse and shows increased reliability and accuracy of forecasting compared with the classical method. Jilani-Burney approach based on frequency density based partitioning of the historical data. Modify made by Jilani-Burney of Chen-Hsu method is to specify a Fuzzy Logical Relationship Group (FLRG). This approach results improved perfomance. Stevenson-Porter's approach modifies the method Jilani-Burney using the year to year percentage of data changes as universe of discourse. Stevenson-Porter modification method based on the largest number of data on that partition the universe of discourse show increased reliability forecasting by MSE reached 99,95% and increase forecasting accuracy with MAPE reached 99,14% from previous methods.

Kata Kunci : peramalan, fuzzy time series, pendekatan baru fuzzy time series, PDB Indonesia