Abstract
This study compares the accuracy of forecasting using ANFIS and Fuzzy Time Series the number of Australian tourists to Bali. The data used in this study are data on the number of Australia tourists visit to Bali from the period January 2006 through December 2011. ANFIS consists of two stages of learning and testing phases. Least Squares Estimator is used to study the forward direction and Error Back Propagation learning is used in the reverse direction. Forecasting with Fuzzy Time Series is forecast to capture the pattern of previous data is then used to project the data to come. The results of comparison of both methods showed that the ANFIS method has a higher forecasting accuracy than the method of Fuzzy Time Series. Forecasting by using ANFIS method obtained AFER aqual to 9,26% while the prediction using the method of Fuzzy Time Series obtained AFER aqual to 14,02%
Highlights
PendahuluanAdaptive Neuro-Fuzzy Inference System (ANFIS) merupakan jaringan adaptif yang berbasis pada sistem kesimpulan fuzzy (fuzzy inference system)
This study compares the accuracy of forecasting using Adaptive Neuro-Fuzzy Inference System (ANFIS) and Fuzzy Time Series the number of Australian tourists to Bali
“Fuzzy Metric Approach for Fuzzy Time Series Forecasting based on Frequency Density Based Partitioning”, Proceedings of World Academy of Science, Engineering and Technology, Vol.[34 ]: pp
Summary
Adaptive Neuro-Fuzzy Inference System (ANFIS) merupakan jaringan adaptif yang berbasis pada sistem kesimpulan fuzzy (fuzzy inference system). ANFIS dapat membangun suatu mapping input-output yang keduanya berdasarkan pada pengetahuan manusia (pada bentuk aturan fuzzy if-) dengan fungsi keanggotaan yang tepat. Fuzzy time series merupakan proses dinamik dari suatu variabel linguistik yang nilai linguistiknya adalah himpunan fuzzy. Keunggulan pemodelan fuzzy time series adalah mampu memformulasikan suatu permasalahan berdasarkan pengetahuan pakar atau data-data empiris. Hasil peramalan dari kedua metode tersebut dibandingkan dengan tujuan mengetahui keakuratan hasil peramalan jumlah kunjungan wisatawan asal Australia ke Bali. Parameter yang dipakai sebagai perbandingan adalah AFER dan MSE dari masing-masing metode. Average Forecasting Error Rate (AFER) dan Mean Squared Error (MSE) sebuah estimator adalah nilai yang diharapkan dari error. Error yang ada menunjukkan seberapa besar perbedaan hasil estimasi dengan nilai yang akan diestimasi
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