ABSTRAK
Harga sepeda motor selalu berubah setiap tahunnya, oleh karena itu diperlukan sebuah pendekatan dalam memprediksi besarnya harga sepeda motor dengan keakuratan
maksimum. Salah satu dari jenis prediksi kuantitatif adalah prediksi data time series yakni suatu teknik prediksi yang dibangun menggunakan data runtun waktu pada periode tertentu. Dalam tugas akhir ini digunakan metode Weighted Evolving Fuzzy Neural Network (WEFuNN) untuk memprediksi harga sepeda motor berdasarkan data runtun waktu. WEFuNN merupakan pengembagan dari metode Evolving Fuzzy Neural Network (EFuNN) yang memiliki struktur hybrid dari metode Fuzzy Inference System (FIS) dan jaringan saraf tiruan (Neural Network) dengan menerapkan prinsip Evolving Conection System (ECOS) didalam jaringan. Tingkat keakuratan hasil prediksi diukur dengan nilai MAPE (Mean Absolute Percentage Error). Hasil prediksi WEFuNN didapat hasil error rata-rata (MAPE) yaitu sebesar 1,269% dengan menggunakan data penjualan real periode Januari 2014 sampai dengan Juli 2014.
Kata kunci: weighted evolving fuzzy neural networks, fuzzy, evolving connectionist system, fuzzy inference system, peramalan
WEFUNN METHOD IN FORECASTING THE PRICE OF MOTORCYCLE SALES
ABSTRACT
Motorcycles price is always changing every year, therefore we need an approach to predict the magnitude of the price of a motorcycle with a maximum of accuracy. One of the types of quantitative prediction is forecasting of time series data that is a
prediction technique which is constructed using time series data over a given period. In this thesis used Weighted Evolving Fuzzy Neural Network (WEFuNN) to predict
the price of a motorcycle based on time series data. WEFuNN is developing a method of Evolving Fuzzy Neural Network (EFuNN) which has a hybrid structure of the Fuzzy Inference System (FIS) and artificial neural networks (Neural Network) by applying the principle of Conection Evolving System (ECOS) in the network. The level of accuracy of the prediction is measured by the value of MAPE (Mean Absolute Percentage Error). Results obtained WEFuNN prediction average error (MAPE) is equal to 1,269% by using real sales data for the period of January 2014 through July 2014.
Keywords : weighted evolving fuzzy neural networks, fuzzy, evolving connectionist system, fuzzy inference system, forecasting
