Implementasi Neural Fuzzy Inference System dan Algoritma Pelatihan Levenberg-Marquardt untuk Prediksi Curah Hujan
Abstract: Rainfall prediction
can be used for various purposes and the accuracy in predicting is important in
many ways. In this research, data of
rainfall prediction use daily rainfall data from 2013-2014 years at rainfall
station in Putussibau, West Kalimantan. Rainfall prediction using four parameters:
mean temperature, average humidity, wind speed and mean sea level pressure.
This research to determine how performance Neural Fuzzy Inference System
with Levenberg-Marquardt training algorithm for rainfall prediction. Fuzzy
logic can be used to resolve the linguistic variables used in rule of rainfall.
While neural networks have ability to adapt and learning process, due to
recognize patterns of data from input need training to prediction. And
Levenberg-Marquardt algorithm is used for training because of effectiveness and
convergence acceleration.
The results showed five models NFIS-LM developed using a variety of
membership functions as input obtained that model NFIS-LM with twelve of
membership functions and use four inputs, such as mean temperature, average
humidity, wind speed and mean sea level pressure gives best results to predict
rainfall with values Mean Square Error (MSE) of 0.0262050. When compared with
model NN-Backpropagation, NFIS-LM models showed lower accuracy. It is shown
from MSE generated where model NN-Backpropagation generate MSE of 0.0167990.
Penulis: Nola Ritha, Retantyo
Wardoyo
Kode Jurnal: jptinformatikadd160328
