PREDIKSI CUACA MENGGUNAKAN METODE CASE BASED REASONING DAN ADAPTIVE NEURO FUZZY INFERENCE SYSTEM

Ria Chaniago, The Houw Liong, Ken Ratri Retno Wardani




Abstract


Weather is one of the nature elements that can influence decision making in human's life. Based on that issue, the author wants to make an application that is able to predict weather with good accuracy. The application is a weather forecasting system, using computer technology that implements expert system. The methods used are Adaptive Neuro Fuzzy Inference System (ANFIS) and Case Based Reasoning (CBR), and a combination of both methods will applied to the system. The system also has learning methods like Backpropagation Error (BPE) and Recursive Least Error (RLSE), to increase its accuracy. Clustering and data cleaning also done inside the system, as it needed by forecasting process to achieve a good result. K-Means is the clustering algorithm, while Box and Whisker Plot is the algorithm for data cleaning. The result from this project is to create a weather forecasting system with high accuracy.


Keywords


Expert System, Weather Forecasting, Adaptive Neuro Fuzzy Inferance System, Case Based Reasoning, Backpropagation Error, Recursive Least Square Estimator, K-Means, Box and Whisker plot.

References


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