Estimasi Pola Cuaca Wilayah Pekanbaru Menggunakan Probabilistic Neural Network - Aszani Aszani; Mustakim Mustakim (UIN Sultan Syarif Kasim Riau, 2016)
Currently, Meteorology Station Class 1 of Pekanbaru requested and is expected to be more intensive in monitoring, providing weather forecasts and provide information appropriate to the needs of society. Because information about the right weather is very important and needed by everyone. Utilization of such information include the planning and implementation of programs in various sectors of construction, agriculture, and other economic activities. Besides, the weather always changed so did the weather forecast is not an easy thing, we have to know the weather patterns that occur BMKG parties when the grouping pattern using rainfall data of at least the last 10 years in order to obtain information about the pattern of normal rainfall, below normal and above normal. Probabilistic Neural Network (PNN) provides a general solution for pattern classification problems with an approach developed in statistics called Bayes classification. Therefore, PNN is used to perform pattern classification. In this study,Estimates Weather Regional Pekanbaru Patterns using PNN method with data 2007-2012 and an average overall accuracy of the test results is equal to 88.00%. Accuracy is highest in January, April and August in the amount of 96.77%. While the lowest for the accuracy of November in the amount of 70.00%.