KLASIFIKASI TUTUPAN LAHAN PERKOTAAN MENGGUNAKAN NAIVE BAYES BERBASIS FORWARD SELECTION - Salim, M. (Teknosains: Media Informasi Sains dan Teknologi, 2015)
Urban growth as one of the economic symptoms related to the process of urbanization and population displacement in a major way from the countryside to urban areas has fueled the city's growth issues. These developments will bring up a number of problems when faced with the reality of the limited City area. Urban land cover data that has many attributes with 9 types of target classification using the best attributes of search techniques by applying the forward algorithm selection and naive bayes which merit independently in target and requires only a small amount of training data to determine the required parameter estimation in classification process where accuracy 87,04% better compared to testing using random forest algorithm-based forward selection at the level of 72,72% accuracy. so it can be inferred with previous studies using random forest algorithm with 84,42% accuracy. but better with naive bayes algorithm-based forward selection with increased accuracy percentage level of 2.62%.