KLASIFIKASI TUTUPAN LAHAN PERKOTAAN MENGGUNAKAN NAIVE BAYES BERBASIS FORWARD SELECTION
Abstract
Urban growth as one of the economic symptoms related to theprocess 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%.
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