Perbandingan Metode Pembobotan TF-RF Dan TF-ABS Pada Kategorisasi Berita Di BDI Denpasar

Abstract

BDI Denpasar is a government agency tasked with carrying out training and education for human resources of animation, crafts and art. BDI Denpasar in managing news classes in the Kabar Insan Oke service still uses conventional methods. Therefore an automatic news classification module is needed. This study was made to compare the performance level of news classification at BDI Denpasar using K-NN classification with the TF-RF and TF-ABS term weighting methods. Methods that have a high level of performance will be implemented in the news classification module. This research was carried out by collecting news documents, text preprocessing, term weighting, classification, model validation and testing. The K-NN classification uses the n_neighbhor (k), namely k=3, k=5, k=7 and k=9 using a dataset of 324 documents containing 7 classes taken from BDI Denpasar website. Based on the results of the tests performed, TF-RF method obtained a higher performance at k=5 with an accuracy of 71% with a precision of 73% and a recall of 71%. TF-ABS method with the highest performance value is found at k=9 which obtains 70% accuracy, 63% precision and 70% recall. So the method that will be implemented in the news classification module is TF-RF at k=5 with an accuracy of 71% with a precision of 73% and a recall of 71%.