PART OF SPEECH TAGGING BAHASA INDONESIA MENGGUNAKAN ALGORITMA DECISION TREE

This study aims to apply and analyze Indonesian Part of Speech Tagging (POS Tagging) using the Decision Tree algorithm. The POS Tagging process can use language dictionaries as a reference or assist in the provision of word classes (tags), but there are some constraints such as the determination of word classes for ambiguous words and words that are not in the dictionary, so a further POS Tagging approach is needed to overcome this problem. This study uses Indonesian Language Dictionary (KBBI) data and Indonesian CNN online news data. The news data is then carried out the process of preprocessing, feature extraction, and vectorization so as to produce a dataset of 40,071 which are then used as training data to build the Decision Tree model. POS Tagging testing in this study obtained an accuracy of 95.3%, and a percentage of success of 90.0% in tagging words that were not on KBBI and success of 91.9% for tagging ambiguous words. The results of the implementation of the POS Tagging application for Indonesian sentences are already good although not yet at maximum