PART OF SPEECH TAGGING TEKS BERBAHASA INDONESIA MENGGUNAKAN METODE HIDDEN MARKOV MODEL DAN RULE BASED;PART OF SPEECH TAGGING FOR INDONESIAN LANGUAGE TEXT USING HIDDEN MARKOV AND RULE BASED METHOD

Kathryn Widhiyanti, Agus Harjoko

2011 | Disertasi | PROGRAM STUDI S2 ILMU KOMPUTER

The research conduct a Part of Speech Tagging (POS-Tagging) for text in Indonesian language, supporting another process in digitising natural language e.g. Indonesian language text parsing. POS-Tagging is an automated process of labelling word classes for certain word in sentences (Jurafsky and Martin, 2000). The escalated issue is how to acquire an accurate word class labelling in sentence domain. The author would like to propose a method which combine Hidden Markov Model and Rule Based method. The expected outcome in this research is a better accurary in word class labelling, resulted by only using Hidden Markov Model. The labelling results –from Hidden Markov Model– are refined by validating with certain rule, composed by the used corpus automat ically. From the conducted research through some POS-Tagging document, using Hidden Markov Model, produced 100% as the highest accurary for identical text within corpus. For different text within the referenced corpus, used words subjected in corpus, produced 92,2% for the highest accurary.

Kata Kunci : Part of Speech Tagging, Hidden Markov Model, Rule Based, NLP