IMPLEMENTATION OF HIDDEN MARKOV MODEL WITH VITERBI ALGORITHM FOR DETECTING MEDAN SLANG
Keywords:
Hidden Markov Model, Viterbi Algorithm, Natural Language Processing, Medan Slang, Text ClassificationAbstract
The increasing use of slang language in digital communication has created challenges in automatic text analysis, particularly for regional slang variations such as Medan slang. This study aims to implement the Hidden Markov Model (HMM) with the Viterbi Algorithm to detect Medan slang words in fictional texts. HMM is used to model probabilistic relationships between words through two hidden states, namely standard language and slang language, while the Viterbi Algorithm determines the most probable sequence of states based on observed words. The dataset consists of training and testing data collected from fictional stories containing Medan slang expressions. The proposed method was implemented in a web-based application that provides slang detection, history management, and dataset administration features. Experimental results show that the HMM-Viterbi approach can successfully identify slang words and classify them according to their linguistic categories. The evaluation indicates that the proposed model performs effectively in detecting Medan slang patterns within textual data. This research contributes to the development of Natural Language Processing applications for regional language analysis and demonstrates the potential of probabilistic sequence modeling for local slang detection.
Keywords : Hidden Markov Model, Viterbi Algorithm, Natural Language Processing, Medan Slang,Text Classification.
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Copyright (c) 2026 Gilang Ramadhan Ramadhan, Khairuddin Nasution, Darjat Saripurna
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