Hidden Markov Model Dan Aplikasinya

This paper presents the results of research literature about the author of Hidden Markov Model (HMM). Theories about HMM developed for state that can not be observed directly, the model also remain hidden, but the model parameters are known and fixed output can be obtained. Application state HMM on weather observation within 7 days and hidden and not brought the state to bring a raincoat, using all five parameters: The transition matrix (A), the emission matrix (B), the number of hidden elements of state (N), the number of state observed (M) and the initial probability distribution (π). The output obtained after the evaluation phase and decoding is the same as the chance of rain state 0.13 or δ2 (h) = 0.13 and optimal sequence state is Q={q1*,q2* } = {rain. rain}