Prediksi Kurs Rupiah Terhadap Dolar Dengan FTS-Markov Chain Dan Hidden Markov Model

Abstract

<jats:p>Hidden Markov model is a development of the Markov chain where the state cannot be observed directly (hidden), but can only be observed, a set of other observations and combination of fuzzy logic and Markov chain to predict Rupiah exchange rate against the Dollar. The exchange rate of purchasing and exchange rate of saling is divided into four states, namely down large, down small, small rise, and large rise are symbolized respectively S1, S2, S3, and S4. Probability of sequences of observation for 3 days later is computed by forwarding and Backward Algorithm, determine the hidden state sequence using the viterbi algorithm and estimate the HMM parameters using the Baum Welch algorithm. The MAPE result exchange rate of purchase of FTS-Markov Chain is 1,355% and the exchange rate of sale of FTS-Markov Chain is 1,317%. The sequences of observation which optimized within exchange rate of purchase is X* = {S3,S3,S3}, within exchange rate of sale is also X* = {S3,S3,S3}. Keywords: Exchange rate, FTS-Markov Chain, Hidden Markov Model</jats:p>

Markov average-based weighted fuzzy time series model to predict PT Kimia farma Tbk stock price

“…FTS has been used in several previous researches. Susilowati and Sulistijanti (2018) predicted the number of inpatients using FTS, and Jatipaningrum et al (2019) predicted the Rupiah exchange rate against the Dollar with FTS combined with the Markov chains method. The accuracy of the results obtained is satisfying enough.…”

Application of the Fuzzy Time Series-markov Chain Method to the Rupiah Exchange Rate Against the US Dollar (USD)

“…Kurs rupiah Indonesia mempunyai dampak berarti pada fluktuasi Indeks Harga Saham Gabungan (IHSG) (Jatipaningrum et al 2019). Semakin turunnya harga kurs rupiah terhadap dolar amerika mempengaruhi trend IHSG (Tesa, 2012).…”