Python script fuzzy time series Markov chain model for forecasting the number of diseases cocoa plant in Bendungan district

Abstract

Cocoa is a plantation commodity whose role is essential for the economy, so it is necessary to be aware of its health to maximize production. Cocoa plant disease data is a time series data because it is observed continuously. One of the time series forecasting models is the Fuzzy Time Series Markov Chain (FTS-MC), a combination of the Fuzzy Time Series (FTS) and Markov Chain models. The model uses the principle of fuzzy logic by transferring FTS data to fuzzy logic and using the obtained fuzzy logic groups to derive the Markov chain transition matrix. In this research, a Python script of the FTS-MC model was built in the Google Colaboratory to forecast the number of cocoa plant diseases in Bendungan District to simplify and speed up the data processing. Python was used in this research because of its easy-to-use, flexible, and open-source syntax. In building Python scripts, libraries and functions are needed by utilizing loop processes and if-else statements. Based on the processing results, forecasting with the FTS-MC model using Python only takes less than 1 minute.