The space-time model combines spatial and temporal elements. One example is the Generalized Space-Time Autoregressive (GSTAR) Model, which improves the Space-Time Autoregressive (STAR) model. The GSTAR model assumes that each location has heterogeneity characteristics, and that the data is stationary. In this research, the moving average component is calculated by involving the relationship between variable values at a certain time and residual values at a previous time, and it is assumed that the data is not stationary, so the model used is the Generalized Space-Time Autoregressive Integrated Moving Average (GSTARIMA) Model. The model order for GSTARIMA is determined through the Space-Time Autocorrelation Function (STACF) and Space-Time Partial Autocorrelation Function (STPACF) to ensure accurate forecasting. Previous research only discussed the GSTARIMA(1,1,1) model, so in this research, the GSTARIMA(3,1,1) model will be addressed as a form of development of the GSTARIMA(1,1,1) model and applied to climate data. The climate data used in this research is sourced from NASA POWER and consists of rainfall variables with large data sizes, requiring the use of the data analytics lifecycle method to analyse Big Data. The lifecycle includes six phases: discovery, data preparation, model planning, model building, communicating results, and operationalization. Based on the data processing results with Python software, the GSTARIMA(3,1,1) model has a MAPE value of 9% for out-sample data and 11% for in-sample data. In contrast, the GSTARIMA(1,1,1) model has a MAPE value of 11% for out-sample data and 12% for in-sample data. So the GSTARIMA(3,1,1) model provides more accurate forecasting results. Therefore, selecting the correct model order is crucial for accurate forecasting.

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.