The Development of Intelligent Information System Model for Provincial Economic Forecast
Keywords:
model, information, intelligent, economics forecastingAbstract
This research aims to: 1) Study and analyze current modeling practices for provincial economic forecasting; 2) Develop an appropriate intelligent information model for provincial economic forecasting; and 3) Evaluate the operational efficiency of the developed model. The study examines current modeling practices used by 12 Provincial Treasury Offices under Region 4, collecting in-depth data through interviews with 108 relevant personnel. Findings reveal several limitations in existing models, including a lack of expertise among staff, inconsistencies in hardware and software, and inappropriate data recording formats that do not align with database design principles. The newly developed model utilizes web application technology, MySQL for database management, and Python for programming. It employs the Random Forest Regression model for forecasting, which is capable of handling complex and highly volatile data. The model's performance evaluation yielded high accuracy, with a Coefficient of Determination (R²) of 0.92, a Root Mean Square Error (RMSE) of 3,034.41, and a Mean Absolute Error (MAE) of 2,018.00. Analysis results show that the model closely fits historical data. Performance evaluation indicates a high level of user very satisfaction, with an average score of ( = 4.28 out of 5.00). The highest satisfaction was with the variety of data presentation formats (
= 4.94), followed by the clarity of analytical reports (
= 4.88). These results demonstrate the model’s effectiveness in meeting user needs and its potential as a decision-support tool for economic policy planning by relevant agencies.
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