Classification of User Satisfaction Level of Sihebat Mobile Application Using Random Forest Algorithm
DOI:
https://doi.org/10.37182/3js4yh85Keywords:
SIHEBAT EUCS, Random Forest Classification, Model User Satisfaction, PredictionAbstract
The SIHEBAT (Fuel Efficiency Information System) mobile application was developed by the Kupang City Communication and Information Agency to optimize the fuel budget for official vehicles. The successful implementation of this system requires periodic evaluation of end-user satisfaction. This study aims to (1) analyze user satisfaction levels based on five dimensions of End User Computing Satisfaction (EUCS)—Content, Accuracy, Ease of Use, Format, and Timeliness—and (2) Develop a predictive model of user satisfaction scores using the Random Forest Regression algorithm.. Data were collected through an online questionnaire from 30 vehicle managers and 150 application users within the Kupang City Government. Initial EUCS analysis shows that user satisfaction is at a very satisfied level for Content (97.0%), Format (86.9%), and Accuracy (86.4%). However, improvements are still needed in Ease of Use (78.3%) and Timeliness (76.6%), indicating challenges in interface usability and system responsiveness. For predictive modeling, EUCS variables were used as input features to estimate the overall satisfaction score (numerical value). The Random Forest Regression model was selected due to its ability to handle nonlinear relationships and identify the most influential variables. The evaluation results show excellent predictive performance with a Mean Squared Error (MSE) of 0.02, Root Mean Squared Error (RMSE) of 0.14, and R² of 0.9982. These results indicate that Ease of Use and Timeliness are the most critical predictors of user satisfaction. This model provides a valid and data-driven tool for the Kupang City Communication and Information Agency to prioritize system improvements based on factors that significantly influence user satisfaction.
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