The coffee shop industry in Indonesia is experiencing rapid growth, including D'Kopikap as one of the businesses facing challenges in managing production and inventory. The main difficulty lies in the uncertainty in predicting the number of sales, which is influenced by various factors such as the type of day (weekdays, weekends, holidays, and promo days) and the time of sale. To overcome this problem, a sales prediction system using multiple linear regression algorithm was developed. System development is carried out using the Rapid Application Development (RAD) approach to accelerate the design and implementation process. The data used in this research is divided into two parts, namely training and test data of 80:20. The results showed that the sales prediction system created was able to provide sales forecasts that could be used to assist production planning and inventory management. Evaluation of the model shows the Root Mean Squared Error (RMSE) value of 22.48, Mean Absolute Percentage Error (MAPE) of 37.18%, and Coefficient of Determination (R²) of 72.26%. These results indicate that the model has good enough predictive ability to be used in operational activities. This research is expected to be the basis for the development of a better sales prediction system in the future.

The performance evaluation of student organizations (Ormawa) at Politeknik Negeri Bengkalis is an essential part of the end-of-term management assessment, serving as a form of accountability and a basis for future development. The manual process used so far often takes considerable time and is prone to errors. To address this issue, a web-based decision support system was developed using the Multi-Factor Evaluation Process (MFEP) method, which applies a weighting system in the decision-making process. The system was developed using the Rapid Application Development (RAD) approach, enabling a fast and efficient development process through a linear and sequential methodology. The implementation of the MFEP method in the system achieved an accuracy rate of 88.88%, based on a comparison between field data and system processing results.