<![CDATA[White rat farming has an important role in scientific research and the pharmaceutical industry. However, promising to predict white rat populations each harvest cycle is often a challenge for breeders. This research aims to design and implement a prediction system for white rat production at Rasa Farm using a multiple regression forecasting algorithm. This method was chosen because it is able to analyze the relationship between independent variables and dependent variables in predicting white rat populations.This system was developed on a web basis to make it easier for breeders to upload data, analyze information and get population predictions automatically. The data used includes main factors such as the number of male and female broodstock, mortality rates, daily feed consumption, and environmental temperature. The test results show that the multiple regression algorithm is able to provide fairly accurate estimates with a low error rate (MAPE).This system is expected to help farmers in planning production, optimizing resources, and increasing efficiency in managing white rat farms. For further development, the integration of IoT technology and more complex machine learning methods can be applied to increase prediction accuracy. Keywords:Production prediction, white mice, multiple regression, forecasting, web-based system]]>
