This research focuses on using the K-Nearest Neighbor (KNN) algorithm to model student satisfaction with campus services. The study finds that the quality of the dataset strongly influences the accuracy of the KNN classification results. Factors such as data cleanliness, balanced class distribution, and sufficient training data volume are highlighted as crucial for a successful model. The research also emphasizes the significance of proper feature selection in enhancing classification performance, suggesting that irrelevant features can introduce noise and decrease model accuracy. The model was evaluated using a dataset of 1032 data points and K=5, achieving an accuracy of 93.72%. While the model performed well for certain classes such as "Very Good" and "None", challenges were encountered in classifying the "Fair" and "Deficient" classes. The study concludes that KNN is effective in identifying student satisfaction patterns but highlights the need for improvements in accurately classifying these challenging classes. Ultimately, the research underscores the importance of data quality and feature selection in enhancing the performance of classification models for student satisfaction analysis.

A blood donor is someone who wants to donate their own blood to people in need without any element of coercion from anyone. Predicting the number of blood donors is very important and necessary to find out the number of blood donors in Langkat Regency in 2023-2024, and the prediction results can help PMI Langkat Regency in increasing the number of blood donors. The method applied in this prediction system is Linear Regression, where this analysis determines whether or not each variable is in accordance with the prediction results being tested and estimates that the value of the variable will increase or decrease each month. The prediction system is carried out using the RapidMiner application because this application is very appropriate for producing information output in the form of prediction results for the coming year. The prediction results obtained by testing using the Linear Regression method show increases and decreases every month. There are 11 months where there has been an increase and decrease in the predicted results and are in accordance with the data in 2023, then there is 1 month which has decreased in the predicted results and does not match the data in 2023. From the overall data results, it can be calculated the number of blood donors in Langkat Regency in 2023 and every month. Measuring the error level of prediction results using RMSE, the resulting accuracy level was 83.574%.