Understanding customer purchasing behavior is essential for predicting customer loyalty, which directly impacts a company's long-term success. This research aims to determine the effect of chi-square and information gain feature selection in optimizing customer loyalty classification performance, compared to pure kNN. Using a public customer purchasing behavior dataset from Kaggle, containing 10,000 data, 12 attributes with loyalty_status as the label (Gold, Regular, Silver). Evaluating performance by accuracy, kappa, classification error, recall, precision, and RMSE. The highest accuracy 91.99% was obtained by kNN k=3 with information gain, kappa 0.844, precision 95.44%, recall 86.30%, with the lowest classification error 8.01% and the second lowest RMSE 0.245, after kNN k=3 with chi-square. Results show that feature selection has a positive impact on classification, increasing accuracy and reducing errors, with the combination of the kNN k=3 method and information gain proving successful in obtaining high accuracy in classifying customer loyalty.

One of the CPU scheduling methods that is frequently used to reduce waiting time and average execution time is Shortest Job First (SJF). However, this algorithm's accuracy is largelbravy dependent on how well the job execution time is predicted. The purpose of this study is to enhance work execution time estimates by optimizing the SJF algorithm through the use of the Random Forest Regression model. The model in this study is trained using historical job data. The test results demonstrate how Random Forest Regression may be included into SJF to greatly increase system efficiency, especially in terms of throughput and waiting time reduction.