Item-based collaborative filtering is a popular technique in recommendation systems that aims to provide suggestions for films to watch or services to users based on similarities between items. In this approach, the similarity between items is calculated using metrics such as cosine similarity, allowing the prediction of user preferences for items that have never been rated. This research implements Item-based collaborative filtering using datasets from Kaggle. Experimental results show that the resulting model is able to provide recommendations with significant improvements in evaluation metrics such as Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) of 3.05 and 3.26. This shows that the smaller the value, the better.

Early childhood plays an important role in forming the basis of development, which involves stimulation of various aspects such as moral religious values, social emotional, language, cognitive, and physical motor skills. The concept of early childhood learning is focused on play, where every activity is designed to be play, so that learning becomes more effective. Parents also need to understand today's children's education to interact with children positively. This research focuses on sentiment analysis of children's education-based app reviews on the Google Play Store, using Random Forest and Logistic Regression methods. The review data is taken from three apps with the theme of child development, namely "About Kids", "PrimaKu", and "Teman Bumil", with a range of review years between 2018 and 2023. The test results show that Logistic Regression has higher accuracy compared to Random Forest, especially in the "About Kids" and "PrimaKu" applications with accuracy above 90%. The conclusion of this research highlights the importance of sentiment analysis in improving understanding of user responses to children's education applications, with suggestions for future research to increase the number of datasets and variations in testing schemes by tuning hyperparameters to improve prediction accuracy and more optimal results.