A Comparative Analysis of K-Nearest Neighbors and Random Forest Methods for Recommendations on Selecting Islamic Boarding Schools Based on Student Interest Profiles (primary and middle school students at xxx)

Mas Nurul Hamidah, Rahmawati Febrifyaning Tias, Rifki Fahrial Zainal

Abstract


KNN and Random Forest are one of the classification methods, in this study will compare 2 methods in machine learning namely KNN and Random forest to recommend the type of Islamic boarding school based on student interests, the application of a comparison of 2 classification methods in the recommendation system for selecting the type of Islamic boarding school based on student interests at the Elementary and Middle School levels of Xxx, The types of Islamic boarding schools are salafi, khalafi and mixed, with attributes such as academic tendencies, religious interests, extracurricular involvement, and family background. application of machine learning methods to support decision making in selecting Islamic boarding schools that are in accordance with student character, which is still rarely found in Islamic educational institutions. Performance evaluation is carried out using the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) metrics. The test results show that the Random Forest algorithm gives better results with an MAE of 0.23 and an RMSE of 0.57, compared to KNN which has an MAE of 0.6 and an RMSE of 0.96. Thus, Random Forest shown to be more effective in providing recommendations for selecting appropriate Islamic boarding schools, and can be used as a basis for developing a decision support system for Islamic boarding school-based schools.

Keywords: KNN, Machine Learning, Random Forest, Islamic boarding schools


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DOI: https://doi.org/10.21107/nero.v10i2.30548

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