Comparison Of K-Means And Hierachical Clustering Methods Performance In System Boarding Costs Selection Recommendations

Abstract

The development of technology is currently getting more advanced and growing faster, especially in the field of information technology. The existence of houses in the vicinity of public facilities is very desirable and beneficial for newcomers to a new area. One of the public facilities that usually have boarding is a university or college. Students who study at a university do not only come from within the city but also come from outside the region. However, most students from outside the region have difficulty choosing boarding or rented houses due to limited information. Limited information about the facilities and the inaccuracy of boarding houses becomes a difficulty at the beginning for those who are new students. With the advancement of information technology, it can answer the need for finding a boarding house, and will be very helpful both from the side of the owner and tenant. To solve complex problems, you can use the K-Means Clustering and Hierarchical Clustering algorithm models that are optimized with naïve Bayes. The final result of this study is that the K-Means and naïve bayes accuracy values ​​are higher with 90.82% accuracy, 90.56% precision, 90.68% recall and longer time that is 10 seconds, while for hierachical and naïve values. Bayes got 88.02% accuracy, 87.82% precision, 88.00% recall and 7.6 seconds faster time