Skripsi
KLASIFIKASI PELAGGAN RESERVASI HOTE MENGGUNAKAN METODE RANDOM FOREST
Online hotel reservations have dramatically changed booking possibilities and customer behavior. Reasons for cancellation include changes in plans, scheduling conflicts, and personal affairs. This resulted in losses that had to be faced by the hotel side. To find out the pattern of cancellation of reservations by customers can be done with a classification approach. Classification of customer reservation data that has been collected by 36 thousand from 2017 to 2018. Random forest method is a good classification method based on research (Breiman, 2001) random forest has advantages such as providing good results in classification, being able to cope with very large amounts of data training efficiently, being able to produce lower errors, and being effective for estimating missing data. Customer data has 2 classes namely NOT_cancelled (not cancelled) and cancelled (cancelled) in the customer's booking status. The study divided the data with split data ratios of 90:10, 80:20, 70:30, and 60:40 as well as tree counts of 10, 20, 30, 40, 50, 60, 70, 80, 90, and 100. The results showed that random forest managed to classify hotel reservation customers with the highest accuracy of 86.34% on 30 trees and a data split ratio of 80:20 gave the best results compared to other ratios. In addition, the random forest process will be getting longer with more data and trees being formed.
Inventory Code | Barcode | Call Number | Location | Status |
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2407000293 | T137769 | T1377692024 | Central Library (Referens) | Available but not for loan - Not for Loan |
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