3rd Asia Pacific International Conference on Industrial Engineering and Operations Management

Chatbot In The Selection Of Outpatient Ward In Hospital Using C4.5 Algorithm

Fandi Adi Prasetio, Faiza Renaldi & Fajri Rakhmat Umbara
Publisher: IEOM Society International
0 Paper Citations
Track: Data Analytics and Big Data

The C4.5 algorithm is one of many data mining classification techniques. The final result in the classification of this algorithm is the creation of a decision tree. This tree helps in making predictions and selection. The c4.5 algorithm is widely used in many cases, such as outpatient services, disease prediction, etc. It's just that for the c4.5 algorithm method in the construction of chatbots, only a few apply it. More cases of implementing chatbots using Semantic Web, NLP, etc. This study focuses on the c4.5 algorithm method and its application in chatbots because, in previous studies, it was still rare to implement chatbot services in hospitals using this algorithm. The c4.5 algorithm method in this study will be carried out on a chatbot because in predicting poly hospital services for selection, it is more suitable to use this algorithm. The data used were obtained from the Azra Hospital Bogor, totaling 3,292 data after the data cleaning process with the classification results using the c4.5 algorithm, namely getting the RMSE (Root Mean Square) / Mean value of 12.66%. This number means that the lower the average prediction error, the less. That means that from the total data used, 315 data experienced wrong predictions, and 2,977 other data were predicted correctly. Gradient Boosting was also carried out to reduce the prediction error rate by 0.66% after being tested seven times. In the future, this research is expected to improve the classification test time.

Published in: 3rd Asia Pacific International Conference on Industrial Engineering and Operations Management, Johor Bahru, Malaysia

Publisher: IEOM Society International
Date of Conference: September 13-15, 2022

ISBN: 978-1-7923-9162-0
ISSN/E-ISSN: 2169-8767