Aplikasi Metode Single Exponential Smoothing Untuk Memprediksi Jumlah Penumpang dan Bagasi Berbasis Web
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Institusion
Universitas Katolik Widya Mandira Kupang
Author
TOKAN, Pius Lama
Subject
QA75 Electronic computers. Computer science
Datestamp
2025-01-16 01:09:48
Abstract :
The significant increase in air travel demands efficient management at airports, particularly in handling passengers and baggage. The fluctuating number of passengers and baggage at Terdamu Sabu Airport drives the airport authorities to provide effective and efficient services during passenger and baggage surges. However, the lack of a tool to predict the number of passengers and baggage remains a major challenge. This study aims to predict the number of passengers and baggage at Terdamu Sabu Airport using a web-based Single Exponential Smoothing method. This method utilizes passenger and baggage data from recent years to make predictions, applying a smoothing approach with an alpha value (0 < ? < 1). The study results show that the predicted number of arriving passengers for the following year is 2,105.7541 people with the best alpha value of 0.9 and a MAPE value of 2.92%. The predicted number of departing passengers is 2,257.1375 people with the best alpha value of 0.9 and a MAPE value of 2.82%. Meanwhile, the predicted baggage unload and load amounts are 13,821.2324 kilograms and 12,838.5777 kilograms, respectively, with MAPE values of 4.30% and 4.36%, and the best alpha value of 0.9 for both. This study has produced a web-based application capable of predicting passenger and baggage numbers using theSingle Exponential Smoothing method, which is expected to assist the airport authorities in managing passenger and baggage surges more effectively.
The significant increase in air travel demands efficient management at airports, particularly in handling passengers and baggage. The fluctuating number of passengers and baggage at Terdamu Sabu Airport drives the airport authorities to provide effective and efficient services during passenger and baggage surges. However, the lack of a tool to predict the number of passengers and baggage remains a major challenge. This study aims to predict the number of passengers and baggage at Terdamu Sabu Airport using a web-based Single Exponential Smoothing method. This method utilizes passenger and baggage data from recent years to make predictions, applying a smoothing approach with an alpha value (0 < ? < 1). The study results show that the predicted number of arriving passengers for the following year is 2,105.7541 people with the best alpha value of 0.9 and a MAPE value of 2.92%. The predicted number of departing passengers is 2,257.1375 people with the best alpha value of 0.9 and a MAPE value of 2.82%. Meanwhile, the predicted baggage unload and load amounts are 13,821.2324 kilograms and 12,838.5777 kilograms, respectively, with MAPE values of 4.30% and 4.36%, and the best alpha value of 0.9 for both. This study has produced a web-based application capable of predicting passenger and baggage numbers using theSingle Exponential Smoothing method, which is expected to assist the airport authorities in managing passenger and baggage surges more effectively.
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