Implementasi Metode Exponential Smoothing Untuk Prediksi Bobot Kargo Bulanan Di Bandara Internasional I Gusti Ngurah Rai - Amaliah Gusfadilah; Budi Darma Setiawan; Bayu Rahayudi (Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 2019)
Goods are important objects to meet people's needs and sometimes the procurement of goods can be done by transferring goods. Transfer of goods can use shipping via air transportation. However, the weight of the cargo indirectly can affect the speed of delivery. So that it demands the airport to always improve the provision of adequate facilities to meet the needs of cargo weight. To be able to meet these demands a mature prediction is needed. The prediction of cargo weight aims to determine cargo weight data in the future by using cargo weight data in the past. The prediction method used in this study uses the Exponential Smoothing method. Exponential Smoothing is a method that continually perfects predictive results by smoothing past values ​​of a data sequence by decreasing time. In this study comparing 3 Exponential Smoothing methods including Single Exponential Smoothing, Double Exponential Smoothing, and Triple Exponential Smoothing, where the method is used to generate predictive values ​​and then evaluate the results of predictions using the Mean Absolute Percentage Error (MAPE). The smallest MAPE is found in the Triple Exponential Smoothing method spanning 5 years with parameter values ​​α = 0.9, β = 0.1, and γ = 0.1 of 13.563. Based on the MAPE values ​​that have been obtained between 10 and 20, the Triple Exponential Smoothing method is included in the good criteria.