Open AccessPREDIKSI JUMLAH KEBERANGKATAN PENUMPANG PESAWAT TERBANG MENGGUNAKAN MODEL VARIASI KALENDER DAN DETEKSI OUTLIER (Studi Kasus di Bandara Soekarno-Hatta)
Author(s) -
Alvi Waldira,
Abdul Hoyyi,
Dwi Ispriyanti
Publication year - 2020
Publication title -
jurnal gaussian : jurnal statistika undip
Language(s) - English
Resource type - Journals
ISSN - 2339-2541
DOI - 10.14710/j.gauss.v9i3.28914
Subject(s) - outlier , variable (mathematics) , autoregressive integrated moving average , statistics , variation (astronomy) , variables , anomaly (physics) , econometrics , anomaly detection , value (mathematics) , computer science , mathematics , operations research , time series , data mining , physics , mathematical analysis , astrophysics , condensed matter physics
Transportation has a strategic role, even becoming one of the main needs of the community, especially air transportation services. A large number of passengers in air transportation always experiences a difference every month. One of the differences occurred when approaching Eid al-Fitr, which changes every year based on an Islamic calendar that is different from Masehi calendar. The lunar shift in the occurrence of Eid al-Fitr forms a pattern called calendar variation. The effects of calendar variations can be overcome by using an additional variable, such as a dummy variable, this variable which will be used in the ARIMAX model. Observation of time series is often influenced by several unexpected events such as outliers. This outlier causes the results of data analysis to be less valid. So the researchers added the detection of outliers in this study. Based on the analysis results, the ARIMA calendar variation model is obtained (1.0, [12]), with time variable t, dummy variable , and the addition of one outlier. This model has a MAPE value of 0.07079609 which means this model is very good for forecasting. Forecasting results showed an increase in the number of passengers during the two months before Eid. Keywords: Passenger, calendar variation, outlier detection