PREDIKSI TINGGI PASANG AIR LAUT DI KOTA SEMARANG DENGAN MENGGUNAKAN METODE SEASONAL AUTOREGRESSIVE INTEGRATED MOVING AVERAGE (SARIMA) DAN DETEKSI OUTLIER

SA’ADAH, ALFI FARIDATUS (2014) PREDIKSI TINGGI PASANG AIR LAUT DI KOTA SEMARANG DENGAN MENGGUNAKAN METODE SEASONAL AUTOREGRESSIVE INTEGRATED MOVING AVERAGE (SARIMA) DAN DETEKSI OUTLIER. Undergraduate thesis, FSM Undip.

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Abstract

Semarang as the capital of the province of Central Java is a central transportation that has a high intensity and strategic activities. However, this area has a tidal disaster threat level is high enough. Tidal flood is a natural event or phenomenon where sea water entered the land area when the sea level has getting tides. Tidal flood left many losses such as damaged buildings, reduced incomes, and increased expenditure of public sector. In the future impact of tidal inundation in Semarang city is predicted to be greater by a factor assuming sea level and land subsidence increased so that has needed the forecasting of high tide. The method is often used in forecasting ARIMA method. However, the data pairs tend to experience seasonal monthly. In the time series data sometimes the data contained outliers that may affect the suitability of the model. So that forecasting method is needed that is able to accommodate have seasonal characteristics and outlier is used the Seasonal Autoregressive Integrated Moving Average ( SARIMA ) and outlier detection method. For outlier detection, there are four types of outliers are additive outlier ( AO ), innovational outlier ( IO ), level shift ( LS ) and temporary change ( TC ). The study was conducted on the data of tide in Semarang period January 2004 - December 2012 based on the average high tide occurs when the maximum. The results of research showed that the model SARIMA with 7 outliers result predictions with high accuracy because it has a smaller AIC value is 649,1083 compared to the SARIMA models without outlier is 705,6404. Keywords : Tides, SARIMA, outlier detection.

Item Type:Thesis (Undergraduate)
Subjects:H Social Sciences > HA Statistics
Divisions:Faculty of Science and Mathematics > Department of Statistics
ID Code:43501
Deposited By:Mr Hasbi Yasin
Deposited On:19 Aug 2014 10:44
Last Modified:19 Aug 2014 10:44

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