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STUDI MODEL PERMINTAAN PERJALANAN PENUMPANG KAPAL DARI/KE SABANG

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ABSTRACT Sabang as one of the national tourist destination in the province of Aceh promising lots of charm, such as beautiful beaches and amazing panoramic sea floor. Determination of Sabang as a tourist destination should be supported by an adequate fleet both in quality and quantity. Trip from Banda Aceh to Sabang served by 2 (two) kind of ships, namely the slow boat owned by the government (KMP BRR) and privately owned speedboat (KM Express Bahari 3B). Number of trips to Sabang has increased very rapidly in 2013, so 1 (one) fleet of fast boat (Express Bahari 9) being served as an addition. Although there have been three (3) fleet sail this route, but passengers often can not be served because of the number of passengers exceeds the capacity. Therefore in this study we want to know (1) the factors that affect the demand for ship passengers from / to Sabang and (2) formulating a model through regression analysis using time series model and causal model. Time series model calculated based on secondary data for the last 5 years, ie: from 2010 - 2014. Causal Model processed based on primary data using questionnaires result. From Time Series model obtained equation to model the trip slow boat Banda Aceh - Sabang and vice versa, ie: Q = 172,796.640. e 0,00002104 (p) . As for the fast boats trip model from Banda Aceh - Sabang obtained the equation Q = 1,592E1914.e 0,227 (p) and for models on trip from Sabang - Banda Aceh obtained the equation Q = 3,115E170.e 0,199 (p) . Causal model processed using SPSS 18.0 software with the best model based on criteria R and R 2 , taking into account the significant F-Test and t-Test. The Causal model of Banda Aceh - Sabang passenger trip demand is Y = 3,134 - 0,250 X 9 (traveling time) + 0,203 X 13 (ship mode used) - 0,166 X 6 (population) and for model of passenger request of Sabang - Banda Aceh, Y = 1,450 + 0,437 X 13 (ship mode used) - 0,446 X 1 (age) + 0,255 X 5 (income) + 0,402 X 12 (read / hear Sabang promotion). Keywords: passenger demand, time series models, causal models, regression analysis

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