Open AccessAnalysis of Port Fare Increases On Container Yard Services Using Logistic Regression Model
Author(s) -
Sarika Zuhri,
Prima Denny Sentia,
Nuraini Lubis,
Syarifah Diana Permai
Publication year - 2019
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/697/1/012003
Subject(s) - yard , container (type theory) , port (circuit theory) , payment , logistic regression , regression analysis , operations research , transport engineering , business , operations management , computer science , engineering , mathematics , statistics , finance , mechanical engineering , physics , electrical engineering , quantum mechanics
The port company used in this research is the only port where container ship contained in Aceh and has been operating for more than 1 year. The problem with this was the large buildup of containers with long periods of time in the cultivation field that would cause losses. Losses due to the duration of the buildup are influenced by several factors, such as the yard of ratio and the length of time the container is reviewed based on the large number of containers in the container yard. For payment of the first five days of service, the container accumulation fare shall be borne by container owner company as stakeholder of port company. After more than five days, the customer will be charged the container accumulation rate calculated per box without any time consideration. This causes the customers don’t take the goods immediately, therefore it was necessary to do research on the analysis of port fare increase in port company in Aceh using logistic regression model as the basic of decision making. Based on the prediction of logistic regression model using software R3.4.3, there was a loss cost in the container yard, therefore it was necessary to take decision of the application of the port fare increase on container yard service. Based on the results of these predictions, the resulting model was a good model with an accuracy of 74.8%. The factor that affect of the loss cost was the length of time the container in the container yard period II and III.