FORECASTING MONTHLY NAUTICAL CHART TIME SERIES OF JAVA AREA USING ARIMA METHOD (Case study: Primkopal Pushidrosal Sales Units)
Submitted : 2019-12-01, Published : 2020-03-05.
Abstract
Nautical chart is a sea map made from paper and has a function to safety of navigation at sea as it provides vital information such as configuration of the coastline, depth of the sea, seabed nature, hazardous location, height and navigation aids. Sailing at sea and on the waters are not always safe as many navigational hazards threaten at any time, therefore the role of nautical charts is essential. Inaccuracy in estimating the number of nautical chart production has affected the amount of stockpiles in the warehouse, this is very inefficient for sales unit of Primkopal Pushidrosal. Consequently, the making of an appropriate forecasting model to predict the amount of stock in the coming period is expected to overcome these problems. The data which had been used in this research is Indonesian nautical chart sales from January 2012 to March 2019 in the Java area for 87 periods from January 2012 to March 2019 to predict the number of product requests for the coming 8 periods from April to November 2019, as the most densely sea traffic hence nautical charts in this area are the most utilized. The quantitative analysis of the model uses the Integrated Moving Average Autoregressive (ARIMA) method approach where the stages are from identification of model, estimation of parameter and diagnostic tests, because this method is good enough to predict short-term periods and is suitable for predicting the magnitude of any variable in time series data. Processing data uses Minitab 17 software and the results of the research showed that 87 of these periods where the data has not been stationer against mean, so it must be differentiated level 1 in order to be stationary and the ARIMA model is ARIMA(0,1,1) by equation Xt = - 0.551 + Xt − 1 - 0.85et– 1.
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