FORECASTING MONTHLY NAUTICAL CHART TIME SERIES OF JAVA AREA USING ARIMA METHOD (Case study: Primkopal Pushidrosal Sales Units)

Benny Rushadi, Sulistyo Puspitodjati

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.

Keywords

Forecasting, Nautical chart, ARIMA.

References

Pusat Hidrografi dan Oseanografi TNI Angkatan Laut. (2019). Sejarah Pushidrosal sebagai lembaga Hidrografi Nasional Indonesia. Pushidrosal. Jakarta.

NOAA, What is a nautical chart?, NOAA, online: https://oceanservice.noaa.gov/facts/nautical_chart.html Diakses 30 November 2019.

Peraturan Presiden No.5 Tahun 2010 tentang Kenavigasian.

Pusat Hidrografi dan Oseanografi TNI Angkatan Laut. (2017). Nautical charts and publications catalogue. Pushidrosal. Jakarta.

Shrivastav, A.K., dan Ekata. (2014). Applicability of Box Jenkins ARIMA Model in Crime Forecasting: A case study of counterfeiting in Gujarat State. International Journal of Advanced Research in Computer Engineering & Technology, 494-497.

Putri, N.I., Wirawan, A.A., Khofiyah, N.A., Yuniaristanto. (2018). Peramalan Kebutuhan Jumlah Vaksin Imunisasi Campak Dengan Menggunakan Metode ARIMA: Studi Kasus. Seminar dan Konferensi Nasional IDEC 2018 Surakarta, 7-8 Mei 2018, ISSN:2579-6429.

Sari, D.R., Sugito dan Ispriyanti, D. (2015). ‘Peramalan Indeks Harga Konsumen Menggunakan Model Intervensi Fungsi STEP’, Jurnal Gaussian, vol.4, no.4, ISSN:2339-254, 795-804.

Iriawan, N. dan Astuti, S.P. (2006). Mengolah Data Statistik dengan Mudah Menggunakan Minitab 14. Penerbit Andi. Yogyakarta.

Muis, S. (2008). Meramal Pergerakan Harga Saham Menggunakan Pendekatan Model ARIMA, Indeks Tunggal & Markowitz. Graha Ilmu. Yogyakarta.

Makridakis, S., Wheelwright, S.C., and McGee, V.E. Metode dan Aplikasi Peramalan jilid I (Edisi Revisi). Bina Rupa Aksara. Jakarta.

Article Metrics

Abstract view: 279 times
Download     : 130   times

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Refbacks

  • There are currently no refbacks.