Forecasting Fuel Logistics Demand in Archipelagic Regions Using the ARIMA Methods: A Case Study of the Anambas Islands Regency - Eka Suswaini; Mochammad Agung Wibowo; Ferry Jie (ISB Atma Luhur, 2026)
Indonesia as an archipelago faces complex logistical challenges, especially in the distribution of fuel oil (BBM) to remote areas. This research aims to forecast fuel logistics needs in the Anambas Islands Regency using the Autoregressive Integrated Moving Average (ARIMA) method. Forecasting was carried out on three main aspects: fuel demand (Type 1 and Type 2) per sub-district, sea wave height, and number of vehicles by type. The results show that the three elements have a relatively stable pattern during the forecasting period until June 2025, with the dominant ARIMA model configurations (0,1,0) and (0,1,1). In these configurations, p = 0 indicates no autoregressive component, d = 1 represents first differencing to remove trends, and q = 0 or 1 reflects either the absence or presence of a simple moving-average term to smooth short-term fluctuations. These parameter combinations suggest that the time series data exhibit stable linear trends with limited short-term volatility. Fuel demand per sub-district shows a steady trend, sea waves are in the low to medium category, and the number of vehicles does not experience significant spikes. This stability supports efficient and predictive data-based fuel distribution planning. The research also recommends the integration of forecasting results into the development of an adaptive and sustainable decision-making system in the islands.