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Empowerment of fishery resources requires analysis of forecasting results as an effort to maintain sustainability and human needs. Forecasting is an approach to predicting based on past facts, which is expected to be used as a decision support system. However, there are problems with the accuracy. In this study, we compare the regression method with ARIMA to find out which method can be used as the most appropriate choice in fisheries forecasting with physical benchmarks (seasonal and climate). We conduct a systematic literature study on various studies with the theme of fisheries forecasting. The search focuses on studies with the main criteria in the form of an explicit discussion of the basic forecasting methods and literature on marine physical influences. Then define a search method by combining fishery stocks or landings against forecasting with marine physical features optionally using PRISMA. The results show that the ARIMA models have a better fair value and accuracy than the regression because the ARIMA model captures the history of data autocorrelation and extrapolates it to the forecasting framework that will be carried out. Hence, it is most suitable for use with additional marine physical variability.

Pandeglang Regency is an area prone to earthquakes because in the subduction zone of the Indo-Australian plate and the Eurasian plate in the Sunda Strait. This study analyses the average and maximum values of the Peak Ground Acceleration (PGA) got from earthquakes in the period 2000 to 2020 around Pandeglang Regency with the calculation method. Gutenberg-Richter with the Richter formula combined with the intensity attenuation formula which is a function of magnitude, epicentre distance, and earthquake intensity. From the maximum PGA data, the lowest value is 273 gals, and the highest is 427.5 gals. Meanwhile, for the distribution of the average PGA value, the minimum value is 54 gals, and the maximum value is 67 gals. From the PGA data, the maximum distribution of the highest PGA value is in the center of Pandeglang Regency, and the lowest value of the PGA maximum is in the northern and southern parts of Pandeglang Regency. The PGA value is potentially damaging at the VII MMI scale, where the area with the highest vulnerability is in the sub-district Angsana, Munjul, Panimbang, and Sidangresmi with the highest PGA scores, so this sub-district has the highest risk than other districts.