Whiteleg shrimp (Litopenaeus vannamei) is one of the important aquaculture commodities. This is due to high demand and farming with high stock densities. However, whiteleg shrimp farming is generally large scale, so that farmers with small capital cannot carry out business activities. In this paper, an intensive assessment of four whiteleg shrimp technologies has been carried out with seven criteria. Four intensive shrimp farming technologies are biofloc technology, supra intensive, shrimp farming in mini scale with tarpaulin (BUSMETIK), and Recirculating Aquaculture System (RAS). Seven criteria that used in the assessment are affordability of capital and operational costs on a mini scale, minimum land area for shrimp farming, dependence on the location of raw water sources, environmental friendly, productivity, energy consumption, and biosecurity. The assessment was carried out using the Analytical Hierarchy Process (AHP) method to assess the compatibility of different technologies to low investment. The results of the AHP method show that RAS is ranked first for intensive shrimp farming technology for small farmers. Then followed by supra intensive, BUSMETIK and biofloc technology. Most of the criteria used in the AHP calculation are the advantages that RAS is very suitable for small farmers.

Pekalongan waters, a part of the Java Sea, has potency to develop marine fisheries sector to increase regional income and community livelihoods. The fluctuation of marine fish production every year requires serious attention in planning and policy strategies for the utilization of the fishery resources. Time series fish production data can be used to predict fish production in the following years through the forecasting process. The data used in this study is fish production data from Pekalongan Fishing Port, Central Java, from January 2011 to December 2020. The method used is data exponential smoothing by comparing three exponential smoothing methods consisting of single/simple exponential smoothing, double exponential smoothing and Holt-Winters’ exponential smoothing. The criterion that used to measure the forecasting performance is the mean absolute percentage error (MAPE) value. The smaller MAPE value shows the better the forecasting result. The smallest MAPE value is obtained by finding the optimal smoothing constant value which is usually calculated using the trial and error method. However, in this study, the constant value was calculated using the add-in solver approach in Microsoft Excel. The forecasting results obtained show that forecasting using the Holt Winter Exponential Smoothing method is reasonable with a MAPE value of 37.878.

Spiny lobster nursery is done to produce more adaptive and uniform juvenile lobsters quality. Shelters used in spiny lobster nursery served to reduce physical contact among lobsters in the rearing tank. The purpose of this study was to analyze the effect of different shelter types on physiological response and growth of spiny lobster (Panulirus homarus) juvenile rearing in recirculating aquaculture systems. Lobsters with an average weight of 50.07 ± 2.89 g were reared for 60 days. They were fed once a day with trash fish. The daily feeding rate was 3-4% of total weight. This study used four types of shelter as treatments with two replications. PVC pipe shelter as control (K), individual shelter square shaped (IS ■), individual shelter triangle shaped (IS ▲), and individual shelter tube shaped (IS ●). The weight and length of the lobster carapace improved with the duration of the research in all treatments. Throughout the trial, glucose levels in controls were generally greater than those in specific shelf treatments. The reaction of lobster hemolymph total protein to different shelters is highly variable. Overall, the usage of individual shelters had a considerable positive influence on grown lobsters in this study. This is because individual shelter eliminates contact between lobsters, eliminating the possibility of cannibalism in the cultivation container. This study concludes that IS ■ used in rearing Panulirus homarus showed a lower stress response than the other treatments in terms of glucose and total protein lobster hemolymph during the study. IS ■ is the best because it reduced stress levels and yielded better total biomass among the other treatments.

Cirata Reservoir is a place for fish cultivators who mostly cultivate tilapia using floating net cages. However, water quality conditions, especially dissolved oxygen levels, which play an important role in tilapia culture in floating net cages are always uncertain and affect the growth of tilapia. Therefore, artificial aeration is needed that is able to increase dissolved oxygen levels so that it is suitable for tilapia culture by using Aearator Dua Lapis (ADL) engine. This study aims to inject dissolved oxygen into the surface layer of the reservoir by applying the ADL engine with gasoline. ADL operated at 1800, 4500, and 5500 rpm with a torque of 3.5 N/m2 and the DO value is recorded every 10 minutes up to 1440 minutes, the results of DO value were recorded and analyzed by using descriptive statistic and statistically using ANOVA with a single factor showed that the rotation has a very significant effect on the resulting DO value (p<0.01). For ADL operation in floating net cages, 4500 rpm rotation was used at certain depth (0.4 m; 1 m; 1.5 m) and the DO value measured at 4.00 am to 2.00 am (22 hours) in aerated floating net cages and without aerated floating net cages.Aerated floating net cages have a higher DO value, especially at a depth of 0.4 m. ADL as an aerator in floating net cages has a very significant effect (p<0.01) in increasing the DO value in floating net cages at a depth of 40 cm to 1.5 m. This proved that the use of ADL is able to increase the DO value in floating net cages in the first layer with a depth of up to 4 m and ADL also can be used as emergency aeration or supplemental aeration for tilapia culture.

Besides minimizing environmental impact, one of the goals of ecological intensification for aquaculture is production. Production forecasting is needed to make policies in planning, especially in terms of meeting consumer demand. This paper introduces a method to forecast the total shrimp production for Litopenaeus vannamei and Penaeus Monodon in Indramayu Regency using artificial neural networks. In this case, we used backpropagation neural networks (BPNN). BPNN is a supervised learning algorithm and usually used by perceptron with many layers to change the weights associated with the neurons in the hidden layers. During the training process, the network calculated the output that will be generated based on the given input patterns. The network assigned and adjusted the weights of the input and also the hidden layer to obtain a network with good performance. Networks with small error values close to zero indicate good performance. The criteria used to test the performance of the artificial neural networks method are the root mean squared error (RMSE), the mean absolute percentage error (MAPE), and the correlation coefficient (r). Production data obtained from the relevant government agencies were used to train the algorithms as a part of an artificial intelligence process. This artificial intelligence forecasted the shrimp’s harvest. Forecasting performance is indicated by the accuracy of the prediction process data compared to the real data. The best result for L. vannamei forecasting was obtained in the trainGD with MSE 0.0174 and MAPE 19.28%. The best results P. monodon forecasting were obtained in the TrainRP with MSE 0.0200 and MAPE 22.99%.