In Langsa, fisheries are the sector leaders by fulfilling a capacity of about 6,050 tons per year and on the other hand, fish-aquaculture reaches 1,200 tons per year on average. The fish processing is conducted through catches and aquaculture. The facilities on which this processing takes place are divided into an ice factory unit, a gutting and cutting unit, a drying unit and a curing unit. However, the energy and electricity costs during the production process has become major constraint because of the increase in the fishermen's production and income. In this study, the potential and cost-effectiveness of photovoltaic solar power plant to meet the energy demands of fish processing units have been analysed. The energy requirements of fish processing units have reached an estimate of 130 kW, while the proposed design of solar photovoltaic electricity generation is of 200 kW in an area of 0,75 hectares. In this analysis, given the closeness between the location of the processing units and the fish supply auctions, the assumption is made that the photovoltaic plants (OTR) were installed on the roof of the building as compared to the solar power plants (OTL) installed on the outside of the location. The results shows that the levelized cost of OTR instalation is IDR 1.115 per kWh, considering 25 years of plant life-span at 10% of discount rate, with a simple payback period of 13.2 years. OTL levelized energy, on the other hand, is at IDR 997.5 per kWh with a simple payback period of 9.6 years. Blood is an essential component of living creatures in the vascular space. For possible disease identification, it can be tested through a blood test, one of which can be seen from the form of red blood cells. The normal and abnormal morphology of the red blood cells of a patient is very helpful to doctors in detecting a disease. With the advancement of digital image processing technology can be used to identify normal and abnormal blood cells of a patient. This research used self-organizing map method to classify the normal and abnormal form of red blood cells in the digital image. The use of self-organizing map neural network method can be implemented to classify the normal and abnormal form of red blood cells in the input image with 93,78% accuracy testing.
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