RETRACTED: Surface Modeling Vehicle for Shrimp Ponds using Artificial Neural Network

Abstract

The worldwide frozen shrimp trade market in 2018 was recorded at the US $ 17.2 billion or around Rp.232.2 trillion. Indonesia is one of the principal exporters of frozen shrimp in the worldwide market. In light of this information, shrimp cultivating is a promising area, yet shrimp cultivating is particularly controlled by water quality. Water quality in shrimp cultivating consistently changes. Many variables impact changes in water quality (shrimp biomass, PH, and temperature). These components in the water should be inside the standard limit rank. Thusly, to accomplish the creation effectiveness of the shrimp business, it is important to robotize water quality control. This study aims to develop Surface Modeling Vehicles (SMV) for Shrimp Ponds utilizing an Artificial Neural Network. The test outcomes show an exactness pace of 94%, the expectation is that the instruments created by the exploration will want to acknowledge exact Vename Shrimp cultivating, so creation proficiency and expanded shrimp creation can be accomplished.

Development of Automatic Pond Reservoir System to Support Automatic Surface Vehicle

“…Besides being suitable for fuzzy partial data, it is also suitable for monitoring needs. Fuzzy in this study will be used to acquire data from sensors on previously developed tools [6] [7], to then refine the rules to control the valve (pH and water flow) and PWM for the waterwheel.…”

“…This research is based on [5] aims to develop Automatic Pond Reservoir which is integrated with previous research on Realtime Surface Modeling Vehicle Tambak Shrimp (ReSMeV-TU) [6] [7]. The system built in this study implements fuzzy logic control on the Arduino for the classification process IOP Publishing doi:10.1088/1755-1315/1168/1/012052 2 and triggers the Pond Reservoir to ensure the water remains in optimal conditions for shrimp growth and development, while the shrimp used in this study is the Vaname shrimp.…”