Prediction of Freshwater Fish Disease Severity Based on Fuzzy Logic Approach, Arduino IDE and Proteus ISIS

EasyChair Preprint 10522

Abstract

This study examines the importance of maintaining the health quality of freshwater fish to increase aquaculture yields. Fish diseases such as Ichthyophthirius, Saprolegniasis, and Columnaris are often a serious threat to freshwater fish farmers. The purpose of this study was to predict the severity of fish disease and diagnosis of freshwater fish disease by calculating the inputs of water clarity, water temperature, oxygen levels, mild disease, moderate disease, and severe disease. The fuzzy logic method (fuzzy membership function, fuzzy rule base, defuzzification) is used to predict the output variable in the form of disease severity and disease diagnosis. The simulation of the fuzzy logic approach through analytical and computational calculations (MATLAB software) produces variable output values ​​(disease severity and disease diagnosis values) that are exactly the same as the results of programming simulations based on Arduino IDE and Proteus ISIS. This shows that the integration of the two simulations has been going well. The results of the simulation measurements are displayed on the LCD display by displaying the variable values

Keyphrases: Arduino IDE, Disease Diagnosis, Freshwater fish disease severity, Fuzzy Logic, MATLAB

@booklet{EasyChair:10522,
  author    = {Ridwan Siskandar and Wiyoto Wiyoto and Sesar Husen Santosa and Agung Prayudha Hidayat and Billi Rifa Kusumah and Muhammad Danang Mukti Darmawan},
  title     = {Prediction of Freshwater Fish Disease Severity Based on Fuzzy Logic Approach, Arduino IDE and Proteus ISIS},
  howpublished = {EasyChair Preprint 10522},
  year      = {EasyChair, 2023}}