DEVELOPMENT OF ALGORITHM FOR WATER QUALITY MONITORING BASED ON SMARTPHONE IN VANAME SHRIMP PONDS, SUBANG REGENCY

The traditional method of water quality monitoring involves steps such as sample collection, transportation, laboratory testing, and data analysis, which are time-consuming and resourceintensive. These limitations have led to the need for more efficient and real-time monitoring approaches. Severa...

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Bibliographic Details
Main Author: Salomo Rora, Juan
Format: Final Project
Language:Indonesia
Subjects:
Online Access:https://digilib.itb.ac.id/gdl/view/77548
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Institution: Institut Teknologi Bandung
Language: Indonesia
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Summary:The traditional method of water quality monitoring involves steps such as sample collection, transportation, laboratory testing, and data analysis, which are time-consuming and resourceintensive. These limitations have led to the need for more efficient and real-time monitoring approaches. Several studies have indicated the potential of using smartphone sensors to collect data on parameters such as atmosphere, chlorophyll-a, temperature, and seismic activity. In this context, a simple mobile application like HydroColor can be utilized for crowdsourcing water quality data, equivalent to traditional methods. An empirical model is developed through multiple regression analysis, utilizing the reflectance of RGB wavelengths as independent variables and observation data from the WQM Horiba U-50 device as dependent variables. A total of 48 data parameters of salinity, temperature, and turbidity collected from vaname shrimp ponds in Subang Regency during the third week of June 2023 were utilized in the development process of the empirical model. The empirical model for salinity, using a polynomial order of 4 (Sal = 13.448 – 6.542 (KB) + 15.938 (KB)2 + 150.169 (KB)3 – 192.544 (KB)4 where KB stands for Blue Chromaticity, has proven to be the best-fit model. However, accurate empirical models for turbidity and temperature have not been identified. These findings highlight the potential use of smartphone applications and empirical models to enhance the effectiveness of water quality monitoring.