3 research outputs found
Water Quality Monitoring System with Parameter of pH, Temperature, Turbidity, and Salinity Based on Internet of Things
This research aims to monitor the quality of water used for aquariums. The physical parameters used are water pH, water temperature, water turbidity, and water salinity. Using a pH sensor, temperature sensor, turbidity sensor, and salinity conductivity sensor with Arduino as the controller. The prototype method used in this research, starting from the formulation, research, building stages to testing and evaluating the results of the research. The working process of the system is when the system is activated, the sensors will detect and capture the amount of value contained in the water, then the data from the sensor is sent to a database in the cloud using an ethernet shield that is connected to the media router as a liaison for the internet network then displayed on the website dashboard in the form of graphs and monitoring record tables in real time. The sensors function to detect water quality, where quality standards have been set in this system, namely temperature standards of 27-30°C, pH standards of 7.0-8.0, turbidity standards of 2.5-5 ntu, and salinity of 20-28 ppt. If the sensor detects non-compliance with water quality standards, the buzzer in this system will sound. From the results of system testing, sensors can detect water quality in real time within 5-10 seconds. Based on the research results, this water quality monitoring system is effective to help ensure the quality of the water in the aquarium so that it always meets the standards
PERBANDINGAN ALGORITMA LINEAR REGRESSION, LSTM, DAN GRU DALAM MEMPREDIKSI HARGA SAHAM DENGAN MODEL TIME SERIES
Penelitian ini bertujuan untuk memprediksi harga saham dengan membandingkan algoritma Linear Regression, Long Short-Term Memory (LSTM), dan Gated Recurrent Unit (GRU) dengan dataset publik kemudian menentukan performa terbaik dari ketiga algoritma tersebut. Dataset yang diuji bersumber dari Indonesia Stock Exchange (IDX), yaitu dataset harga saham KEJU berbentuk time series dari tanggal 15 November 2019 sampai dengan 08 Juni 2021. Parameter yang digunakan untuk pengukuran perbandingan adalah RMSE (Root Mean Square Error), MSE (Mean Square Error), dan MAE (Mean Absolute Error). Setelah dilakukan proses training dan testing, dihasilkan sebuah analisis bahwa dari hasil perbandingan algoritma yang digunakan, algoritma Gated Recurrent Unit (GRU) memiliki performance paling baik dibandingkan Linear Regression dan Long-Short Term Memory (LSTM) dalam hal memprediksi harga saham, dibuktikan dengan nilai RMSE, MSE, dan MAE dari uji coba GRU paling rendah, yaitu nilai RMSE 0.034, MSE 0.001, dan nilai MAE 0.024
Water Quality Monitoring System with Parameter of PH, Temperature, Turbidity, and Salinity Based on Internet of Things
This research aims to monitor the quality of water used for aquariums. The physical parameters used are water pH, water temperature, water turbidity, and water salinity. Using a pH sensor, temperature sensor, turbidity sensor, and salinity conductivity sensor with Arduino as the controller. The prototype method used in this research, starting from the formulation, research, building stages to testing and evaluating the results of the research. The working process of the system is when the system is activated, the sensors will detect and capture the amount of value contained in the water, then the data from the sensor is sent to a database in the cloud using an ethernet shield that is connected to the media router as a liaison for the internet network then displayed on the website dashboard in the form of graphs and monitoring record tables in real time. The sensors function to detect water quality, where quality standards have been set in this system, namely temperature standards of 27-30°C, pH standards of 7.0-8.0, turbidity standards of 2.5-5 ntu, and salinity of 20-28 ppt. If the sensor detects non-compliance with water quality standards, the buzzer in this system will sound. From the results of system testing, sensors can detect water quality in real time within 5-10 seconds. Based on the research results, this water quality monitoring system is effective to help ensure the quality of the water in the aquarium so that it always meets the standards