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Elohansen Padang
Jurusan Fisika FMIPA UNIPA
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RANCANG BANGUN ALAT UKUR KETINGGIAN PERMUKAAN AIR SUNGAI MENGGUNAKAN SENSOR ULTRASONIK AJ-SR04M Yasir Abdan Syakur; Elohansen Padang; Baina Afkril
Jurnal Natural Vol. 19 No. 1 (2023): Jurnal Natural
Publisher : FMIPA Universitas Papua

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30862/jn.v19i1.231

Abstract

Monitoring of water levels provides many benefits in various fields of application such as flood disaster mitigation, irrigation management, and dam management. Research has been carried out on the design and testing of a river water level measuring instrument based on the AJ-SR04M ultrasonic sensor. This ultrasonic sensor has several advantages, including accurate measurement results, low costs, can be found easily on the market, simple, and waterproof making it suitable for river water level measurement applications. The developed measuring instrument is also equipped with temperature and humidity sensors as a correction factor for the speed of sound, because these two parameters affect the speed of sound. The Arduino Uno board is used as a signal processing unit in charge of processing all output signals from sensors. The micro SDcard module is used as a container for storing measurement data. Meanwhile, the LCD board is used to display the measurement results. The Arduino Uno board is programmed using the Arduino IDE software. The test results show that the instrument has a very good level of accuracy. This is evidenced by the results of testing the devices which showed a relative error of 0.066%. In addition, the tool also functions with very good precision where the correlation coefficient (R2) obtained from testing the instrument is 0.98.
MODEL PREDIKSI SUHU PERMUKAAN LAUT PERAIRAN KO-TA MANOKWARI KABUPATEN MANOKWARI MENGGUNAKAN DEEP NEURAL NETWORKS Elohansen Padang
Jurnal Natural Vol. 19 No. 1 (2023): Jurnal Natural
Publisher : FMIPA Universitas Papua

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30862/jn.v19i1.232

Abstract

In this research, we have been done developed a prediction model for Sea Surface Temperature (SST) in the sea of Manokwari City, Manokwari Regency using deep learning models, especially the Deep Neural Networks (DNN) model. The SST data used is ERA5 reanalysis data provided by the European Center for Medium-Range Weather Forecast (ECMWF) from 2000-2021 (8233 daily data). The SST data is divided into two parts, namely training and testing data with a proportion of 90% and 10%, respectively. The DNN model developed uses the hyperparameter optimizer adam, the ReLu activation function, the learning rate is 0.01, the Batch size is 30, the number of inputs is 10, the number of epochs is 100 and is equipped with early stops. Meanwhile, the number of hidden layers varied between 1 until 4. Likewise, the number of neurons in each hidden layer varies from 8, 16, 32, 64, or 128 neurons. Based on the test results, the DNN model with 2 hidden layers and 32 neurons per hidden layer gives more accurate results than the other models, with RMSE, MAE, and R2 values respectively 0.121; 0.015; and 0.935. Therefore, this DNN model can be recommended as a model to predict SST in sea of Manokwari City.
Co-Authors Baina Afkril Yasir Abdan Syakur