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International Journal of Applied Sciences and Smart Technologies
Published by Universitas Sanata Dharma
ISSN : 26558564     EISSN : 26859432     DOI : http://dx.doi.org/10.24071/ijasst
Core Subject : Science, Engineering,
Computer Science & IT Energy Engineering Industrial & Manufacturing Engineering
International Journal of Applied Sciences and Smart Technologies (IJASST) is published by Faculty of Science and Technology, Sanata Dharma University Yogyakarta-Central Java-Indonesia. IJASST is an open-access peer reviewed journal that mediates the dissemination of academicians, researchers, and practitioners in engineering, science, technology, and basic sciences which relate to technology including applied mathematics, physics, and chemistry. IJASST accepts submission from all over the world, especially from Indonesia.
Arjuna Subject : Umum - Umum
Articles 183 Documents
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Mini Solar Power Resources for IoT system in the Vannamei Shrimp Pond Model Widjaja, Damar; Seli Laka, Yohanes Priyanto
International Journal of Applied Sciences and Smart Technologies Volume 07, Issue 2, December 2025
Publisher : Universitas Sanata Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24071/ijasst.v7i2.10452

Abstract

Shrimp pond water quality is one of the challenging issues for shrimp farmer to keep or even increase their production level to meet the domestic and export needs. Many researches have been done to help shrimp farmer to manage water quality using Internet of Things (IoT) technologies. In order to make all the devices in the IoT system work properly, it needs an adequate power supply. Mini solar power plant installation is an alternative way to give shrimp farmers an electricity power access when their area has no electricity power network. In this study, we propose the use of mini solar power plants to supply the power to IoT devices in shrimp pond model. There are two main sub-system in this model, i.e. power supply sub-system and IoT based monitoring and controlling sub-system. Power supply sub-system consist of solar panel, solar cell controller, battery, INA219 sensor, and LM2596 step down IC. IoT sub-system perform monitoring and controlling on two shrimp pond models with Arduino Mega microcontroller works as the main processor. The mini solar power system works well as it was designed. Mini solar power plant capable of charging the 12V 40Ah battery in 5 hours. In order to make the IoT system works, it only needs 75,6 Wh from 480Wh battery capacity.
Design of a Speed Sensorless Control System on a DC Motor using a PID Controller Harini, Bernadeta Wuri; Gunadi, Theodore Galeno; Mahardika, Shakuntala Gema; Pangestu, Sirilus Praditya; Putri, Regina Chelinia Erianda; Mardikus, Stefan
International Journal of Applied Sciences and Smart Technologies Volume 07, Issue 2, December 2025
Publisher : Universitas Sanata Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24071/ijasst.v7i2.13271

Abstract

The speed sensorless control system on a DC motor is a DC motor speed control system that does not use a speed sensor to measure the motor speed. The motor speed value is estimated by an observer from the stator current and voltage that are measured using a sensor. This study uses an R observer method.  The difference between the estimated speed and the reference speed is then used by the PID controller to adjust the motor speed to match the desired reference speed. PID parameter tuning using heuristic method. With a setpoint of 6800 RPM and using a combination of Kp = 0.5, Ki = 0.05, and Kd = 0.34, a speed value of 6792.76 RPM was obtained. Sensorless motor speed control using a PID controller produces an optimal system with a low Steady State Error (SSE) value of around 0.1%, very small oscillations of 0.39%, a fast rise time of 4 seconds, and a fast settling time of 6 seconds.
Improving the Accuracy of Prediction of Dissolved Oxygen and Nitrate Level Using LSTM with K-Means Clustering and Spearman Analysis Arshella, Ika Arva; Mustika, I Wayan; Nugroho, Prapto
International Journal of Applied Sciences and Smart Technologies Volume 07, Issue 2, December 2025
Publisher : Universitas Sanata Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24071/ijasst.v7i2.12361

Abstract

This study discusses how to prepare data properly before entering the learning process for prediction using Deep Learning (DL). Long Short-Term Memory (LSTM) is one of the DL methods that is often used for prediction because of its superiority in maintaining long-term information. Although LSTM has proven effective, there are issues related to low-quality data that can reduce prediction accuracy. This problem is important to discuss because accuracy is important in predicting a value while field conditions can reduce the quality of the data taken. Data merging based on the relationship of each data collection location using the Spearman analysis and the K-Means clustering method is used to improve data quality. The results of the study show that improving data quality by merging data using K-Means has been successfully applied to various dataset conditions. In this study, we used two types of datasets related to river water quality, namely Dissolved Oxygen (DO) concentration and Nitrate levels for our simulation. The first data set produced DO predictions for eight locations with an average R2 = 0.9998, MAE = 0.0007, MSE = 1,13×10-6. The second data set produced nitrate predictions for ten locations with an average R2 = 0.7337, MAE = 0.0111, MSE = 0,00029

Page 19 of 19 | Total Record : 183

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