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All Journal Andalasian International Journal of Applied Science, Engineering, and Technology Jurnal Ilmu Komputer dan Sistem Informasi (JIKSI)
Armin Armin
Politeknik Negeri Balikpapan, Indonesia
Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Energy Industrial & Manufacturing Engineering Mechanical Engineering Social Sciences

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PLC–HMI integration for water tank control and monitoring system with control performance evaluation industrial Armin Armin; Ihsan Ihsan; Muhammad Bahit; Nur Yanti; Mikail Eko Prasetyo Widagda; Ferdy Kahandanie; Endang Sri Apriana; Noprianto Noprianto
Jurnal Ilmu Komputer dan Sistem Informasi |JIKSI| Vol. 7 No. 01 (2026): Jurnal Ilmu Komputer dan Sistem Informasi (JIKSI)
Publisher : Institute of Information Technology and Social Science (IITSS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61346/jiksi.v7i01.327

Abstract

This research designs and implements a water tank control and monitoring system based on a Programmable Logic Controller (PLC) integrated with a Human-Machine Interface (HMI) to enhance operational efficiency and reliability in an industrial environment. The system is built using a Schneider M221 PLC and analogue modules, with a level transmitter as the primary sensor for level control, and flow and temperature transmitters to complement process condition information. The control logic is implemented using ladder logic in EcoStruxure Machine Expert. At the same time, the HMI interface is developed in Vijeo Designer for real-time visualization, setpoint adjustment, and pump status monitoring and alarms. System performance is evaluated through hardware functional testing (power supply, relay, pump) and sensor testing based on 4–20 mA signals converted using a scaling Equation. To further assess control performance, a dynamic response test, more specifically, a step response, was employed. From a starting level of 10 cm to a setpoint of 25 cm, data was recorded every two seconds. The study includes critical performance metrics such as rising time, settling time (within ±5% tolerance), overshoot, and steady-state error. With a rising time of 9.22 seconds, a settling time of 14 seconds, an overshoot of 2.0%, and no steady-state error, the data showed that the system steadily approached the intended setpoint. These findings demonstrate that integrating a PLC and HMI provides reliable water tank level monitoring and control and is suitable for use as an automation solution in industrial settings.
Analysis of Public Sentiment Towards the Prabowo-Gibran Administration Using Deep Neural Network Algorithms Kartika Wahyuningsih; Adib Ahmad Istiqlal; Armin Armin
Andalasian International Journal of Applied Science, Engineering and Technology Vol. 6 No. 1 (2026): March 2026
Publisher : LPPM Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/aijaset.v6i1.257

Abstract

The leadership of Prabowo and Gibran has drawn significant public attention, particularly in the early phase of the administration. In the digital era, public opinions both supportive and critical are widely expressed through platforms such as YouTube, generating large volumes of data. Manual analysis of this data is impractical, while previous machine learning and deep learning approaches have produced suboptimal results. Therefore, this study applies a Deep Neural Network (DNN) to analyze public sentiment more effectively. The process involves inputting a dataset derived from YouTube comments obtained through scraping using the v3 API. This is followed by preprocessing, labeling, data splitting, applying IndoBERT word embedding, and DNN, with the final step being evaluation using a confusion matrix. The results of this study indicate that as the depth of the hidden layers increases, the accuracy improves, though not significantly. The best model performance was obtained by without SMOTE using a depth of 6 hidden layers, with an accuracy of 68.84%, precision of 67.5%, recall of 68.84%, and an F1 score of 64.22%.
Co-Authors Adib Ahmad Istiqlal Endang Sri Apriana Ferdy Kahandanie Ihsan Ihsan Kartika Wahyuningsih Mikail Eko Prasetyo Widagda Muhammad Bahit Noprianto Noprianto Nur Yanti