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All Journal MATICS : Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) ELINVO (Electronics, Informatics, and Vocational Education) Jurnal IPTEK JURNAL DINAMIKA VOKASIONAL TEKNIK MESIN Explore IT : Jurnal Keilmuan dan Aplikasi Teknik Informatika Soeropati: Journal of Community Service Jurnal Teknologi KERNEL: Jurnal Riset Inovasi Bidang Informatika dan Pendidikan Informatika Jurnal Teknologi dan Terapan Bisnis
Angga Debby Frayudha
Politeknik Semen Indonesia
Religion Arts Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Environmental Science Industrial & Manufacturing Engineering Mechanical Engineering Social Sciences Other

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Monitoring Pemeliharaan Prediktif Agitator Mixer pada Water Treatment Berbasis Data (IoT) Widikda, Aris Puja; Frayudha, Angga Debby
Jurnal Teknologi Vol 25, No 3 (2025): Desember 2025
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/teknologi.v25i3.8297

Abstract

Clean water is a vital necessity for human life and industry, so the clarity of the water treatment system (water treatment) is a crucial factor in maintaining the continuity of clean air supply. One of the important component in this system is the agitator mixer, which functions to mix coagulant and flocculant chemicals so that the dirt particle inspection process runs optimally. Damage to the agitator such as bearing wear, blade alignment, or electric motor disruption can cause a decrease in air quality and increase maintenance costs. This research developed an Internet of Things (IoT)-based predictive maintenance monitoring system to detect the working condition of the agitator mixer in real-time through vibration, temperature, and rotational speed (RPM) sensors. The obtained data was analyzed using the Isolation Forest algorithm to detect anomalies and ANFIS to predict maintenance times. The test results showed a MAPE value of 0.518% and a correlation coefficient of 0.9997, indicating high accuracy between sensor data and actual conditions. This system is able to provide early warning of potential damage, so that maintenance can be carried out in a planned manner without stopping the water treatment process. The implementation of this system improves operational efficiency, extends equipment life, and supports the digital transformation towards a smart and sustainable water treatment industry.
IoT-Based Predictive Maintenance for AC Motors in Water Treatment Plants Using Multi-Sensor Data and LSTM Networks with GAN Augmentation Frayudha, Angga Debby; Widikda, Aris Puja
Elinvo (Electronics, Informatics, and Vocational Education) Vol. 10 No. 2 (2025): November 2025 (In-Press)
Publisher : Department of Electronic and Informatic Engineering Education, Faculty of Engineering, UNY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/elinvo.v10i2.89410

Abstract

AC motors are critical assets in water treatment plants because they operate continuously to drive key processes. Reactive or schedule-based maintenance can miss early degradation and increase the risk of unplanned downtime. This study presents a field implementation of an Internet of Things (IoT)-based predictive maintenance system in a WTP. The system integrates vibration, temperature, and rotational speed (RPM) sensors with a cloud-based IoT pipeline for real-time data acquisition. Operational data were collected for 30 days from a single motor unit and analyzed using Random Forest and Long Short-Term Memory models. To address limited abnormal-event data, Generative Adversarial Network (GAN)-based augmentation was applied during training. The results show that LSTM performed more consistently than Random Forest; after augmentation, the F1-score improved from 0.92 to 0.95. The monitoring data also captured warning-level changes during operation, including vibration up to 3.9 mm/s, temperature up to 95 °C, and rotational speed dropping to around 1420 RPM, which may indicate abnormal operating conditions requiring inspection. Given the single-unit scope and short duration, the findings are reported as an initial implementation case study. Nevertheless, the work demonstrates the feasibility of a low-cost IoT-based monitoring and prediction framework to support maintenance decisions in WTP operations.
Development of IOT-Based Predictive System for Water Treatment for Monitoring Electric Motor Agitators Widikda, Aris Puja; Frayudha, Angga Debby
Jurnal IPTEK Vol 29, No 2 (2025): December
Publisher : LPPM Institut Teknologi Adhi Tama Surabaya (ITATS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31284/j.iptek.2025.v29i2.8230

Abstract

This research aims to develop an Internet of Things (IoT)-based predictive maintenance system for AC electric motors used in water treatment plants. The primary objective is to reduce unplanned downtime and enhance operational reliability by enabling proactive scheduling of maintenance activities. Research design adopts a research and development approach, beginning with a preliminary study, followed by system design, prototype implementation, data acquisition, and performance validation. The system integrates vibration, temperature, and rotation sensors with an Arduino/ESP32 microcontroller for real-time data collection. Data is transmitted via MQTT protocol to a cloud platform for storage and analysis. Machine learning algorithms, including Random Forest and Long Short-Term Memory (LSTM), are applied to classify equipment condition and detect anomalies. To address the limitation of failure data, Generative Adversarial Networks (GANs) are employed to generate synthetic training data, improving model robustness. Experimental results show that vibration levels reached 3.9 mm/s, temperature rose to 95 °C, and motor speed dropped to 1420 RPM, all of which signaled potential failure before actual breakdown. The LSTM model achieved an F1-score of 0.92, which increased to 0.95 when combined with GAN-based data augmentation, outperforming Random Forest. In conclusion, the proposed system demonstrates that integrating IoT with multi-sensor data and advanced machine learning enables early fault detection in AC motors. This approach offers a cost-effective and scalable solution for predictive maintenance, reducing downtime and extending equipment lifespan in water treatment operations.
Co-Authors Aris Puja Widikda Aris Yulianto Fatmawatul Qomariyah Hamzah Agung Harjanto, Chrisna Tri Juwita, Mario Benson Moh Sugihariyadi Pande, Ivan Roy Purwanti, Siti Putra, Mario Benson Suyono Suyono Tri Adi Prasetya