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All Journal Technology and Informatics Insight Journal Journal of Fuzzy Systems and Control (JFSC)
Danang Hendrawan
Sekolah Tinggi Teknik Pati
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering

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Comparative Analysis of Random Forest and LSTM for Predictive Maintenance of Electric Motors Muhammad ‘Atiq; Musab Ali El Nefati; Arief Marwanto; Fajar Husain Asy'ari; Rio Subandi; Danang Hendrawan
Journal of Fuzzy Systems and Control Vol. 4 No. 2 (2026): Vol. 4 No. 2 (2026)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jfsc.v4i2.421

Abstract

This study presents a comparative analysis of Random Forest and Long Short-Term Memory (LSTM) algorithms for predictive maintenance of electric motors using Industrial Internet of Things (IIoT) sensor data (current, temperature, vibration). A synthetic dataset from Kaggle comprising 5,000 samples across three operational states (HEALTHY, WARNING, CRITICAL) was employed. Experiments were conducted on Google Colab with a Tesla T4 GPU. The LSTM model achieved a classification accuracy of 98.9%, outperforming Random Forest (98.5%), with perfect precision and recall for the WARNING state and improved recall for the CRITICAL state (0.97 vs. 0.96). However, Random Forest demonstrated substantially shorter training time (2 seconds vs. 30 seconds). For real-time industrial deployment on Programmable Logic Controllers (PLCs), Random Forest is recommended due to its rapid inference (5-10 ms), while LSTM is better suited for critical assets where detection accuracy is prioritized. The findings also confirm the viability of Google Colab as an accessible platform for predictive maintenance research in academic settings. This work contributes a practical framework for selecting between machine learning and deep learning approaches in IIoT-based motor fault diagnosis.
Analisis Komparatif Protokol I2C, UART, dan SPI pada Sistem Mikrokontroler Berbasis Tinkercad Muh. Alaika Sulthonnudin; Muhammad Atiq; Raka Dian Mahardi; Rizal Agri Wahyuadi; Danang Hendrawan
Technology and Informatics Insight Journal Vol. 5 No. 2 (2026): TIIJ
Publisher : LP3M Universitas Putra Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32639/e8sh3t14

Abstract

The selection of a serial communication protocol affects pin efficiency, connection topology, and data-exchange behavior in embedded systems. This study compares I2C, UART, and SPI through a Tinkercad-based simulation experiment using two Arduino Uno boards as Master and Slave. Three scenarios were evaluated: I2C with a temperature sensor, UART with a PIR sensor, and SPI with a photoresistor. Data were observed through the Serial Monitor and Slave-side LED responses. All observed functional scenarios consistently transferred the intended sensor status. I2C provided efficient wiring through SDA and SCL, UART offered a simple point-to-point configuration at 9600 bps, while SPI required more signal lines for synchronous communication. The findings indicate that protocol selection should consider topology, pin availability, and application requirements. Hardware experiments are still required to quantify latency, throughput, and transmission errors.
Co-Authors Arief Marwanto Fajar Husain Asy'ari Muh. Alaika Sulthonnudin Muhammad Atiq Muhammad ‘Atiq Musab Ali El Nefati Raka Dian Mahardi Rio Subandi Rizal Agri Wahyuadi