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All Journal Jurnal Engine: Energi, Manufaktur, dan Material Advance Sustainable Science, Engineering and Technology (ASSET)
Rifki Hermana
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Chemistry Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering

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Prototype of a Dust Monitoring Device in the Mechanical Engineering Laboratory at PGRI University Semarang Using the GP2Y1010AU0F Sensor Rifki Hermana; Muchamad Malik; Agus Mukhtar; Andrew Joewono; Gostsa Khusnun Naufal
Jurnal Engine: Energi, Manufaktur, dan Material Vol. 8 No. 2 (2024)
Publisher : Proklamasi 45 University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30588/jeemm.v8i2.1999

Abstract

ABSTRACT This study aims to develop an accurate and real-time air quality monitoring system in the Mechanical Engineering Laboratory that provides information on the measured parameter values. The research employs an experimental approach to determine the effectiveness of variables within the experiment. The primary sensor used is the GP2Y1010AU0F Optical Dust Sensor, which operates based on infrared light to measure particulate dust concentration levels. Analysis of sensor testing results was conducted by observing variations in sensor readings, notably after trials involving tissue burnt as a dust source. The dust sensor consistently recorded an average dust concentration of 0.597 Kg/m³. Subsequently, tests were conducted using baby powder with a constant weight of 30 mg per trial. Sensor readings varied between 0.35 Kg/m³ and 0.38 Kg/m³, indicating that within the given weight range, the powder mass does not significantly impact sensor readings. Further, tests on indoor dust density, with an average concentration of 36.01 µg/m³, revealed a relatively low average dust concentration in the room during the measurement period..
Hybrid XGBoost-LSTM Framework for Accurate SOC, SOH, DOD and Internal Resistance Estimation in Li-ion Cells Putra Pralano, Axel; Florence Gnana Poovathy John; Rifki Hermana
Advance Sustainable Science Engineering and Technology Vol. 8 No. 2 (2026): February-April
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v8i2.3119

Abstract

Accurate estimation of State of Charge (SOC), State of Health (SOH), Depth of Discharge (DOD), and internal resistance is critical for Battery Management Systems (BMS) in electric vehicles and energy storage. Conventional methods fail to capture the nonlinear and temporal dynamics of lithium-ion cells, while existing machine learning approaches lack systematic benchmarking for embedded deployment. This study evaluates three hybrid models XGBoost-LSTM, XGBoost-SVR, and Linear Regression-Random Forest on high-resolution Samsung 30T single-cell data (five cycles, 6,081 timesteps). Models used 35 mutual information-selected features, identical preprocessing, and Bayesian hyperparameter optimization. XGBoost-LSTM achieved superior accuracy: SOC (R²=0.983), SOH (R²=0.985), DOD (R²=0.977), and internal resistance (R²=0.972), outperforming baselines significantly (Wilcoxon p<0.05). Computational profiling showed 15 ms inference latency and 60 MB memory usage, suitable for real-time BMS at 10 Hz. Results indicate that hybrid temporal learning improves battery diagnostics, while further validation across multiple chemistries, extended temperatures, multi-cell setups, and longer cycles is recommended for practical deployment.
Co-Authors Agus Mukhtar Andrew Joewono Florence Gnana Poovathy John Gostsa Khusnun Naufal Muchamad Malik Putra Pralano, Axel