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All Journal Resocia: International Journal of Applied Research and Innovation Sapientia Diversalis: Journal of Human Interaction and Social Studies Technema: Journal of Intelligent Engineering and Computing
Bustomi Bustomi
Institut Pertanian Bogor

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Journal : technema journal of intelligent engineering and computing

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Air Quality Index Prediction Using Machine Learning Algorithms on the Beijing PM2.5 Dataset Tuti Susilawati; Bustomi Bustomi
Technema: Journal of Intelligent Engineering and Computing Vol. 1 No. 1 (2026): March: Technema: Journal of Intelligent Engineering and Computing
Publisher : CV SCRIPTA INTELEKTUAL MANDIRI

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Abstract

Accurate prediction of Air Quality Index (AQI) is critical for mitigating public health risks associated with urban air pollution. This study presents an empirical analysis of PM2.5 concentration forecasting in Beijing using advanced machine learning algorithms, integrating high-resolution atmospheric data and meteorological variables. A multi-stage pipeline was implemented, including data preprocessing, feature selection, and model training with Random Forest, Gradient Boosting, Support Vector Regression, and Long Short-Term Memory (LSTM) networks. Predictive performance was evaluated using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and R², while spatial and temporal fidelity was assessed across districts, diurnal cycles, and seasonal periods. LSTM models consistently achieved superior accuracy, capturing short-term pollution spikes, seasonal variability, and spatial heterogeneity, whereas ensemble methods provided stable baseline predictions with moderate sensitivity to extreme events. Sensitivity analysis identified wind speed, humidity, and neighboring PM2.5 measurements as key predictors. The results demonstrate that integrating recurrent neural networks with ensemble approaches enables reliable, operationally relevant AQI forecasts, offering both theoretical validation of sequential modeling for urban air quality and practical guidance for environmental monitoring, public health interventions, and city-level policy implementation.  
Digital Twin Technology for Sustainable Industrial Operations Erlita Sulistiati; Bustomi Bustomi; Guslila Sari Nasution; Atina Salamah; Rian Ardianto
Technema: Journal of Intelligent Engineering and Computing Vol. 1 No. 2 (2026): : June: Technema: Journal of Intelligent Engineering and Computing
Publisher : CV SCRIPTA INTELEKTUAL MANDIRI

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Abstract

Digital Twin technology has emerged as a strategic enabler for sustainable industrial transformation by integrating physical operations, virtual representations, predictive analytics, and sustainability-oriented decision support into a unified cyber–physical environment. This study aims to develop and analytically evaluate a comprehensive Digital Twin framework capable of supporting sustainable industrial operations through the integration of operational efficiency, energy performance, resource optimization, and system resilience dimensions. A non-empirical system design approach was employed to construct a multilayer architecture consisting of physical operation, data acquisition, communication and synchronization, digital twin modeling, analytics and optimization, and sustainability decision-support layers. Technical evaluation was conducted through model-based simulation and analytical assessment using standardized sustainability and operational indicators. The findings demonstrate that the proposed framework strengthens operational visibility, predictive maintenance capability, energy efficiency, resource utilization, responsiveness, and resilience through continuous interaction between physical and virtual environments. The analysis further indicates that Digital Twin integration facilitates circularity, sustainability governance, and Industry 5.0 readiness by enabling adaptive and data-driven industrial decision making. The study contributes a holistic conceptual framework that advances the understanding of Digital Twin technology as a sustainability-enabling infrastructure for future industrial systems.  
Co-Authors Anjela Karunia Amalia Ariana, Putu Agus Atina Salamah Ayu Ambarwati Elinda Novita Dewi Erlita Sulistiati Guslila Sari Nasution Layyinatus Shifah Moh. Imron Rosidi Muhammad Nurfaizi Arya Rahardja Nahri Idris Rian Ardianto Tuti Susilawati Wan Muna Marwah