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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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Social Media Penetration and Voter Turnout in Southeast Asia: A Multi-Country Comparative Study Using Public Survey Data Elinda Novita Dewi; Muhammad Nurfaizi Arya Rahardja; Ayu Ambarwati; Bustomi Bustomi; Wan Muna Marwah; Moh. Imron Rosidi; Anjela Karunia Amalia
International Journal of Applied Research and Innovation Vol. 1 No. 1 (2026): January: Resocia: International Journal of Applied Research and Innovation
Publisher : CV SCRIPTA INTELEKTUAL MANDIRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65310/gfd1gy02

Abstract

This study examines the relationship between social media penetration and voter turnout in Southeast Asia using a multi-country comparative design based on publicly available survey data. Drawing on cross-national evidence, the analysis shows that social media access is associated with higher voter turnout, but only under specific political, institutional, and social conditions. Countries with competitive elections, higher institutional trust, and credible information environments display stronger turnout effects linked to social media use, while contexts characterized by patronage-based mobilization and political uncertainty show weaker or uneven outcomes. The study further demonstrates that campaign strategies on social media shape participation through personalization, network-based diffusion, and emotional framing, often mobilizing particular voter segments rather than the electorate as a whole. Social inequalities in education, income, gender, and civic capacity significantly moderate these effects, limiting the participatory gains of digital expansion for marginalized groups. Overall, the findings suggest that social media acts as a conditional amplifier of electoral participation rather than a universal driver of voter turnout in Southeast Asia.  
Social Capital and Community Empowerment in Rural Development Programs: A Sociological Analysis Nahri Idris; Layyinatus Shifah; Elinda Novita Dewi; Bustomi Bustomi; Putu Agus Ariana
Journal of Human Interaction and Social Studies Vol. 1 No. 1 (2026): :February: Sapientia Diversalis: Journal of Human Interaction and Social Studie
Publisher : CV SCRIPTA INTELEKTUAL MANDIRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65310/edv65p82

Abstract

We examine how social capital operates as a relational infrastructure shaping community empowerment within rural development programs through a multi-site qualitative case study grounded in interviews, focus groups, and observations. Findings show that empowerment emerges from dense interactional networks that connect bonding, bridging, and linking ties, enabling communities to negotiate power, institutional access, and collective learning. Gendered and livelihood-based networks function as critical arenas where trust and reciprocity are translated into durable participatory practices, while supportive governance arrangements stabilize these relational gains across time. Comparative analysis demonstrates that sustainable empowerment depends on balancing internal cohesion with external partnerships and institutional memory, producing adaptive capacities resilient to social and administrative change. The study advances a sociological model that conceptualizes social capital as a dynamic process linking interaction, power, and sustainability in rural contexts, and offers methodological insights for evaluating empowerment beyond output-centered metrics. By foregrounding relational mechanisms, the research clarifies how community agency is continuously reproduced through negotiated networks that integrate institutional credibility with everyday cooperation, informing more inclusive and durable
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