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All Journal International Transactions on Artificial Intelligence (ITALIC) JOT
Po Abas Sunarya
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Computer Science & IT Control & Systems Engineering Library & Information Science

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Adaptive Fuzzy Hybrid AI for Urban Energy Traffic Decision Support Qurotul Aini; Andriyansah Andriyansah; Mekani Vestari; Po Abas Sunarya; Carlos Perez
International Transactions on Artificial Intelligence Vol. 4 No. 2 (2026): May
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/italic.v4i2.1049

Abstract

Urban energy and traffic systems are two highly interdependent components of smart city infrastructures, both of which operate under significant uncertainty caused by fluctuating demand, human mobility patterns, weather variability, and policy constraints. While Artificial Intelligence (AI) techniques particularly machine learning and deep learning have demonstrated strong predictive capabilities in these domains, their black box nature limits interpretability, trust, and adoption in real world urban governance. Methods: This study proposes an adaptive fuzzy hybrid artificial intelligence framework that integrates fuzzy inference systems with ensemble machine learning models to support uncertainty aware and explainable decision making in urban energy and traffic management. The proposed framework is validated using real world secondary data obtained from open government and smart city data portals, including urban energy demand, traffic flow, and environmental indicators. The primary objective of this research is to develop a robust and interpretable decision-support model capable of dynamically adapting to uncertain urban conditions while maintaining high predictive performance. Experimental evaluations demonstrate that the proposed fuzzy hybrid AI framework consistently outperforms standalone machine learning approaches in terms of decision stability, robustness under uncertainty, and interpretability across multiple urban scenarios. Conclusion: The findings indicate that adaptive fuzzy hybrid AI offers a practical, scalable, and policy aligned solution for urban energy traffic decision support, contributing to sustainable smart city governance and supporting evidence-based decision making in line with global sustainability agendas.
Anxiety Prediction Model Based on Smartwatch Activity Data and Self-Reported Affect Scale in Adolescents Po Abas Sunarya; Mohd Faiz Hilmi; Nesti Anggraini Santoso; Sondang Visiana Sihotang; Ramiro Santiago Ikhsan
Journal of Orange Technology Vol. 2 No. 1 (2025): October
Publisher : Sinar Mentari Sundara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.68012/jot.v2i1.43

Abstract

This study proposes an anxiety prediction model based on smartwatch activity data integrated with a self-reported affect scale in adolescents. Anxiety among adolescents is a growing public health concern, often underdetected due to subjective assessment and limited continuous monitoring. To address this gap, this research combines objective physiological and behavioral indicators collected from smartwatches, including heart rate variability, sleep duration, physical activity intensity, and daily movement patterns, with subjective emotional states measured through a validated affect scale. Data were collected longitudinally from adolescent participants over several weeks to capture temporal variations in activity and mood. Machine learning techniques were applied to develop and evaluate predictive models capable of identifying anxiety levels with high accuracy. Model performance was assessed using standard metrics such as accuracy, precision, recall, and F1-score. The results demonstrate that the integration of wearable sensor data with self-reported affect significantly improves anxiety prediction compared to single-source data models. The proposed model offers a scalable, non-invasive, and real-time approach for early anxiety detection, supporting timely intervention and personalized mental health monitoring for adolescents. This study contributes to the development of human-centered digital health technologies and highlights the potential of wearable-based analytics in preventive mental healthcare systems for future applications.
Co-Authors Andriyansah . Carlos Perez Mekani Vestari Mohd Faiz Hilmi Nesti Anggraini Santoso Qurotul Aini Ramiro Santiago Ikhsan Sondang Visiana Sihotang