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All Journal JURNAL KESEHATAN LINGKUNGAN (Journal of Environmental Health) Journal of Robotics and Control (JRC)
Hamzah, Firdaus Mohamad
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Aerospace Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Mechanical Engineering Public Health

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Meta Analysis: Risk Factor Analysis of Dengue Disease Incidence in Indonesia Keman, Soedjajadi; Azizah, R; Hamzah, Firdaus Mohamad
JURNAL KESEHATAN LINGKUNGAN Vol. 17 No. 1 (2025): JURNAL KESEHATAN LINGKUNGAN
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jkl.v17i1.2025.69-76

Abstract

Introduction: Dengue fever is endemic in over 100 countries, Africa, America, and several other European countries. Indonesia became the top of the list a few years ago. The study aimed to analyze risk factors (hanging clothes, water reservoir conditions, and jumantik cadres) with the incidence of DHF. Methods: This is quantitative research with meta-analysis. Meta-analysis has four stages: data abstraction, data analysis using JASP Version 0.18.3, and publication bias test. Conduct heterogeneity tests, funnel plots, egger tests, and forest plots. Results and Discussion: The heterogeneity test of hanging clothes and water reservoirs using a random effects model because p-value smaller than 0.05. Jumantik cadre using the fixed effects model is larger than p < 0.001 i.e. p = 0.303. The forest plot of hanging clothes has pooled value PR=e0.26 = 1.297 (95% CI -0.05-0.57), pooled water reservoir value PR= e0.55 = 1.73 (95% CI 0.30 - 0.79), and jumantik cadre pooled value OR= e0.70= 2.01 (95% CI 0.24 - 1.33). The highest risk factor for dengue cases is jumantik cadres with the pooled value obtained PR = e0.70 = 2.01 (95% CI 0.24 – 1.33). Conclusion: Based on the results of the meta-analysis in this study, jumantik cadres has the greatest risk factor value compared to other variables. The second highest risk factor was in water reservoirs, followed by the next variable hanging clothes.
AI-Driven Energy Management Techniques for Enhancing Network Longevity in Wireless Sensor Networks Hadi, AL-Shukrawi Ali Abbas; Wahab, Aeizaal Azman Bin Abdul; Hamzah, Firdaus Mohamad; Veena, B. S.
Journal of Robotics and Control (JRC) Vol. 6 No. 1 (2025)
Publisher : Universitas Muhammadiyah Yogyakarta

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Abstract

WSNs and mobile systems are critical for monitoring and data collection, but energy efficiency remains one of the biggest challenges due to very limited battery life in sensor nodes. The issue here is the challenge of energy management by adopting sophisticated optimization techniques and AI-driven methodologies. This research develops a Q-learning model of dynamic energy optimization. The proposed method uses MATLAB simulations and real-world testing to validate improvements. The methodology employs adaptive routing and real-time power adjustments, which optimize energy usage. The results show a 34.92% increase in energy savings compared to traditional methods, where baseline energy efficiency was 65%. The Packet Delivery Ratio (PDR) improved from a baseline of 85% to 96.38%, ensuring more reliable data communication. The network latency was reduced by 24 ms, from the initial 50 ms, thus enhancing real-time responsiveness. Q-learning approach was extended for an additional 10 hours against the 7-hour baseline established by conventional systems. These improvements are based on fully dynamic routing with online adjustments, which makes the network adaptive to changing environments. This methodology is promising for energy-efficient and high-performance communication systems in remote and critical applications. The findings contribute to sustainable network operations and reduce the maintenance costs, making WSNs viable for long-term deployments.
Co-Authors Hadi, AL-Shukrawi Ali Abbas R Azizah Soedjajadi Keman Veena, B. S. Wahab, Aeizaal Azman Bin Abdul