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All Journal Jurnal Ilmiah Ilmu Terapan Universitas Jambi
Kapçiu, Rinela
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Education Engineering Industrial & Manufacturing Engineering

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MODELING INFLATION DYNAMICS USING THE LOGISTIC MODEL: INSIGHTS AND FINDINGS Kapçiu, Rinela; Preni, Brikena; Kalluçi, Eglantina; Kosova, Robert
Jurnal Ilmiah Ilmu Terapan Universitas Jambi Vol. 8 No. 1 (2024): Volume 8, Nomor 1, June 2024
Publisher : LPPM Universitas Jambi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22437/jiituj.v8i1.32605

Abstract

This paper examines applying the logistic model, frequently used in biology, to analyze inflation patterns in dynamic economic systems. The primary objective is to simulate and analyze the complex dynamics of inflation, thus providing new insights into the stability of financial institutions. Numerical methods such as Euler's Method, Runge-Kutta Method (RK4), and Adams-Bashforth-Moulton's method were used to simulate inflation patterns by discretizing the logistic equation. The data utilized in this research were obtained from INSTAT, BoA, MoF, and Eurostat, with quarterly results from 1995 to 2023. The simulation results indicated that the RK4 and Adams-Bashforth-Moulton methods yielded more precise and reliable inflation forecasts than Euler's. The logistic model represented the non-linear aspects of inflation dynamics well, emphasizing the necessity of using suitable numerical approaches. The study's findings highlight the effectiveness of the logistic model in economic analysis, specifically in forecasting inflation trends. Enhanced closure approaches have proven their effectiveness in analyzing intricate economic data, providing crucial insights into the stability of inflation, and informing policy formulation. This study utilizes the logistic model to analyze inflation dynamics, offering a unique methodology for comprehending and forecasting inflation in economic systems. An analysis of several closure techniques reveals a novel aspect of financial modeling tools. The findings indicate that incorporating advanced numerical methods can significantly improve the precision of economic models. These findings significantly impact economic research and policy formulation, especially in devising measures to manage inflation and ensure financial stability.
URBAN FLOOD RESILIENCE: A MULTI-CRITERIA EVALUATION USING AHP AND TOPSIS Kosova, Robert; Hajrulla, Shkelqim; Xhafaj, Evgjeni; Kapçiu, Rinela
Jurnal Ilmiah Ilmu Terapan Universitas Jambi Vol. 8 No. 2 (2024): Volume 8, Nomor 2, December 2024
Publisher : LPPM Universitas Jambi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22437/jiituj.v8i2.35387

Abstract

Floods are increasingly recognized as one of the most destructive natural disasters, driven by urban expansion, climate change, and unregulated development. This is particularly true in developing countries, where rapid urbanization has increased impervious surfaces, amplifying flood risks in urban areas. This study focuses on Albania, evaluating urban resilience against floods through the lens of water-related disaster data. Using a multi-criteria decision analysis (MCDA) approach, specifically the Analytic Hierarchy Process (AHP) and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), the research assesses flood vulnerability and resilience in rapidly urbanizing regions. Integrating AHP and TOPSIS with mixed methods introduces a novel approach, offering a comprehensive evaluation framework for flood risk management. The findings highlight critical vulnerabilities and suggest that targeted urban planning and disaster mitigation efforts can enhance resilience. Future research could incorporate climate projections and granular urban data, supporting a more adaptive flood management strategy.
UTILIZING ARTIFICIAL INTELLIGENCE IN ENERGY MANAGEMENT SYSTEMS TO IMPROVE CARBON EMISSION REDUCTION AND SUSTAINABILITY Tabaku, Eda; Vyshka, Eli; Kapçiu, Rinela; Shehi, Alban; Smajli, Ensi
Jurnal Ilmiah Ilmu Terapan Universitas Jambi Vol. 9 No. 1 (2025): Volume 9, Nomor 1, March 2025
Publisher : LPPM Universitas Jambi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22437/jiituj.v9i1.38665

Abstract

This article examines the revolutionary potential of artificial intelligence (AI) in improving energy management systems (EMS) to reduce carbon emissions and tackle pressing climate change issues. We conduct a comprehensive literature analysis to analyze AI-driven solutions for optimizing energy usage, minimizing carbon footprints, and promoting sustainability across diverse industries. Conventional EMS methodologies often depend on static and reactive strategies, limiting their efficacy in the face of increasing global energy needs and regulatory requirements. Conversely, AI-driven EMS provides sophisticated data analytics, predictive maintenance, and real-time optimization, markedly enhancing efficiency and emissions control. Our research includes case studies from both industrial and public sectors that illustrate the quantifiable effects of AI-integrated Energy Management Systems in reducing operating expenses, improving renewable energy integration, and fostering better energy practices. Significant hurdles, such as elevated implementation costs, data privacy issues, and regulatory compliance, are examined with prospective legislative frameworks to promote AI use. We underscore the significance of AI in delivering actionable insights, harmonizing energy practices with climate policy, and promoting a sustainable energy future. This study concludes that AI-driven Energy Management Systems are essential for significant emissions reductions and the development of resilient, eco-efficient energy systems, highlighting the necessity for strategic investment and supportive policies to optimize AI technology's societal and environmental advantages in energy management.
Co-Authors Hajrulla, Shkelqim Kalluçi, Eglantina Kosova, Robert Preni, Brikena Shehi, Alban Smajli, Ensi Tabaku, Eda Vyshka, Eli Xhafaj, Evgjeni