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All Journal Management Studies and Entrepreneurship Journal (MSEJ) Jurnal Komtekinfo
Puspabhuana, Adam
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Computer Science & IT Decision Sciences, Operations Research & Management

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The Role of Artificial Intelligence in Enhancing Cloud-Based Disaster Management Systems Puspabhuana, Adam; Andhika; Triyana, Yudi; Rifky Adhani, Muhamad
Jurnal KomtekInfo Vol. 12 No. 2 (2025): Komtekinfo
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35134/komtekinfo.v12i2.645

Abstract

Disaster management systems are vital in mitigating the impacts of natural and human-induced disasters. However, traditional methods often struggle with limitations in responsiveness and efficiency, particularly as disaster events become more frequent and severe. This study investigates the role of Artificial Intelligence (AI) in enhancing cloud-based disaster management systems, focusing on improving predictive, analytical, and operational capabilities. The research examines key AI technologies that can be integrated into cloud platforms, including machine learning, natural language processing, and computer vision. AI substantially improves disaster response and recovery by enhancing real-time data processing, decision-making, and resource allocation. The study also highlights AI's potential in early warning and risk assessment, providing decision-makers with more accurate and timely information. Empirical analysis suggests that AI-enhanced cloud systems significantly reduce response times and improve resource distribution during disaster events, reducing loss of life and property. The research concludes with practical recommendations for implementing AI in cloud-based disaster management and identifying areas for future exploration. The findings underscore the transformative potential of AI in creating more resilient disaster management infrastructures.
Artificial Intelligence In Predictive Analytics For Advancing Credit Risk Management In The Digital Economy Putri, Putri Sarah Olivia; Puspabhuana, Adam; Winarno, Dwi
Management Studies and Entrepreneurship Journal (MSEJ) Vol. 6 No. 6 (2025): Management Studies and Entrepreneurship Journal (MSEJ)
Publisher : Yayasan Pendidikan Riset dan Pengembangan Intelektual (YRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37385/msej.v6i6.9584

Abstract

The rapid advancement of digital technologies has encouraged the banking sector to adopt Artificial Intelligence (AI)-based approaches for credit risk management. Traditional credit scoring methods often lack accuracy in identifying default risks, particularly for unbanked and underbanked groups, leading to higher Non-Performing Loan (NPL) rates. This research addresses the need for a more adaptive, accurate, and inclusive credit risk assessment system in the digital economy era. This research aims to develop and evaluate an AI-driven predictive analytics model for credit risk assessment by comparing the performance of machine learning algorithms, such as Logistic Regression, Random Forest, XGBoost, and Deep Learning. The dataset comprises customer demographics (such as age and income), details of their banking relationship (including mortgage and securities account), and their response to the most recent personal loan campaign. The comparative analysis indicates that Random Forest substantially outperformed the other models, demonstrating superior accuracy (98.80%) alongside balanced precision (93.75%) and recall (93.75%), as well as the highest ROC-AUC (99.86%). These results highlight its robustness in both classification performance and discriminatory power. XGBoost and Deep Learning followed, showing competitive but lower predictive capabilities. In contrast, Logistic Regression exhibited clear limitations, yielding the lowest accuracy (90.40%) and precision (50%), despite achieving a relatively high recall (92.71%) and ROC-AUC (96.77%). This suggests that while Logistic Regression can identify positive cases, its overall reliability and precision are insufficient compared to advanced ensemble and deep learning methods.
Co-Authors Andhika Dwi Winarno, Dwi Putri, Putri Sarah Olivia Rifky Adhani, Muhamad Triyana, Yudi