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All Journal International Transactions on Artificial Intelligence (ITALIC) Journal of Computer Science and Technology Application
Syahrul Muarif Wahid
Alfabet Inkubator Indonesia
Computer Science & IT Control & Systems Engineering Library & Information Science Other

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Improving Natural Resource Management through AI: Quantitative Analysis using SmartPLS Juan Carlos Rodr ́ıgue; John van der Merwe; Syahrul Muarif Wahid; Galih Putra Cesna; Dimas Aditiya Prabowo
International Transactions on Artificial Intelligence Vol. 2 No. 2 (2024): International Transactions on Artificial Intelligence
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/italic.v2i2.548

Abstract

This study evaluates the role of Artificial Intelligence (AI) in enhancing the efficiency of natural resource management through a quantitative analysis using SmartPLS. Data was collected from 200 professionals with significant experience in AI and natural resource management. Descriptive statistics indicated high levels of AI usage (X1) and technological competence (X2) among respondents, with average scores of 4.2 and 4.0, respectively. Convergent and discriminant validity were confirmed, with all constructs having factor loading values above 0.7 and AVE exceeding 0.5. Structural model analysis revealed that AI usage and technological competence positively and significantly impact natural resource management efficiency (Y1), with path coefficients of 0.45 and 0.38, respectively. These findings underscore AI's critical role and the necessity of technological training to maximize its benefits. This research contributes to the literature by highlighting the importance of integrating AI in sustainable resource management practices, providing a robust framework for future studies.
The Role of Natural Language Processing in Enhancing Chatbot Effectiveness for E-Government Services Mungkap Mangapul Siahaan; Richard Andre Sunarjo; Rizky Sebastian; Syahrul Muarif Wahid
CORISINTA Vol 2 No 1 (2025): February
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/corisinta.v2i1.71

Abstract

The rapid digital transformation of public administration has led to the adoption of (B) Natural Language Processing (NLP)-powered chatbots to enhance the accessibility, efficiency, and responsiveness of (O) e-government services. However, despite their increasing deployment, many government chatbots still struggle with intent recognition, response accuracy, multilingual processing, and user engagement, limiting their effectiveness. This study investigates (M) the role of NLP in improving chatbot performance within e-government services by evaluating four case studies: Ask Jamie (Singapore), UK Government Digital Assistant, MyGov Corona Helpdesk (India), and Gov.sg Chatbot. Using a mixed-methods approach, this research assesses chatbot effectiveness based on accuracy, response time, query resolution rate, and user satisfaction metrics. The findings indicate that (R) NLP-driven chatbots significantly outperform rule-based systems, with higher accuracy (up to 89%), faster response times (~2.1 seconds), and improved query resolution rates (92%), demonstrating their capacity to automate public service delivery efficiently. However, key challenges remain, including bias in NLP models, data privacy concerns, and the difficulty of integrating NLP chatbots into legacy IT infrastructures. Additionally, multilingual processing remains a limitation, affecting inclusivity for diverse populations. To overcome these challenges, this study proposes advancements in adaptive NLP models, real-time learning algorithms, ethical AI frameworks, and blockchain-based security solutions to ensure fair, secure, and transparent chatbot interactions in digital governance. These findings contribute to the growing body of research on AI-driven public service automation and highlight the potential of NLP to enhance (C) citizen-government interactions, reduce administrative burdens, and improve trust in e-government platforms. Future research should focus on bias mitigation, improving multilingual NLP capabilities, and integrating AI ethics into chatbot governance frameworks to ensure sustainable, scalable, and citizen-centric e-government chatbot solutions.
Co-Authors Dimas Aditiya Prabowo Galih Putra Cesna John van der Merwe Juan Carlos Rodr ́ıgue Mungkap Mangapul Siahaan Richard Andre Sunarjo Rizky Sebastian