PEMANFAATAN ARTIFICIAL INTELLIGENCE DALAM PENGEMBANGAN SISTEM BERBASIS WEB UNTUK MENINGKATKAN EFISIENSI DAN KUALITAS LAYANAN DIGITAL: Utilizing Artificial Intelligence In Web-Based System Development To Enhance Digital Service Efficiency And Quality - Abdul Hamid Arribathi; Dede Herman (Universitas Wiralodra, 2026)
This research is motivated by the increasing need of organizations for fast, personalized, and efficient digital services, while conventional web systems remain static and unadaptive to user behavior. This type of research is a literature study with a descriptive qualitative approach. The research procedure includes identification of articles from Scopus and Google Scholar databases for the 2020-2025 period, selection based on relevance to AI and web system topics, thematic content analysis, and synthesis of findings to draw conclusions. This study aims to analyze the role of Artificial Intelligence in web-based system development and identify its contributions to improving operational efficiency and digital service quality. The theoretical benefit of this research is to enrich scientific knowledge in the field of AI and information systems, while the practical benefit is to provide guidance for developers and managers of web systems in adopting AI technology for service optimization. The findings reveal that AI implementation through chatbots achieves an auto-resolution rate of 68% with a BERTScore F1 of 84.81%, while content personalization increases Click-Through Rate (CTR) by 271% and user engagement by 172%. Additionally, response speed is proven to be the most influential factor in customer satisfaction, and user experience serves as a critical mediator between website personalization and customer engagement. This research confirms that structured and systematic AI integration can overcome the limitations of conventional web systems, with successful chatbot implementations in Indonesia achieving an F1-score of 0.93 for public services and real-time API integration. hese research findings are supported by Chang et al. (2024), who proved that the application of machine learning and deep learning in credit risk prediction significantly improves accuracy compared to conventional methods, a principle that aligns with the improvement of content personalization accuracy in web systems.