https://ejournal.pancawidya.or.id/index.php/galaksi Jurnal Galaksi Yayasan Sraddha Panca Widya Nusantara en-US Jurnal Galaksi 3089-2341 https://ejournal.pancawidya.or.id/index.php/galaksi/article/view/68 <p><em>The Backyard Villa is a medium-scale lodging business that relies on manual booking methods like text messages and phone calls. This approach leads to various issues, including delayed confirmations, data duplication or loss, and limited access to information for prospective guests. These inefficiencies negatively impact the operational effectiveness and overall service quality. This study aims to design and implement a web-based reservation information system to enhance service performance and competitiveness in the digital era. The system was developed using the waterfall method, which includes requirement analysis, system design, implementation, and testing phases. The system defines two main types of users—guests and admins with different access rights and features. System functionality testing was conducted on 28 scenarios, covering the booking process, villa data management, and reporting. The test results demonstrated a 100% success rate. The system not only simplifies the reservation process for customers through self-service but also supports the management in handling booking data, customer information, and operational reports more efficiently. The implementation of this system is expected to significantly improve the operational efficiency and service quality of The Backyard Villa by automating manual tasks, reducing errors, and providing a better user experience for both guests and management.</em></p> Ni Made Nadiya Aryadivani I Wayan Ady Juliantara I Made Agus Oka Gunawan Copyright (c) 2025 Ni Made Nadiya Aryadivani, I Wayan Ady Juliantara , I Made Agus Oka Gunawan https://creativecommons.org/licenses/by-nc-sa/4.0 2025-08-24 2025-08-24 2 2 51 60 10.70103/galaksi.v2i2.68 https://ejournal.pancawidya.or.id/index.php/galaksi/article/view/96 <p><em>Decision Intelligence (DI) has emerged as an integrative paradigm that combines data, analytics, and artificial intelligence to enhance organizational decision-making. Despite this growing interest, many existing DI approaches place disproportionate emphasis on predictive intelligence while providing limited methodological guidance on how predictions are transformed into actionable and accountable decisions. Machine learning models are highly effective at forecasting and classification; however, they do not inherently incorporate organizational constraints, human preferences, or decision trade-offs. This study proposes a structured, end-to-end Decision Intelligence framework that explicitly integrates machine learning–based prediction with Decision Support System (DSS) modelling. The framework positions DSS as the core decision logic by employing the Analytic Hierarchy Process (AHP) to formalize contextual and human preferences and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to execute alternative ranking. Furthermore, contextual intelligence and outcome intelligence are embedded to ensure decision relevance, transparency, and continuous improvement. Using a Design Science Research approach, this study develops and demonstrates the proposed framework as a systematic solution for bridging the gap between predictive analytics and decision execution. The framework contributes to Decision Intelligence research by clarifying the role of DSS in AI-driven decision environments and by providing a replicable structure for integrating prediction, decision modelling, and outcome evaluation.</em></p> I Gede Iwan Sudipa I Dewa Gede Agung Pandawana I Made Subrata Sandhiyasa Copyright (c) 2025 I Gede Iwan Sudipa, I Dewa Gede Agung Pandawana, I Made Subrata Sandhiyasa https://creativecommons.org/licenses/by-nc-sa/4.0 2025-08-30 2025-08-30 2 2 61 68 10.70103/galaksi.v2i2.96