Information Systems, Information Systems and Management, Computer Science
9
Scopus Publications
Scopus Publications
Customer-centric circular economy as-a-service decision-making: Machine learning-driven open innovation in food service Tutur Wicaksono, Marhadi Marhadi, Agustinus Fritz Wijaya, Velly Anatasia, Krisztina Taralik Cleaner Environmental Systems, 2025 This study addresses a key research gap by developing a machine learning-driven open innovation framework for prioritizing Circular Economy as a Service (CEaaS) measures in the food service sector. Conventional sustainability strategies often fail to integrate dynamic customer preferences with scalable circular economy practices. To bridge this gap, this research employs Random Forest classification and feature selection to assess the impact of sixteen CEaaS measures on customer satisfaction across Indonesian and Hungarian food service markets. The framework is grounded in PRISMA-guided literature review, customer surveys, and machine learning-based decision analytics. Sustainable sourcing, reusable packaging systems, and circular loyalty programs emerge as the most critical CEaaS measures, reflecting global consumer priorities for ethical procurement, waste minimization, and behavior-based engagement. Notably, surplus food redistribution and food waste upcycling services rank consistently high, underscoring growing expectations for visible circularity and social value. The cross-country model validation confirms the framework’s robustness and generalizability. By integrating circular economy principles, customer insights, and machine learning, the study advances open innovation theory and provides actionable guidance for sustainable transformation in the food service sector. The findings support Sustainable Development Goals related to responsible consumption, climate action, industry innovation, and global partnerships.
Artificial Intelligence Governance for Innovation and Resilient Data Security in Academic Institutions Merryana Lestari, Agustinus Fritz Wijaya, Mega Kartika Sari, Laurence Keane Leander Proceedings 7th International Conference on Informatics Multimedia Cyber and Information System Icimcis 2025, 2025 The rapid adoption of Artificial Intelligence (AI) presents a significant challenge for Indonesian higher education institutions to balance innovation with resilient data security. The study is among the first to integrated AI Governance for Innovation and Resilient Data Security (AIG-IRDS) model, including ISO/IEC 42001, ISO/IEC 27001, the NIST-AI Risk Management Framework, Zero Trust Architecture principles, and COBIT 2019, along with national regulations such as the PDP Law. This integrated model establishes an adaptive, auditable, and ambidextrous governance framework that enables institutions to sustain innovation while ensuring trust, compliance, and data resilience. The research employs a conceptual empirical hybrid design combining expert panel validation, limited case studies and a proof-of-concept evaluation two universities in Indonesia. Quantitative results reveal significant improvements of 76% in governance maturity, 77% in auditability, and 39% in AI-driven innovation measured over 12 weeks. These findings confirm the model’s effectiveness in enhancing institutional trust, compliance readiness, and secure AI adoption, while fostering innovation within a standardized governance framework. Theoretically, this study contributes by operationalizing ambidextrous AI governance, extending IT governance and risk management models. Practically, it offers a replicable blueprint for secure and ethical AI deployment in higher education aligned with both global standards and local regulatory expectations.
Machine Learning-Driven Feature Prioritization for Battery Electric Vehicle Charging Infrastructure in Highly Polluted Area Tutur Wicaksono, Ferdinand Mahardhika, Agustinus Fritz Wijaya, Lilis Susilawaty, Glisina Dwinoor Rembulan Proceeding 2025 International Conference on Information Technology Research and Innovation Harnessing Intelligent Machines for Sustainable Development Aligning AI with the Sdgs Icitri 2025, 2025 Air pollution remains a critical issue in developing countries, where fossil fuel vehicles significantly degrade air quality. Despite growing interest in battery electric vehicles (BEVs), adoption lags due to inadequate charging infrastructure. This study applies a machine learning approach to identify and prioritize key charging infrastructure features that can enhance BEV adoption. A sequential mixed-methods design was used, starting with in-depth interviews with 15 nonBEV users to explore essential features, followed by a survey of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{1, 5 1 1}$</tex> participants for validation. A Random Forest model ranked features based on their importance across five adoptionrelated variables. Customer support, charging availability, and interoperability emerged as primary priorities, while charging speed, payment flexibility, and home charging were identified as secondary. These findings highlight the need for consumeroriented, accessible, and dependable infrastructure to support sustainable transportation transitions in highly polluted regions. Theoretically, this study advances the understanding of technology adoption by integrating behavioral insights with data-driven feature prioritization in the context of sustainable mobility.
Boundary Identification using Backtracking Algorithm in Depth First Search Approach Teady Matius Surya Mulyana, Agustinus Fritz Wijaya, Jusia Amanda Ginting, I Gusti Ngurah Suryantara, Reyes Thierry Kwa Proceedings 7th International Conference on Informatics Multimedia Cyber and Information System Icimcis 2025, 2025 In binary image processing, standard Connected Component Labeling (CCL) methods introduce significant computational overhead by indiscriminately processing internal, boundary-isolated components, whereas traditional edge detection yields fragmented results. This study addresses the need for an efficient and topologically flexible segmentation method focused exclusively on exposed object boundaries. We propose the Boundary Backtracking Algorithm (BBA), a novel approach that strategically integrates a Boundary-Seed Trigger criterion with a flexible, recursive Depth-First Search (DFS) traversal. The BBA uniquely initiates traversal only from object pixels touching the background and features variable connectivity modes (4-and 8-connectivity) to adapt to different topological definitions. Quantitative results demonstrate that the BBA achieves an average 70% reduction in computational time compared to non-selective standard Flood Fill methods on dense matrices. Functionally, the study proves the feasibility of dynamically switching between connectivity modes, enabling accurate labeling of components connected only diagonally. The BBA thus provides a precise, rapid, and adaptable segmentation framework, especially beneficial for real-time applications such as industrial quality inspection and robotic vision.
STARS: Websocket Design and Implementation Penidas Fiodinggo Tanaem, Augie David Manuputty, Agustinus Fritz Wijaya 2022 International Seminar on Application for Technology of Information and Communication Technology 4 0 for Smart Ecosystem A New Way of Doing Digital Business Isemantic 2022, 2022 The need for real-time data has become the center of attention for today's technology users. Thus, there are several methods that can be used, one of which is websocket. Websocket itself can be used to present real-time data using minimal resources. Thus, in this study, we will present a websocket concept that is implemented at SWCU STARS. The end result is a websocket that is built and used by utilizing the Erlang programming language to produce a reliable, lightweight and scalable system which shows the advantages obtained from other webservers.
Analysis of information technology governance e-KTP using COBIT 5 framework Tridoyo, Agustinus Fritz Wijaya Proceedings 2017 International Conference on Innovative and Creative Information Technology Computational Intelligence and Iot Icitech 2017, 2017 The system of population administration is information technology infrastructure in Dinas Kependudukan Catatan Sipil (DISDUKCAPIL) Landak District. Importance the population administration system that operates, makes it optimal condition. It needs to be controlled and evaluated its performance from the information system so that the system is built organization can achieve business goals. Therefore, it is necessary to evaluate information technology governance performance, in this study standard used for performance evaluation process that is COBIT 5 framework on domain DSS (Deliver, Service, Support). The COBIT 5 template can be a performance evaluation tool assess whether system information can meet the needs of business objectives. Based on the evaluation of information technology governance systems that use COBIT 5 indicate that an organization has implemented all processes, which means DISDUKCAPIL Landak District has reached the level ability at level 3 (established process). To improve the level of IT process capabilities, COBIT 5 has provided guidance in the form recommendations for harmonization between e-KTP manufacturing services business objectives DISDUKCAPIL Landak District with IT goals Menteri Dalam Negeri in Indonesian to improve the efficiency and effectiveness of e-KTP making services to achieve organizational goals.
SME development based on information and communication technology to global market in district Kendal Journal of Theoretical and Applied Information Technology, 2015
The evaluation of logistic information system performance at university use framework cobit 4.1 domain monitoring and evaluate (study case: Satya Wacana Christian University) Journal of Theoretical and Applied Information Technology, 2014
