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All Journal JUSIFO : Jurnal Sistem Informasi JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) International Journal of Advances in Data and Information Systems Jurnal Teknik Informatika (JUTIF) Jurnal Locus Penelitian dan Pengabdian Jurnal Pengabdian Masyarakat Akademisi Jurnal PkM MIFTEK Journal of Artificial Intelligence and Digital Business eProceedings of Engineering
Mukti, Iqbal Yulizar
Unknown Affiliation
Aerospace Engineering Humanities Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Social Sciences Other

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Data Governance and AI Strategy: A Systematic Synthesis of Information Systems Frameworks for Competitive Advantage Budianto, Farhan Alif; Lubis, Muharman; Mukti, Iqbal Yulizar; Budianto, Setyo
RIGGS: Journal of Artificial Intelligence and Digital Business Vol. 5 No. 1 (2026): Februari - April
Publisher : Prodi Bisnis Digital Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/riggs.v5i1.6991

Abstract

The rapid integration of Artificial Intelligence (AI) into organizational strategy has intensified the need for robust data governance mechanisms that ensure data quality, accountability, and strategic alignment. While prior studies have examined data governance and AI strategy separately, a comprehensive synthesis explaining how Information Systems (IS) frameworks bridge technical data management and competitive advantage remains limited. This study addresses this gap by conducting a Systematic Literature Review (SLR) to synthesize key IS frameworks that govern data for AI-driven strategic outcomes. Following the PRISMA 2020 protocol, relevant peer-reviewed articles published between 2018 and 2026 were systematically collected from Scopus, Web of Science, and the AIS eLibrary. A total of 65 high-quality studies were selected for thematic and theoretical analysis. The findings reveal three dominant thematic clusters: algorithmic accountability and ethics, data pedigree and provenance, and the evolving strategic role of the Chief Data Officer (CDO). The synthesis further demonstrates a theoretical shift from static, compliance-oriented governance toward dynamic capabilities grounded in the Resource-Based View. This study contributes to IS and AI strategy literature by re-conceptualizing data governance as a second-order organizational capability that enables sustainable competitive advantage. Practical implications highlight the importance of governance agility, strategic alignment, and trust-building mechanisms in scaling AI initiatives.
Analyzing User Needs and Recommending Targeted Features for Bi’ih Village Tourism Website Using Text Mining and K-Means Clustering Artamevia, Mima; Lubis, Muharman; Mukti, Iqbal Yulizar; Handayani, Dini
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 6 (2025): JUTIF Volume 6, Number 6, Desember 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.6.5458

Abstract

Tourism village websites often do not fully reflect user needs, resulting in digital services that cannot be optimally utilized by residents and potential tourists. This situation limits access to information and reduces the effectiveness of tourism promotion efforts, especially in villages that are undergoing digital transformation. This study was conducted to identify the overall needs of users and compile data-based feature recommendations for the development of the Bi'ih Village website as a durian tourism village. The research method used a quantitative approach through the distribution of an online questionnaire to 110 respondents consisting of visitors and residents, with five open-ended questions and several structured questions. The data was analyzed using text mining to find dominant words and themes, as well as the K-Means Clustering technique determined through the Elbow method to group user characteristics. The analysis results showed that there were 2,702 tokens and 677 meaningful words, with the highest demand for government information and visual tourism content. The segmentation process produced three main groups, namely Active Supporters (61.4%), Tech Enthusiasts (27.3%), and Moderate Users (11.4%). This study contributes a data-driven approach to designing more relevant and measurable features for tourism village websites. The impact is expected to increase the adoption of village digital services, strengthen tourism competitiveness, and support the acceleration of the Smart Village concept implementation. The novelty of this study lies in the integration of text mining and clustering as the basis for developing user-oriented feature recommendations.
Co-Authors Ardhana, Sonia Frisca Putri Ari Fajar Santoso Artamevia, Mima Asti Amalia Nur Fajrillah Audini, Nurul Auliya, Feby Nandha Budianto, Farhan Alif Budianto, Setyo Dini Handayani, Dini Dwianti, Indah Fathoni, Fikri Juniarti, Feni Kirana, Shilvy Chandra Lukman Abdurrahman Luthfi Ramadani Muharman Lubis Nur Insan, Almira Syamsi Prambudia, Yudha Puspito, Dewa Cetra Prabasworo Rahmat Mulyana Riverinda Rijadi, Safara Cathasa Safitri, Intania Fadya Sari, Laras Ayu Ditya Setiawan, Herro Arya Sintha, Ersa Yunianti Taufik Nur Adi Tiara Verita Yastica Yuli Adam Prasetyo