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All Journal MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer International Journal of Enterprise Modelling
Galih Prakoso Rizky A
Universitas Pembangunan Nasional Veteran, Jakarta, Indonesia
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering Industrial & Manufacturing Engineering Library & Information Science Mathematics Transportation

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Developing the Adaptive Digital IT Governance Framework for Next-Generation IT Governance Bambang Saras Yulistiawan; Rifka Widyastuti; RR Octanty Mulianingtyas; Galih Prakoso Rizky A; Hengki Tamando Sihotang
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 25 No. 1 (2025)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v25i1.5628

Abstract

The increasing complexity of digital transformation requires an adaptive, measurable, and contextaware IT governance model. However, existing frameworks such as COBIT, ITIL, TOGAF, and ISO/IEC 38500 tend to be partial and prescriptive, failing to address strategic, operational, and innovative needs holistically. This study proposes the Adaptive Digital IT Governance Framework, anovel governance model synthesized from eleven leading IT frameworks and structured into three integrated domains: Govern, Manage, and Adapt. Employing a Design Science Research methodology, the model was developed through a systematic framework analysis, conceptual domain formulation, iterative implementation mapping, and the design of a maturity assessment instrument. The results demonstrate that the Adaptive Digital IT Governance Framework offers a modular, scalable, and value-driven governance solution suited for diverse organizational contexts. Theoretical contributions include extending the IT governance paradigm by integrating strategic alignment, agile governance, and digital sustainability. Practically, the framework provides actionable guidance for designing, assessing, and enhancing digital governance systems across sectors. Unlike previous cross-framework synthesis efforts, the Adaptive Digital IT Governance Framework explicitly introduces the Adapt domain, operationalizing governance agility, innovation capability, and sustainability measurement. This makes the Adaptive Digital IT Governance Framework the first modular, maturity-oriented framework that simultaneously integrates strategy, operations, and adaptability, positioning it as a next-generationmodel to support organizational resilience and sustainable digital transformation.
Content-Based Filtering Using TF-IDF for a Course Recommendation System for Indonesian MSME Entrepreneurs Rr Octanty Mulianingtyas; Bagus Hendra Saputra; Rohani Situmorang; Galih Prakoso Rizky A
International Journal of Enterprise Modelling Vol. 20 No. 2 (2026): May: Enterprise Modelling
Publisher : International Enterprise Integration Association

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/int.jo.emod.v20i2.172

Abstract

Micro, Small, and Medium Enterprises (MSMEs), particularly those led by female entrepreneurs, play a vital role in Indonesia’s economic development; however, digital learning platforms often lack adaptive mechanisms that align course offerings with individual learning needs. Existing platforms generally rely on manual selection or generic categorization, creating a gap in personalized recommendation support within digital entrepreneurship education. The primary objective of this study is to develop and assess a content-based course recommendation system for Femalepreneur.id that addresses this limitation. A quantitative experimental research design was adopted using user profile data collected through questionnaires and course descriptions obtained from the platform repository. The methodology integrates systematic text preprocessing, feature representation using Term Frequency–Inverse Document Frequency (TF-IDF), and similarity computation through cosine similarity to generate personalized recommendations. The experimental results indicate Mean Precision@3 at 51.5%, Mean Average Precision@3 (MAP@3) at 42.9%, and Hit Ratio@3 at 90.9%. The precision matrix demonstrates the system can recommend relevant result until three courses as the maximum value based on the ground truth.  While, Hit Ratio matrix reveals that at least the system can recommend at least one relevant topic.  These findings confirm the effectiveness of TF-IDF in modelling textual learning features and highlight the contribution of the proposed system in strengthening personalized digital entrepreneurship learning for female entrepreneurs.
Co-Authors Bagus Hendra Saputra Bambang Saras Yulistiawan Hengki Tamando Sihotang Rifka Widyastuti Rohani Situmorang RR Octanty Mulianingtyas