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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Journal of Renewable Energy, Electrical, and Computer Engineering
Antariksa, Muhammad Deagama Surya
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering

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COBIT 2019 Framework in IT Governance: A Systematic Literature Review of Implementation Challenges and Benefits Across Various Industry Sectors Antariksa, Muhammad Deagama Surya; Angin, Maria Perangin; Widodo, Aris Puji
Journal of Renewable Energy, Electrical, and Computer Engineering Vol. 5 No. 1 (2025): March 2025
Publisher : Institute for Research and Community Service, Universitas Malikussaleh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/jreece.v5i1.19501

Abstract

Information technology (IT) governance is a critical factor in ensuring that IT investments align with business objectives, improve operational efficiency, and mitigate risks across various industries. COBIT 2019, a widely used IT governance framework, has been adopted in numerous sectors to address these needs. This study explores the implementation of COBIT 2019 by conducting a systematic literature review (SLR) of 23 relevant articles published between 2020 and 2024, sourced from internationally indexed journals. The research focuses on identifying the challenges, benefits, success factors, and strategies to mitigate the issues encountered during the implementation of COBIT 2019. The findings indicate that COBIT 2019 has been successfully applied in diverse industries such as education, healthcare, logistics, and mineral mining, demonstrating its flexibility and adaptability to different organizational contexts. Key benefits include improved alignment of IT with business strategies, enhanced risk management, better resource optimization, and increased operational efficiency. However, challenges such as the complexity of the framework, limited resources, and a lack of understanding of the frameworks terminology have been identified. Successful implementation is largely dependent on strong management support, stakeholder engagement, and adequate resource allocation. Mitigation strategies such as ongoing training, development of comprehensive communication plans, and regular evaluation of IT governance practices have been suggested to overcome these challenges. This study provides practical insights into the implementation of COBIT 2019, offering organizations guidance on maximizing its benefits while addressing the barriers to successful implementation.
BERT Model Fine-tuned for Scientific Document Classification and Recommendation Antariksa, Muhammad Deagama Surya; Sugiharto, Aris; Surarso, Bayu
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 4 (2025): August 2025 (in progress)
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i4.6789

Abstract

The increasing number of academic documents requires efficient and accurate classification and recommendation systems to assist in retrieving relevant information. This system is built using the "bert-base-uncased” model from Hugging Face, which has been fine-tuned to improve the classification accuracy and relevance of document recommendations. The dataset used consists of 2.000 academic documents in the field of computer science, with features including titles, abstracts, and keywords, which were combined into a single input for the model. Document similarity is measured using cosine similarity, resulting in recommendations based on semantic proximity. Unlike traditional approaches, which rely primarily on word frequency or surface-level matching, the proposed method leverages BERT’s contextual embeddings to capture deeper semantic meanings and relationships between documents. This allows for more accurate classification and more context-aware recommendations. Evaluation results show that the best model configuration (learning rate 3e-5, batch size 32, optimizer AdamW) achieved 89.5% training accuracy and an F1-score of 0.8947, while testing yielded 91% accuracy and 90% F1-score. The recommendation system consistently produced Precision@k values above 92% for k between 5 and 30, with Recall@k reaching 1.0 as k increased. These results indicate that the system not only performs reliably in classifying complex academic texts but also effectively recommends contextually relevant documents. This integrated approach shows strong potential for enhancing academic document retrieval and supports the development of semantically aware information management systems.
Co-Authors Angin, Maria Perangin Aris Puji Widodo Aris Sugiharto Bayu Surarso