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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Kalbisocio: Jurnal Bisnis dan Komunikasi The Indonesian Journal of Computer Science
Syalevi, Rahmad
Unknown Affiliation
Humanities Computer Science & IT Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Social Sciences

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Journal : The Indonesian Journal of Computer Science

 1 
PERENCANAAN STRATEGIS SI/TI MENGGUNAKAN FRAMEWORK WARD & PEPPARD: STUDI KASUS UNIVERSITAS PARAMADINA Syalevi, Rahmad; Nazief, Bobby A.A; Barcah, Quintin K. D.
The Indonesian Journal of Computer Science Vol. 14 No. 5 (2025): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v14i5.4978

Abstract

Universitas Paramadina (UPM) requires information technology (IT) infrastructure and services aligned with its vision, mission, and business processes to support its educational and research missions. Challenges in managing IT services at UPM necessitate a strategic IS/IT plan. Ward & Peppard is the approach used in this study to build an IS/IT strategic plan aligned with UPM's strategic objectives. The business model canvas, value chain analysis, critical success factor, PESTEL, mcfarlan’s strategic grid, and Gartner technology trends are employed as supporting analytical instruments. Data is collected through interviews, direct observations, and reviews of relevant documentation. Using thematic analysis and open coding methods, this research designs an IS/IT strategic plan for UPM. The results include recommendations for 8 new applications and 17 updates to existing applications, 13 IT initiatives focusing on infrastructure adjustments, and 10 IS/IT management strategies covering policy development, governance structure, data management, and IT audits. This strategic roadmap is developed for the next five years to enhance UPM's added value and competitive advantage through optimized IT.
Perancangan Early Warning System Berbasis Data Warehouse untuk Pencegahan Mahasiswa Drop Out Syalevi, Rahmad; Purnama, Diki Gita; Ayu, Jenar Mahesa
The Indonesian Journal of Computer Science Vol. 15 No. 1 (2026): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v15i1.5075

Abstract

Higher education institutions require integrated, analytics-based data management to support strategic decision-making and student drop out prevention. This study aims to design a Data Warehouse (DW) model as the foundation for an Early Warning System (EWS) to detect student drop out risks at Universitas Paramadina. The DW is designed using the Kimball lifecycle approach with a star schema implementation, integrating data from multiple business processes such as academics, finance, and LMS activities. The EWS is developed using a supervised learning classification approach, utilizing Logistic Regression as the baseline model and proposing Random Forest for advanced modeling. The results demonstrate that an integrated DW effectively supports machine learning-based predictive analytics and serves as a strategic framework for proactive student drop out prevention.
Co-Authors Ayu, Jenar Mahesa Barcah, Quintin K. D. Diki Gita Purnama, Diki Gita Jaya, Suhendra Saputra Melani Puspasari, Hasna Nazief, Bobby A.A Subagio, Aris Tri Utami, Avita Yova Ruldeviyani Zharif Mustaqim, Ilham