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All Journal PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic International Journal of Applied Mathematics, Sciences, and Technology for National Defense
Rahmat Budiarto
Bina Insani University
Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Mathematics Physics

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Cybersecurity and AI as enablers of economic resilience: A framework for sustainable growth in developing countries Ali Siraj Ahmed; Rahmat Budiarto
International Journal of Applied Mathematics, Sciences, and Technology for National Defense Vol. 3 No. 2 (2025): International Journal of Applied Mathematics, Sciences, and Technology for Nati
Publisher : FoundAE

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58524/app.sci.def.v3i2.857

Abstract

Digital transformation offers unprecedented opportunities for sustainable economic growth in developing countries. However, these benefits are accompanied by increased cybersecurity risks and challenges in integrating emerging technologies like artificial intelligence (AI). This paper proposes a conceptual framework that positions cybersecurity and AI as dual enablers of economic resilience aligned with Sustainable Development Goal 8 (SDG 8). The framework comprises three interconnected pillars: cybersecurity infrastructure, AI-driven economic transformation, and AI-enhanced cybersecurity mechanisms. A case study of Sudan illustrates how tailored interventions can foster secure, inclusive, and resilient digital economies in low- and middle-income contexts. The study highlights the importance of ethical governance, multi-stakeholder collaboration, and capacity building to maximize the benefits of AI while mitigating risks. Future research directions include empirical validation and policy experimentation to refine the framework and accelerate digital-led economic resilience in the Global South.
Data-Driven Insights for Higher Education Marketing: Segmenting Applicant Pools Using K-Means Clustering Jafar Shadiq; Harjunadi Wicaksono; Rahmat Budiarto; Raisha Nur Salamah; Zidan Al Buqhori Fakhrudin
PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Vol. 13 No. 2 (2025): September 2025
Publisher : LPPM Universitas Islam 45 Bekasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33558/piksel.v13i2.11545

Abstract

This research aims to optimize marketing strategies for new student recruitment at Bina Insani University (BiU), which faces intense competition. The current marketing efforts are generic and inefficient. Utilizing the CRISP-DM framework, this study applies the K-Means clustering data mining method to analyze primary data from applicants from 2021 to 2024. The analysis focuses on the attributes of previous school major, information source, and location. The findings successfully identified four distinct segments of prospective students: the "Proactive Outreach Segment," reached through school presentations; the "Social Network & Affiliation Segment," influenced by friends and relatives; the "Academic Recommendation Segment," who rely on guidance from teachers; and the "Digital & Non-Technical Segment," who actively seek information on social media. Based on the unique profile of each cluster, this study provides recommendations for specific and targeted marketing strategies to improve the effectiveness and efficiency of student recruitment
Co-Authors Ali Siraj Ahmed Harjunadi Wicaksono Jafar Shadiq Raisha Nur Salamah Zidan Al Buqhori Fakhrudin