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All Journal ARMADA : Jurnal Penelitian Multidisiplin Bisnis, Jasa dan Keuangan
Abdul Wajid Fazil
Department of Information Systems, Faculty of Computer Science, Badakhshan University, Badakhshan, Afghanistan
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PERAN PASAR MODAL DALAM MENDORONG PERTUMBUHAN EKONOMI DI INDONESIA Nuhaeni; Heri Irawan; Srianti Permata; Supriadi Muslimin; Abdul Wajid Fazil
Bisnis, Jasa dan Keuangan Vol. 1 No. 2 (2025): JULI-BISNIS,JASA DAN KEUANGAN
Publisher : Yayasan Cendekia Citra Gemilang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61798/kgazmd79

Abstract

Penelitian ini dilakukan untuk  mengetahui peran pasar modal dalam mendorong pertumbuhan ekonomi negara. Jenis penelitian adalah penelitian Pustaka (kajian Pustaka) dengan metode konten analisis. Oleh karena itu, pembahasan penelitian ini didasarkan pada tinjauan pustaka serta beberapa karya yang relevan dengan topik penelitian. Analisis konten adalah metode analisis data yang digunakan. Ini adalah metode penelitian yang digunakan untuk membuat kesimpulan yang relevan dan reflikatif dari data berdasarkan konteksnya. Hasil Penelitian menunjukan bahwa untuk meningkatkan perekonomian indonesia adalah  dengan memperkuat sektor investasi melalui pasar modal. Hasil Penelitian menunjukkan bahwa pasar modal perlu mendapat perhatikan khusus dari pemerintah Indonesia, karena pasar modal dan perbankan merupakan sarana untuk masyarakat yang memiliki  modal serta pihak yang  memerlukan dana.
Integrating Blockchain and Machine Learning for Predictive Cyber Defense Systems Ahmad Jamy Kohistani; Irfanullah Azimi; Abdul Wajid Fazil
ARMADA : Jurnal Penelitian Multidisiplin Vol. 3 No. 12 (2025): ARMADA : Jurnal Penelitian Multidisplin, December 2025
Publisher : LPPM Sekolah Tinggi Ilmu Ekonomi 45 Mataram

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Abstract

The rapid expansion of cyber threats targeting critical infrastructures highlights the limitations of traditional centralized security systems, which suffer from latency, scalability constraints, and single points of failure. This study addresses this problem by examining how the integration of Blockchain and Machine Learning (ML) can strengthen predictive cyber defense and enhance real-time anomaly detection. The purpose of the research is to synthesize current evidence on the security, efficiency, and operational benefits of Blockchain–ML frameworks through a Systematic Literature Review (SLR). Following PRISMA guidelines, a structured search was conducted across four major databases IEEE Xplore, ScienceDirect, Scopus, and Web of Science covering peer-reviewed literature published between 2020 and 2025. Using a four-category keyword strategy, the review initially identified 1100 records, ultimately narrowing the final dataset to 25 studies that met all inclusion criteria. The results indicate that Blockchain significantly enhances data integrity, auditability, and threat-intelligence reliability, while ML improves predictive accuracy and supports real-time detection. Together, these technologies outperform conventional centralized systems in terms of transparency, resilience, and operational efficiency. The study concludes that Blockchain–ML integration provides a robust foundation for next-generation, decentralized cybersecurity architectures, offering measurable improvements in security and system performance.
Co-Authors Ahmad Jamy Kohistani Heri Irawan Irfanullah Azimi Nuhaeni Srianti Permata Supriadi Muslimin