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All Journal ADI Journal on Recent Innovation (AJRI) Jurnal MENTARI: Manajemen, Pendidikan dan Teknologi Informasi
Silawati, Nur
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Religion Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Education Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Other

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Analysis of Distance Learning Technology Impact on Accessibility and Educational Effectiveness: Analisis Teknologi Pembelajaran Jarak Jauh Dampak Aksesibilitas dan Efektivitas Pendidikan Azizah, Nur; Firiza, Muhammad Daffa; Sunarya, Po Abas; Silawati, Nur
Jurnal MENTARI: Manajemen, Pendidikan dan Teknologi Informasi Vol 3 No 2 (2025): March
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/mentari.v3i2.739

Abstract

Penelitian ini dilatarbelakangi oleh meningkatnya adopsi teknologi dalam pembelajaran jarak jauh serta tantangan dalam aksesibilitas dan efektivitasnya. Tujuan penelitian ini adalah menganalisis faktor aksesibilitas, kepribadian merek, adopsi teknologi, efektivitas, dan literasi digital dalam mendukung keberhasilan pembelajaran jarak jauh. Metode yang digunakan adalah pendekatan kuantitatif dengan model persamaan struktural (SEM)berbasis SmartPLS, menggunakan data dari survei 300 responden dengan berbagai latar belakang. Hasil penelitian menunjukkan bahwa aksesibilitas dan kepribadian merek berpengaruh signifikan terhadap adopsi teknologi, yang meningkatkan efektivitas pembelajaran. Selain itu, literasi digital berperan penting dalam mengoptimalkan pengalaman belajar. Kesimpulannya, penelitian ini menegaskan bahwa aksesibilitas dan literasi digital adalah faktor kunci dalam meningkatkan efektivitas pembelajaran jarak jauh, sehingga rekomendasi diberikan kepada pengembang teknologi dan pembuat kebijakan untuk meningkatkan aksesibilitas serta dukungan terhadap literasi digital.
Integrating AI-Driven Predictive Analytics and Smart Contracts for Data-Driven Supply Chain Risk Management Pujiati, Tri; Kamil, Mustofa; Silawati, Nur; Ikhsan, Ramiro Santiago
ADI Journal on Recent Innovation Vol. 7 No. 1 (2025): September
Publisher : ADI Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/ajri.v7i1.1318

Abstract

Global supply chains face increasing uncertainty, while traditional risk management often lacks adaptability. This study investigates how AI driven predictive analytics and smart contracts enhance resilience, using mixed methods with case studies and big data analysis. A mixed method approach was employed, combining big data analytics from supply chain networks with machine learning models for predictive forecasting, supported by case studies from multinational manufacturing and logistics companies as well as secondary data from industry reports. The findings reveal that AI driven predictive models significantly improve demand forecasting accuracy, identify potential disruptions earlier, and enhance supplier risk assessment compared to conventional approaches, while integrating data from IoT enabled devices provides real time visibility across logistics operations. Overall, AI powered predictive analytics demonstrates substantial potential in transforming risk management within global supply chains by enabling proactive strategies and resilience, allowing organizations to reduce vulnerabilities, optimize performance, and strengthen competitiveness in dynamic markets, with future research suggested to explore the integration of blockchain for transparency and ethical governance in supply chain ecosystems.
Co-Authors Firiza, Muhammad Daffa Ikhsan, Ramiro Santiago Mustofa Kamil, Mustofa Nur Azizah Po Abas Sunarya Pujiati, Tri