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All Journal Jurnal Pengabdian Masyarakat Bangsa Jurnal Kecerdasan Buatan dan Teknologi Informasi
Zaman, Zain Nur
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Agriculture, Biological Sciences & Forestry Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Economics, Econometrics & Finance Education Energy Health Professions Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Mathematics Medicine & Pharmacology Nursing Physics Public Health Social Sciences

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Sosialisasi Pengenalan Artificial Intelligence (AI) untuk Meningkatkan Literasi Digital Siswa Madrasah Ibtidaiyah Ma'arif Nu Tidu Zaman, Zain Nur; Matsuka, Risqi Akbar; Soedarmaji, Prayogo Bagus; Al Azkiya, Zidan Azka; Al Bana, Muhammad Hanif
Jurnal Pengabdian Masyarakat Bangsa Vol. 3 No. 11 (2026): Januari
Publisher : Amirul Bangun Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59837/jpmba.v3i11.3832

Abstract

Perkembangan teknologi digital mendorong perlunya peningkatan literasi digital sejak usia dini, termasuk pemahaman dasar mengenai Artificial Intelligence (AI). Namun, pada jenjang pendidikan dasar, khususnya di lingkungan madrasah ibtidaiyah, pengenalan AI masih sangat terbatas. Kegiatan pengabdian kepada masyarakat ini bertujuan untuk memberikan pemahaman awal tentang konsep Artificial Intelligence kepada siswa MI Ma’arif NU Tidu serta menumbuhkan minat dan kesadaran siswa terhadap penggunaan teknologi secara bijak. Metode pelaksanaan kegiatan menggunakan pendekatan sosialisasi edukatif dan interaktif melalui media visual, simulasi sederhana, permainan edukatif, serta diskusi ringan yang disesuaikan dengan karakteristik siswa sekolah dasar. Hasil kegiatan menunjukkan adanya peningkatan pemahaman siswa terhadap konsep dasar AI, di mana siswa mulai mampu mengenali contoh penerapan AI dalam kehidupan sehari-hari. Selain itu, antusiasme dan partisipasi aktif siswa selama kegiatan menjadi indikator bahwa pendekatan pembelajaran yang kontekstual dan menyenangkan efektif dalam meningkatkan literasi digital. Kegiatan ini diharapkan dapat menjadi langkah awal dalam memperkuat literasi digital siswa madrasah ibtidaiyah serta menjadi model kegiatan pengabdian yang dapat diterapkan pada lingkungan pendidikan dasar lainnya.  
A SYSTEMATIC LITERATURE REVIEW ON THE INTEGRATION OF ARTIFICIAL INTELLIGENCE IN INFORMATION SYSTEM REQUIREMENTS ANALYSIS Matsuka, Riski Akbar; Prayogo Bagus Sudarmaji; Zaman, Zain Nur; Ilham Albana
Jurnal Kecerdasan Buatan dan Teknologi Informasi Vol. 5 No. 2 (2026): May 2026
Publisher : Ninety Media Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69916/jkbti.v5i2.452

Abstract

Requirements analysis is a critical phase in the development of information systems, as it significantly influences the overall success of a system. However, traditional approaches to requirements analysis are often performed manually and are prone to errors, inconsistencies, and inefficiencies. The advancement of Artificial Intelligence (AI) provides new opportunities to improve the effectiveness and automation of this process. This study aims to analyze the integration of AI in requirements analysis using a Systematic Literature Review (SLR) approach. The review follows the PRISMA 2020 guidelines and examines relevant studies published between 2020 and 2025. A total of 14 selected articles were analyzed to identify commonly used AI techniques, evaluate their effectiveness, and explore existing challenges. The results indicate that various AI techniques, including Machine Learning, Deep Learning, Transformer-based models, and Large Language Models (LLMs), have been widely applied in requirements analysis tasks such as classification, ambiguity detection, information extraction, and prioritization. These techniques demonstrate improvements in accuracy, time efficiency, and consistency compared to conventional methods. Despite these advantages, several challenges remain, including data imbalance, limited model generalization, lack of explainability, and limited validation in real-world industrial environments. Therefore, further research is needed to enhance the reliability and applicability of AI-based approaches in practical settings.
Co-Authors Al Azkiya, Zidan Azka Al Bana, Muhammad Hanif Ilham Albana Matsuka, Riski Akbar Matsuka, Risqi Akbar Prayogo Bagus Sudarmaji Soedarmaji, Prayogo Bagus