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All Journal Jurnal MENTARI: Manajemen, Pendidikan dan Teknologi Informasi International Transactions on Artificial Intelligence (ITALIC) Journal of Computer Science and Technology Application
Pasha, Lukita
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Languange, Linguistic, Communication & Media Library & Information Science Other

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Improving Equity in Remote Learning through Adaptive E-Learning Technologies for Multicultural and Multilingual Learners Pasha, Lukita; Fitriani, Anandha; Hua, Chua Toh; Daeli, Mardaleni
Jurnal MENTARI: Manajemen, Pendidikan dan Teknologi Informasi Vol 4 No 1 (2025): September
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/mentari.v4i1.900

Abstract

The rapid growth of remote learning has exposed significant inequalities in digital education access and learning outcomes, particularly for multicultural and multilingual learners. This study aims to explore how adaptive e-learning technologies can enhance equity in distance education by personalizing learning experiences and accommodating diverse linguistic and cultural backgrounds. A mixed-methods approach was used, involving surveys and interviews with students and teachers from three multicultural secondary schools in Indonesia. Quantitative data were collected through pre- and post-tests measuring learning engagement and comprehension, while qualitative insights were obtained through focus group discussions. The results indicate a significant improvement, with average pre-test scores of 65.3 increasing to 78.9 in the post-test (p < 0.05). Student engagement also rose, as weekly study time increased from 5.2 to 6.5 hours and completed modules improved by 30%. The adaptive technologies evaluated include multilingual user interfaces, culturally responsive learning content, and real-time feedback systems. The findings show a notable in crease in student motivation, participation, and academic performance among marginalized learner groups. Additionally, students reported a greater sense of inclusion and relevance in their digital learning environments. The study concludes that adaptive e-learning technologies can significantly reduce educational disparities when combined with inclusive instructional design. It recommends the integration of localized content and supportive policies to sustain equitable digital education for all learners, particularly in culturally diverse settings.
Advanced Cyber Threat Detection: Big Data-Driven AI Solutions in Complex Networks Rizky, Agung; Zaki Firli, Muhammad; Aulia Lindzani, Nur; Audiah, Sipah; Pasha, Lukita
CORISINTA Vol 1 No 2 (2024): August
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/corisinta.v1i2.42

Abstract

In the rapidly evolving digital landscape, cybersecurity has become increasingly critical, especially within complex network environments. This research presents the development of a cyber threat detection system that leverages Artificial Intelligence (AI) and Big Data analytics to enhance accuracy and speed in identifying and responding to cyber threats. The system was evaluated through rigorous testing, demonstrating a high detection accuracy of 95\% for malware and unauthorized access attempts, along with an impressive detection speed of 2 seconds on average for most threats. Additionally, the system exhibited strong scalability, maintaining optimal performance even with increasing network complexity. These findings underscore the system's robustness and practical applicability in real-world scenarios. However, further refinement is suggested to improve anomaly detection and reduce response times for more complex threats. This study contributes valuable insights into the integration of AI and Big Data in cybersecurity, providing a scalable and effective solution for protecting critical network infrastructures.
Integrating MidJourney Scripts into Architectural Design for Aesthetic Innovation Aini, Qurotul; Setiyowati, Harlis; Ikhsan, Ramiro Santiago; Amroni, Amroni; Pasha, Lukita
International Transactions on Artificial Intelligence Vol. 4 No. 1 (2025): November
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/italic.v4i1.955

Abstract

The use of Artificial Intelligence (AI) in creative disciplines, particularly architecture, has introduced a paradigm shift in how design concepts are developed and visualized. This research explores the integration of MidJourney generative scripts within the architectural design process. Using a hybrid methodology of qualitative observation and computational experimentation, this study evaluates how AI-driven image generation influences form exploration, material perception, and aesthetic decision-making. The main objective is to identify how AI-based generative systems, specifically MidJourney, can enhance conceptual creativity and accelerate the design iteration cycle in architectural practice. Unlike previous procedural models limited to parametric control, this study introduces an adaptive AI human feedback mechanism enabling continuous co-evolution between designer intent and machine generation. Our findings indicate that AI-assisted workflows enhance the production of innovative architectural compositions, offering greater visual diversity, while simultaneously enhancing creative efficiency and reducing design fatigue. Quantitatively, AI integration improved design iteration speed by 65% and increased aesthetic consistency scores from 78% to 91%, compared to traditional workflows. Integrating MidJourney generative scripting into architectural workflows creates a dynamic feedback loop between human intuition and machine creativity, leading to a new model of aesthetic co-creation that expands the boundaries of contemporary architectural design.
Co-Authors Amroni, Amroni Audiah, Sipah Aulia Lindzani, Nur Daeli, Mardaleni Fitriani, Anandha Harlis Setiyowati Hua, Chua Toh Ikhsan, Ramiro Santiago Qurotul Aini Rizky, Agung Zaki Firli, Muhammad