https://ejournal.gomit.id/ijaaiml <p>The International Journal of Advances in Artificial Intelligence and Machine Learning (IJAAIML) is a prominent academic journal dedicated to publishing cutting-edge research and developments in the fields of Artificial Intelligence (AI) and Machine Learning (ML). It serves as an essential platform for researchers, practitioners, and professionals worldwide to share innovative ideas, technologies, and empirical studies that contribute to advancing AI and ML. The journal emphasizes both theoretical advancements and practical applications, showcasing how these technologies are shaping various industries, including healthcare, finance, education, robotics, and autonomous systems.</p> CV Media Inti Teknologi en-US International Journal of Advances in Artificial Intelligence and Machine Learning 3089-185X https://ejournal.gomit.id/ijaaiml/article/view/689 <p><strong>Background</strong>: The rapid growth of online learning in higher education requires innovative solutions that support independent, scalable, and standardized learning experiences. Artificial Intelligence (AI), particularly Learning Analytics (LA) and Natural Language Processing (NLP), offers opportunities to enhance digital learning through adaptive content delivery, personalized learning pathways, and automated formative feedback.<br /><strong>Aims</strong>: This study aimed to develop and evaluate an AI-driven adaptive online digital module for Communication courses that supports personalized learning and standardized instruction across higher education institutions using Gemini.<br /><strong>Methods</strong>: This study employed the ADDIE development model, comprising Analysis, Design, Development, Implementation, and Evaluation. Learning Analytics was used to monitor student engagement and learning progress, while NLP analyzed students' written responses to generate automated formative feedback. The module was validated by instructional design, subject matter, and media experts, followed by individual and small-group trials. Its effectiveness was evaluated using normalized gain (N-Gain) analysis.<br /><strong>Results</strong>: Expert validation, individual trials, and small-group evaluations indicated that the developed module achieved a "very good" level of feasibility. The effectiveness evaluation produced a high N-Gain score (0.7), indicating a substantial improvement in student learning outcomes. The integration of Learning Analytics and NLP supported adaptive learning, timely feedback, and increased student engagement.<br /><strong>Conclusion</strong>: The AI-driven adaptive digital module is feasible and effective for supporting online learning in Communication courses. Integrating Learning Analytics and Natural Language Processing enables personalized instruction and data-informed learning support, making the module a promising approach for improving learning quality in higher education.</p> Utari Dewi Andi Kristanto Atan Pramana Husni Mubarok Rizki Fitri Rahima Uulaa Arqoma Nurveda Carreza Favian Avila Syahmi Makibane Daniel Ntlhane Copyright (c) 2026 Utari Dewi, Andi Kristanto, Atan Pramana, Husni Mubarok, Rizki Fitri Rahima Uulaa, Arqoma Nurveda Carreza, Favian Avila Syahmi, Makibane Daniel Ntlhane https://creativecommons.org/licenses/by-sa/4.0 2026-08-21 2026-08-21 3 2 77 88 10.58723/ijaaiml.v3i2.689 https://ejournal.gomit.id/ijaaiml/article/view/662 <p><strong>Background:</strong> In an attempt to solve the problem of internal damage and crack of wind turbine blades that are difficult to be evaluated, a method based on cluster analysis is proposed to study the fault warning of wind turbine blades using machine learning.<br /><strong>Aims:</strong> Developing an early warning method (fault warning) for explicit damage (including surface cracks and internal damage) to wind turbine blades by applying cluster analysis (K-Means) and unsupervised learning to short-term historical operational data.<br /><strong>Method:</strong> Firstly, 2 MW wind turbine was used to collect short-term historical operation data of wind turbine and perform data pre-processing. Secondly, the wind speed-power, wind speed-hub speed and wind speed-tip speed ratio were used to classify the parameters affecting the explicit faults of wind turbine blades by using the cluster analysis method, and the distance analysis was performed by using the combined weight method segmentation, and the axial vibration data of the abnormal data points were analyzed by using the Fourier series transformation. And then, establishing the wind turbine blade explicit fault evaluation rules based on the performance reliability theory. Lastly, the feasibility of the method is verified by cases.<br /><strong>Result:</strong> The results demonstrated that the method can quickly warning the wind turbine blade explicit faults, especially for evaluation of the wind turbine blade surface explicit cracks and internal explicit fault. <br /><strong>Conclusion:</strong> This work has achieved the fault warning of large components based on the wind turbine short-term historical operation data.</p> Yuli Guo Bing Zeng Yirui Qiao Copyright (c) 2026 Yuli Guo, Bing Zeng; Yirui Qiao https://creativecommons.org/licenses/by-sa/4.0 2026-09-09 2026-09-09 3 2 89 98 10.58723/ijaaiml.v3i2.662