Dr. Raden Arief Setyawan, ST., MT.

Overview

Dr. Raden Arief Setyawan, ST., MT., a distinguished faculty member of the Faculty of Engineering in the Department of Electrical Engineering, will deliver a lecture that examines the evolving landscape of research output within the department. The analysis draws on a representative sample of publication titles extracted from the department’s repository, revealing several noteworthy patterns that illustrate both thematic focus and methodological diversification over recent years.

First, the data show a pronounced concentration on Internet‑of‑Things (IoT) and monitoring systems. Titles such as “Sistem Monitoring Sungai Brantas berbasis Internet of Things,” “Pemanfaatan Teknologi Internet of Thing untuk Peringatan Awal Bahaya Banjir dan Angin Kencang di Kota Batu,” and “IOT Based Weather Monitoring System” underscore a sustained interest in deploying sensor networks for environmental and infrastructure monitoring. This trend reflects the department’s strategic alignment with smart city initiatives and the growing demand for real‑time data acquisition in engineering practice.

Second, computer vision and stereo‑vision technologies appear repeatedly, as evidenced by multiple entries on stereo vision distance measurement and semi‑global block matching methods. The recurrence of identical titles—such as “Implementation of stereo vision semi‑global block matching methods for distance measurement”—suggests ongoing refinement of algorithms, collaborative projects, or series of studies that build upon earlier findings. This indicates a robust research pipeline in the field of machine perception, likely driven by interdisciplinary collaborations with the computer science and electrical engineering departments.

Third, the presence of AI‑centric research—e.g., “YOLOv3 with Spatial Pyramid Pooling for Object Detection with Unmanned Aerial Vehicles” and “Forecasting Rice Status for a Food Crisis Early Warning System Based on Satellite Imagery and Cellular Automata”—highlights the department’s engagement with advanced machine learning techniques applied to both agricultural and surveillance contexts. The integration of satellite imagery, cellular automata, and deep learning models points to a multidisciplinary approach that leverages data science to address societal challenges.

Fourth, the dataset contains a mix of Indonesian and English titles, reflecting the department’s commitment to both local relevance and international visibility. The bilingual nature of the publications suggests an effort to disseminate findings to a broad audience while maintaining strong ties to regional research priorities.

Finally, the duplication of certain titles indicates a systematic approach to research dissemination, possibly through conference series, journal special issues, or collaborative workshops. This pattern may also signal the department’s emphasis on building a cohesive research narrative that evolves over time, allowing for incremental improvements and cumulative knowledge building.

In sum, the publication data reveal a department that is deeply invested in IoT, computer vision, and AI, with a clear trajectory toward integrating these technologies for environmental monitoring, agricultural forecasting, and smart infrastructure. The recurring themes and methodological consistency suggest a mature research ecosystem that balances foundational studies with applied innovation, positioning the Department of Electrical Engineering as a leader in addressing contemporary engineering challenges.

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Research Interest

• IoT-based systems
• Climate monitoring
• Distance measurement
• Stereo vision
• Machine learning
• ANFIS
• Multiple Linear Regression
• Object detection
• Unmanned Aerial Vehicles (UAVs)
• Greenhouse automation
• Coastal conservation
• E-learning server

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Qualifications

• Doktor S3, UNIVERSITAS BRAWIJAYA
• Magister S2, UNIVERSITAS INDONESIA
• Sarjana S1, UNIVERSITAS BRAWIJAYA