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All Journal Mechatronics, Electrical Power, and Vehicular Technology Semantik TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Teknik dan Ilmu Komputer Sinergi Majalah Kedokteran Gigi Indonesia Journal of Mechanical Engineering Education (Jurnal Pendidikan Teknik Mesin) ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Techne : Jurnal Ilmiah Elektroteknika Journal of Robotics and Control (JRC)
Daniel Santoso
Universitas Kristen Satya Wacana
Aerospace Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Dentistry Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Mechanical Engineering Other

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Journal : Journal of Robotics and Control (JRC)

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The Voltage Control in Single-Phase Five-Level Inverter for a Stand-Alone Power Supply Application Using Arduino Due Santoso, Daniel; Pratomo, Leonardus Heru
Journal of Robotics and Control (JRC) Vol 2, No 5 (2021): September (Forthcoming Issue)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

In the era of industrial revolution 4.0 expanded digital transformation, such as inverters. The principle of the inverter is to change the DC source to the AC source. The device using an AC source needs a voltage source that is controlled. Hence, the voltage source that is controlled is usually generated from a stand-alone power supply. The stand-alone power supply usually used a conventional inverter. The conventional inverter uses high frequency switching to obtain lower distortion harmonic in output voltage. Another solution is using a five-level inverter that has fewer power switches. The purpose in these research is to make a stand-alone power supply using a single-phase five-level inverter asymmetric topology, which has five power switches to control voltage output based on the standard of IEEE 519. The inverter does sinusoidal pulse width modulation on two the signal reference that was shifting 180 degrees toward the carrier signal. That research has been simulated using Power Simulator software and has been implemented in the laboratory. According to the result of simulation and implementation are generated voltage THD value amount of 4.39%.
Unveiling the Predictive Power of Machine Learning and Deep Learning: A Comparative Study on Disease Diagnosis, Detection, and Mortality Risk in Healthcare Santoso, Daniel; Firdaus, Asno Azzawagama; Yunus, Muhajir; Pangri, Muzakkir
Journal of Robotics and Control (JRC) Vol. 6 No. 4 (2025)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v6i4.26223

Abstract

This study compares the roles of machine learning (ML) and deep learning (DL) in healthcare, focusing on their applications, challenges, and prospects. It addresses the increasing relevance of AI in public health systems and contributes a structured analysis of how ML and DL process different healthcare data types. A systematic literature review was conducted using sources from Google Scholar, Elsevier, Springer, IEEE, and MDPI, applying inclusion criteria based on relevance, publication quality, and recency (2018–2024). Article selection and synthesis using meta-analysis followed the PRISMA framework. The review identified four key application areas: (1) disease outbreak prediction, (2) disease forecasting, (3) disease diagnosis and detection, and (4) disease hotspot monitoring and mapping. ML techniques such as Random Forest and ensemble methods show high performance in handling structured data like patient records, whereas DL architectures like convolutional neural network (CNN) and long-short term memory (LSTM) are superior for unstructured data, including medical imaging and bio signals. Challenges common to both approaches include data quality issues, dataset bias, privacy concerns, and integration into existing healthcare infrastructures. Looking forward, promising directions include explainable AI (XAI), transfer learning, federated learning, and real-time data use from wearable and internet of things (IoT) devices. The study concludes that while ML and DL can significantly improve diagnosis, response to health threats, and resource allocation, maximizing their impact requires continuous cross-sector collaboration, transparency, and ethical governance.
Co-Authors Adi Gunawan Andika Sukma Deryawan Anton Suprayudi Ashidqi, Muhamad Dzaky Asno Azzawagama Firdaus Bob William Chandra Deddy Susilo Fransiscus Dalu Setiaji Heriyanti Amalia Kusuma Jati Wasesa Leonardus Heru Pratomo M.Th. Esti Tjahjanti Muhajir Yunus Muzakkir Pangri Oei Kurniawan Utomo Oyas Wahyunggoro Prapto Nugroho Prapto Nugroho Roni Wijaya Sanjaya, Kurnia Saptadi Nugroho Sriyono Sriyono Tri Handoko Wahid Munawar Yayi Suryo Prabandari Yonas Aditya Darmawan