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All Journal Bulletin of Electrical Engineering and Informatics Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Piston: Journal of Technical Engineering Buletin Ilmiah Sarjana Teknik Elektro Makara Journal of Technology JHeS (Journal of Health Studies) International Journal of Electrical, Computer, and Biomedical Engineering (IJECBE)
Tomy Abuzairi
Departemen Teknik Elektro, Fakultas Teknik, Universitas Indonesia
Aerospace Engineering Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering Medicine & Pharmacology Nursing Public Health

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Pulse Oximeter Monitoring Bracelet for COVID-19 Patient using Seeeduino Suhartina, Rahmalisa; Abuzairi, Tomy
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 7 No. 1 (2021): April
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v7i1.20529

Abstract

The increase in positive cases of COVID-19 makes it grave to monitor the level of oxygen saturation in the blood (SPO2) of COVID-19 patients. The purpose is to prevent silent hypoxia, which lowers oxygen levels in the blood without symptoms. In general, a conventional pulse oximeter is a clip that is clamped on a finger to measure SPO2 levels and heart rate per minute (HR). This research aims to design a compact pulse oximeter monitoring bracelet. The main components of the pulse oximeter monitoring bracelet are the Seeeduino XIAO microcontroller, MAX30100 sensor, and OLED display. The method of collecting data on ten people using a conventional pulse oximeter and prototype device to measure SPO2 and HR levels the interval 30 seconds were a taken measurement. The results show that the Pearson correlation value for SPO2 and HR are -0.73 and 0.98, respectively. These results demonstrated that there is a strong relationship between variables and sufficient linearity. In addition, a pulse oximeter monitoring bracelet is easy to use and low-costs, which makes it an attractive option for the successful implementation of such monitoring SPO2 and HR of COVID-19 patients.
Simple Simulation of Perturb and Observe MPPT Algorithm on Synchronous Buck Converter Abuzairi, Tomy; Rachmad, Ralfi Wibowo
Buletin Ilmiah Sarjana Teknik Elektro Vol. 5 No. 4 (2023): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v5i4.9347

Abstract

The efficiency of the PV system can be improved by operating the solar panel on its Maximum Power Point (MPP). However, ariations in irradiance and temperature will lead to the shifting of solar panel MPP. To continuously operate the solar panel near its MPP, a tracking algorithm is needed. In this research, a model consisting of a synchronous buck converter and a Maximum Power Point Tracking (MPPT) algorithm will be designed as aMATLAB/Simulink model. Perturb and Observe technique will be used to implement the algorithm into the synchronous buck converter, which will control a 10 W solar panel load so it will operate near its MPP. Results show that the PV system model can track the Solar Panel MPP in various simulated irradiance.
Automated Young Children’s Pain Detection via Facial Expressions with YOLO v11 Ramdhanie, Gusgus Ghraha; Nurdina Widanti; Bambang Aditya Nurgraha; Tomy Abuzairi; Nur Agustini; Dessie Wanda
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 1 (2026): February 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i1.7206

Abstract

This study demonstrates that pain detection in young children using a YOLO v11-based deep learning model can be performed effectively. By utilizing image data taken from video recordings of immunization and IV infusion procedures, then processed into photo frames and labeled using Roboflow, the model is able to provide good evaluation results. The dataset was divided into 70:20:10 for training, validation, and testing. Model performance evaluation uses accuracy, precision, recall, and F1-score metrics, and is visualized through a performance curve and confusion matrix. The results show that YOLO v11 has great potential as a pain detection method, with an mAP@0.5 achievement of 0.893, an accuracy of 78%, a precision of 89.3%, a recall of 97%, and an F1-score of 83%. The high recall value indicates the model's excellent ability to recognize pain expressions, making it relevant for use in clinical contexts to ensure pain symptoms are not overlooked. Overall, this performance demonstrates that YOLO v11 can be a more objective and accurate approach than manual instruments, and has the potential to be developed as a tool for healthcare professionals in pediatric pain assessment.
Co-Authors Adam Febriyanto Nugraha Ajinugroho, Fajar Arie Kusumaningrum Bambang Aditya Nurgraha Bohr-Ran Huang Bohr-Ran Huang, Bohr-Ran Dimas Agung Setiaji Gusgus Ghraha Ramdhanie Junivan Sulistianto Maurice Efroza Mochamad Chalid Muchamad Syahrul Gunawan Naufal Inas Fikri Nji Raden Poespawati Nur Agustini Nurdina Widanti Pandi Rachmad, Ralfi Wibowo Rahmalisa Suhartina Ralfi Wibowo Rachmad Retno Wigajatri Purnamaningsih Rizqy Pratama Rahman Siti Fauziyah Solly Aryza Suhartina, Rahmalisa Tzu-Ching Lin Tzu-Ching Lin, Tzu-Ching Vito Louis Nathaniel Wanda , Dessie Yeni Rustina