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All Journal Journal of Electronics, Electromedical Engineering, and Medical Informatics Journal of Robotics and Control (JRC) Indonesian Journal of Electrical Engineering and Computer Science Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Media of Computer Science Scientific Journal of Engineering Research Jurnal Teknokes
Abdullayev, Vugar
Unknown Affiliation
Aerospace Engineering Biochemistry, Genetics & Molecular Biology Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mechanical Engineering

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Performance Comparison of ECG Bio-Amplifier Between Single and Bi-Polar Supply Using Spectrum Analysis Based on Fast Fourier Transform Maghfiroh, Anita Miftahul; Musvika, Syevana Dita; Abdullayev, Vugar
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 4 No. 4 (2022): November
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v4i4.156

Abstract

Heart performance is one of the vital signs that cannot be ignored and must be monitored periodically. In this case, the measuring range of the human heart rate is between 60-100 BPM, in which the measurement unit is expressed as Beat per Minute (BPM). Therefore, it is very important to use Electrocardiograph equipment to tap the electrical signals of the heart with correct readings and minimal interference such as frequency of electric lines and noise. The purpose of this study was to compare the instrumentation amplifier using a single supply with a bi-polar supply in the ECG design to select the best instrumentation amplifier, which is expected to contribute to other researchers in choosing the right type of instrumentation amplifier that is efficient and qualified. In this case, the research was carried out by comparing two single supply instrumentation amplifiers using the AD623 IC and the bi-polar supply using the AD620 IC, continued by the use of Fast Fourier Transform (FFT) to determine the frequency spectrum of the ECG signal. The test results further showed that the use of single power instrumentation could reduce more noise compared to the Bi-Polar instrumentation amplifier by strengthening 60 dB Low pass filter circuit. Meanwhile, the FFT results in finding the frequency spectrum explained that the FFT results on the ECG signal provided information that the ECG signal had a frequency range between 0.05 Hz and 100 Hz. When the frequency is more than 100 Hz, the frequency started to be suppressed and when the frequency is less than 100 Hz, the frequency is passed. This research could be further used as a reference by other researchers to determine which type of instrumentation amplifier is better.
Analysis of Temperature Distribution in Blood Banks Through Storage of Measurement Results with IoT Monitoring in the Blood Donation Unit of Indonesian Red Cross Surabaya Wardhana, Farisy Azis Satria; Maghfiroh, Anita Miftahul; Titisari, Dyah; Sumber, Sumber; Abdullayev, Vugar
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 5 No. 2 (2023): May
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v5i2.170

Abstract

Temperature or temperature is an indicator of the degree of heat of an object. Cold chain or cold chain is a supply chain system that considers the temperature level in the process. Cold chain to keep frozen or chilled products in an environment with a certain temperature during production, storage, transportation, processing and sales. This is intended to maintain product quality. The purpose of this study was to determine the temperature distribution in the Blood bank at Blood Transfusion Unit Indonesian Red Cross Surabaya City which was used for storage of blood products. By using the ESP32 system and the DS18B20 temperature sensor which will then be monitored via IoT, it will make it easier for users to monitor. The results of these measurements will be stored in a micro SD card for analysis. The data is processed by Non-Parametric Test resulting in an interpretation that the temperature of each shelf is different due to the difference in the location of the sensor placement. The temperature difference is also influenced by the pattern of use and the process of heat transfer from the bottom to the top of the shelf. This research was considered successful with the result of the highest temperature distribution being 3°C and the lowest being 2°C. The location of these racks can be useful in determining day-to-day monitoring measuring points. This value has met the standard for storage of blood products, which is in the range of 2°C-6°C.
Comparative Analysis of YOLO11 and Mask R-CNN for Automated Glaucoma Detection Fayyadh, Muhammad Naufaldi; Saragih, Triando Hamonangan; Farmadi, Andi; Mazdadi, Muhammad Itqan; Herteno, Rudy; Abdullayev, Vugar
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 8 No 1 (2026): January
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v8i1.1266

Abstract

Glaucoma is a progressive optic neuropathy and a major cause of irreversible blindness. Early detection is crucial, yet current practice depends on manual estimation of the vertical Cup-to-Disc Ratio (vCDR), which is subjective and inefficient. Automated fundus image analysis provides scalable solutions but is challenged by low optic cup contrast, dataset variability, and the need for clinically interpretable outcomes. This study aimed to develop and evaluate an automated glaucoma screening pipeline based on optic disc (OD) and optic cup (OC) segmentation, comparing a single-stage model (YOLO11-Segmentation) with a two-stage model (Mask R-CNN with ResNet50-FPN), and validating it using vCDR at a threshold of 0.7. The contributions are fourfold: establishing a benchmark comparison of YOLO11 and Mask R-CNN across three datasets (REFUGE, ORIGA, G1020); linking segmentation accuracy to vCDR-based screening; analyzing precision–recall trade-offs between the models; and providing a reproducible baseline for future studies. The pipeline employed standardized preprocessing (optic nerve head cropping, resizing to 1024×1024, conservative augmentation). YOLO11 was trained for 200 epochs, and Mask R-CNN for 75 epochs. Evaluation metrics included Dice, Intersection over Union (IoU), mean absolute error (MAE), correlation, and classification performance. Results showed that Mask R-CNN achieved higher disc Dice (0.947 in G1020, 0.938 in REFUGE) and recall (0.880 in REFUGE), while YOLO11 attained stronger vCDR correlation (r = 0.900 in ORIGA) and perfect precision (1.000 in G1020). Overall accuracy exceeded 0.92 in REFUGE and G1020. In conclusion, YOLO11 favored conservative screening with fewer false positives, while Mask R-CNN improved sensitivity. These complementary strengths highlight the importance of model selection by screening context and suggest future research on hybrid frameworks and multimodal integration
Preprocessing Image for License Plate Detection: A Systematic Literature Review Prasetyo, Riyan Bagas Dwi; Abdullayev, Vugar; Prakisya, Nurcahya Pradana Taufik; Sujana, Yudianto; Siswanto, Rahmat
Media of Computer Science Vol. 2 No. 2 (2025): December 2025
Publisher : CV. Digital Innovation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69616/mcs.v2i2.241

Abstract

Rapid population growth contributes to an increase in the volume of vehicles, creating major challenges in their management. One potential solution is the application of deep learning-based artificial intelligence technology for automatic detection of vehicle license plates. This research uses a Systematic Literature Review (SLR) approach to evaluate the performance of various deep learning architectures in the detection process. Out of 125 articles identified, 20 articles were selected based on specific selection criteria. The analysis revealed that preprocessing techniques, such as HE, AHE, ECHE, CLAHE, and ECLACHE, have significant contributions in the processing of vehicle license plate datasets. These techniques were able to improve the visual quality of the images, thus supporting the detection process with an accuracy rate of more than 95%. This research also identified challenges, such as high computational requirements and large-scale data processing. Further research is recommended to apply preprocessing on standardized datasets to develop a reliable, efficient and sustainable detection system.
Co-Authors Abdulhamid, Mohanad Ahmadzada, Allahshukur Ahmed, Abdussalam Ali Alhaq, Elmira Rofida Amrinsani, Farid Andi Farmadi Anita Miftahul Maghfiroh Asgarova, Bahar Babayev, Nicat Djatmiko, Wahyu Dwi Kartini, Dwi Dyah Titisari, Dyah Dzira Naufia Jawza Fatma Indriani Fayyadh, Muhammad Naufaldi Friska Abadi Herianto Herianto Jafarov, Elvin Kholiq, Abd Kurniawan, Rozali Arsyad Lamidi, Lamidi Mahdy, Hafizh Aushaf Muhammad Alkaff Muhammad Amirul Mu'min Muhammad Itqan Mazdadi Muhammad Reza Faisal, Muhammad Reza Musvika, Syevana Dita Muzachim, Zuva Nani Sulistianingsih Nazila, Ragimova Nugraha, Priyambada C. Nur Astrif, Citra Prakisya, Nurcahya Pradana Taufik Prasetyo, Riyan Bagas Dwi Rabani, Rifqi Ragimova, Nazila Rudy Herteno Sabarudin Saputra Saragih, Triando Hamonangan Sa’diah, Halimatus Setiani, Rahma Siswanto, Rahmat Sumber, Sumber Triana Rahmawati Utama, Egan Graha Wahyu Caesarendra Wardhana, Farisy Azis Satria Yana Safitri, Yana Yudianto Sujana