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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 Scientific Journal of Engineering Research Jurnal Teknokes
Abdullayev, Vugar
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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 Materials Science & Nanotechnology Mechanical Engineering

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Development of Adaptive PD Control for Infant Incubator Using Fuzzy Logic Kholiq, Abd; Lamidi, Lamidi; Amrinsani, Farid; Triwiyanto, Triwiyanto; Mahdy, Hafizh Aushaf; Nazila, Ragimova; Abdullayev, Vugar
Journal of Robotics and Control (JRC) Vol 5, No 3 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i3.21510

Abstract

This research aims to design an innovative fuzzy logic auto-tuning PD algorithm to control the temperature in a baby Incubator. The proposed Fuzzy-PD method combines fuzzy logic with PD control using the Arduino Mega 2560 microcontroller. The Proportional and Derivative parameters are adjusted by fuzzy logic based on feedback of error values and rate of change of error. The temperature setting range used in data collection is 32-37°C. When the temperature setting is higher, the time required to reach the specified temperature setting becomes longer. The overshoot tends to be low, as the system is designed to respond to temperature changes with high precision. The temperature inside the baby Incubator can be maintained with a low steady-state error value. The adaptive fuzzy-PD system can restore the temperature inside the baby Incubator to the set temperature after a disturbance. Compared to the x device, the average error value is 0.0013%. Independent sample t-tests show no significant difference between the baby Incubator and the Incu analyzer device. It can be concluded that the combination of fuzzy logic and PD control system works well in maintaining temperature stability with low error values. The results are better than previous research focusing on designing a PD algorithm with a maximum rise time of 480 seconds. Furthermore, there is potential for further development with a fuzzy logic auto-tuning PID algorithm to achieve better results.
Single Lead EMG signal to Control an Upper Limb Exoskeleton Using Embedded Machine Learning on Raspberry Pi Triwiyanto, Triwiyanto; Caesarendra, Wahyu; Abdullayev, Vugar; Ahmed, Abdussalam Ali; Herianto, Herianto
Journal of Robotics and Control (JRC) Vol 4, No 1 (2023)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v4i1.17364

Abstract

Post-stroke can cause partial or complete paralysis of the human limb. Delayed rehabilitation steps in post-stroke patients can cause muscle atrophy and limb stiffness. Post-stroke patients require an upper limb exoskeleton device for the rehabilitation process. Several previous studies used more than one electrode lead to control the exoskeleton. The use of many electrode leads can lead to an increase in complexity in terms of hardware and software. Therefore, this research aims to develop single lead EMG pattern recognition to control an upper limb exoskeleton. The main contribution of this research is that the robotic upper limb exoskeleton device can be controlled using a single lead EMG. EMG signals were tapped at the biceps point with a sampling frequency of 2000 Hz. A Raspberry Pi 3B+ was used to embed the data acquisition, feature extraction, classification and motor control by using multithread algorithm. The exoskeleton arm frame is made using 3D printing technology using a high torque servo motor drive. The control process is carried out by extracting EMG signals using EMG features (mean absolute value, root mean square, variance) further extraction results will be trained on machine learning (decision tree (DT), linear regression (LR), polynomial regression (PR), and random forest (RF)). The results show that machine learning decision tree and random forest produce the highest accuracy compared to other classifiers. The accuracy of DT and RF are of 96.36±0.54% and 95.67±0.76%, respectively. Combining the EMG features, shows that there is no significant difference in accuracy (p-value 0.05). A single lead EMG electrode can control the upper limb exoskeleton robot device well.
Gender Classification on Social Media Messages Using fastText Feature Extraction and Long Short-Term Memory Sa’diah, Halimatus; Faisal, Mohammad Reza; Farmadi, Andi; Abadi, Friska; Indriani, Fatma; Alkaff, Muhammad; Abdullayev, Vugar
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 6 No 3 (2024): July
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v6i3.407

Abstract

Currently, social media is used as a platform for interacting with many people and has also become a source of information for social media researchers or analysts. Twitter is one of the platforms commonly used for research purposes, especially for data from tweets written by individuals. However, on Twitter, user information such as gender is not explicitly displayed in the account profile, yet there is a plethora of unstructured information containing such data, often unnoticed. This research aims to classify gender based on tweet data and account description data and determine the accuracy of gender classification using machine learning methods. The method used involves FastText as a feature extraction method and LSTM as a classification method based on the extracted data, while to achieve the most accurate results, classification is performed on tweet data, account description data, and a combination of both. This research shows that LSTM classification on account description data and combined data obtained an accuracy of 70%, while tweet data classification achieved 69%. This research concludes that FastText feature extraction with LSTM classification can be implemented for gender classification. However, there is no significant difference in accuracy results for each dataset. However, this research demonstrates that both methods can work well together and yield optimal results.
Development process of decision support systems using data mining technology Asgarova, Bahar; Jafarov, Elvin; Babayev, Nicat; Ahmadzada, Allahshukur; Abdullayev, Vugar; Triwiyanto, Triwiyanto
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 1: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i1.pp703-714

Abstract

Decision support systems (DSS) play a pivotal role as computerized tools, guiding and enhancing decision-making processes vital for organizational progress. This research focuses on developing a system tailored for dynamic decision-making, particularly emphasizing the integration of data mining technology. Decision algorithms and neural networks are discussed in depth, providing a comprehensive understanding of the analytical tools crucial for effective decision support. Additionally, the research sheds light on potential risks, ensuring a nuanced view of challenges that may impact the development of DSS. A significant portion of the study is dedicated to the design of DSS architecture and the strategic integration of data mining within the database. The proposed development stages for a business information system, ranging from feasibility study to release, serve as a structured framework for practical implementation. Details within each stage, including data analysis, cleaning, and module development, are meticulously examined. Emphasis is placed on critical steps such as system design, database design, and extract, transform, load (ETL) process design, elucidating their importance in the holistic development of DSS. The conclusion reinforces the paramount importance of leveraging data mining technology in the process of developing decision support systems.
Exploration of digital filters on cardiac monitor devices equipped with non-invasive blood pressure (NIBP) Nugraha, Priyambada C.; Sumber, Sumber; Muzachim, Zuva; Rabani, Rifqi; Alhaq, Elmira Rofida; Triwiyanto, Triwiyanto; Abdullayev, Vugar
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 6 No. 4 (2024): November
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/edd73780

Abstract

Heart disease is a leading cause of global mortality, making accurate monitoring essential for early detection and prevention of complications. Although heart monitoring technology has advanced, there are still limitations in precisely detecting early symptoms. This study aims to develop a Cardiac Monitor device capable of monitoring patients with heart conditions through three main parameters: electrocardiogram (ECG), phonocardiogram (PCG), and non-invasive blood pressure measurement (NIBP). The system designed in this research integrates digital filters, namely Butterworth (order 2, 4, 8) and Kalman, to enhance the quality of ECG and PCG signals. Additionally, the oscillometric method in non-invasive blood pressure measurement (NIBP) is used as a comparison for blood pressure estimation by analyzing the correlation between the R peak on the ECG signal, pulse transit time (PTT), and the first and second heart sounds (S1, S2) on the PCG signal. Blood pressure estimation is performed using algorithmic calculations to determine the accuracy of the design module in measuring systolic and diastolic pressure. The results indicate that the 8th-order Butterworth filter is more effective in reducing noise in ECG and PCG signals compared to the Kalman filter. The study also finds a significant correlation between the R peak on the ECG and the first heart sound on the PCG. The design module’s blood pressure measurement errors compared to algorithmic estimates are 4.54 ± 4.94 mmHg for systolic pressure and 6.57 ± 3.83 mmHg for diastolic pressure, which are close to the AAMI standard of 5 ± 8 mmHg. These findings highlight the great potential of the developed Cardiac Monitor device in improving early diagnosis accuracy and heart disease management.
The Enhancing Diabetes Prediction Accuracy Using Random Forest and XGBoost with PSO and GA-Based Feature Selection Dzira Naufia Jawza; Mazdadi, Muhammad Itqan; Farmadi, Andi; Saragih, Triando Hamonangan; Kartini, Dwi; Abdullayev, Vugar
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 7 No 2 (2025): April
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v7i2.626

Abstract

Diabetes represents a global health concern classified as a non-communicable disease, impacting more than 422 million people worldwide, with the number expected to increase each year. This study aims to evaluate the performance of the Random Forest and Extreme Gradient Boosting (XGBoost) classification algorithms on the diabetes disease dataset taken from Kaggle. To improve prediction accuracy, feature selection was carried out using Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) which are expected to filter the most relevant features. The study results showed that the Random Forest model without feature selection yielded an Area Under Curve (AUC) value of 0.8120, while XGBoost achieved an AUC of 0.7666. After applying feature selection with PSO, the AUC increased to 0.8582 for Random Forest and 0.8250 for XGBoost. The use of feature selection with GA gave better results, with an AUC of 0.8612 for Random Forest and 0.8351 for XGBoost. These results indicate that the increase in accuracy after feature selection using PSO ranges from 5.7% to 7.6%, while the increase with GA ranges from 6.1% to 8.9%, with GA providing more significant results. This study contributes to improving the accuracy of diabetes disease classification, which is expected to support the diagnosis process more quickly and accurately.
Post-Quantum Cryptography Review in Future Cybersecurity Strengthening Efforts Mu'min, Muhammad Amirul; Safitri, Yana; Saputra, Sabarudin; Sulistianingsih, Nani; Ragimova, Nazila; Abdullayev, Vugar
Scientific Journal of Engineering Research Vol. 1 No. 3 (2025): July
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/sjer.v1i3.2025.35

Abstract

The development of quantum computing technology brings significant challenges to conventional crypto-graphic systems that are currently widely used in digital data security. Attacks made possible by quan-tum computers have the potential to weaken classical algorithms such as RSA and ECC, so a new ap-proach is needed that can guarantee long-term security. This study aims to systematically review the ef-fectiveness and readiness of the implementation of post-quantum cryptography (PQC) algorithms, espe-cially those that have been recommended by NIST, in order to strengthen the resilience of future cyberse-curity systems. The method used was a structured literature study with comparative analysis of lattice-based (Kyber and Dilithium), code-based (BIKE), and hash-based (SPHINCS+) PQC algorithms. Data are obtained from official documents of standards institutions as well as the latest scientific publications. The results of the analysis show that lattice-based algorithms offer an optimal combination of security and efficiency, and demonstrate high readiness to be implemented on limited devices. Compared to other al-gorithms, Kyber and Dilithium have advantages in terms of performance and scalability. Thus, this re-search contributes in the form of mapping the practical readiness of the PQC algorithm that has not been widely studied in previous studies, and can be the basis for the formulation of future cryptographic adop-tion policies. These findings are expected to help the transition process towards cryptographic systems that are resilient to quantum threats.
Development of a Low-Cost and Portable Device for Monitoring Heart Rate, Blood Oxygen Saturation, and Body Temperature in Infants Incubator Nur Astrif, Citra; Triwiyanto, Triwiyanto; Abdullayev, Vugar
Jurnal Teknokes Vol. 17 No. 2 (2024): June
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

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

Abstract

The risk of newborn infant mortality is commonly associated with hypothermia. Hypothermia is a health disorder and a leading cause of death in newborns. caused by an imbalance in the baby's body temperature. Hypothermia is caused by a decrease in body temperature, which can result from various conditions, including high oxygen requirements and a decrease in room temperature, among other factors. Hypothermia occurs due to a decrease in body temperature resulting from various conditions. especially high oxygen requirements and a decrease in room temperature. The purpose of this study is to monitor the health status of newborns. Monitoring the body temperature and oxygen saturation levels in newborns can help detect abnormalities in infants at an early stage. This research is expected to assist patients using a baby cube in providing care for newborns with hypothermia symptoms. The Baby Cube utilizes the DS18B20 sensor for temperature measurement and the MAX3102 sensor for heart rate and oxygen saturation. The data is then processed using the ESP32 microcontroller. and the results are displayed on an LCD screen. The comparative tools used in this study are the standard thermometer and pulse oximeter. The results of this research indicate that the smallest measurement error value is found in the SpO2 measurement of data collection 10. which is 0.1%. The largest measurement error value is found in the SpO2 measurement of data collection 2. which is 5.6% based on the obtained data. However. the measurement results are still within the tolerance limit of ±10%.
Implementation of Gyro Accelerometer Sensor for Measuring Respiration Based on Inhale and Exhale with Delphi Interface Utama, Egan Graha; Triwiyanto, Triwiyanto; Rahmawati, Triana; Abdulhamid, Mohanad; Abdullayev, Vugar
Jurnal Teknokes Vol. 16 No. 2 (2023): June
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

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

Abstract

Accelerometer sensor is widely employed in respiration studies for its ability to detect changes in position and speed. However, there is a lack of research focusing on the optimal placement of this sensor to achieve accurate respiration measurements. This study aims to investigate and analyze the ideal positioning of the gyro accelerometer sensor for precise respiration detection. To achieve this, a design is proposed that utilizes an Arduino Nano as a microcontroller to process signals and derive respiration values from three gyro accelerometer sensors. The obtained respiration signals and values are transmitted to a PC via Bluetooth and visualized through a Delphi application, enabling a comprehensive comparison of the signals from the three sensors. The main contribution of this research lies in studying the impact of gyro accelerometer sensor placement on respiration detection, ultimately identifying the most suitable sensor location. The analysis reveals that the overall error values obtained from the module are promising, with the highest error recorded at 2.06% when the sensor is positioned at the stomach and chest (sensor position 3). This result validates the feasibility of using gyro accelerometer sensors for respiration detection and provides valuable insights for future studies in this domain. However, it is important to acknowledge certain limitations in this research. During respondent movement or walking, noise is observed in the signal, which may affect the accuracy of respiration measurements. These limitations highlight the need for further investigation into refining the sensor placement and signal processing techniques to mitigate noise and enhance overall accuracy. In conclusion, this study emphasizes the significance of gyro accelerometer sensors in respiration detection and addresses the dearth of research regarding their optimal placement. By presenting the error analysis of three sensor positions, the study establishes a foundation for more precise and reliable respiration measurement techniques. Future efforts should concentrate on overcoming the limitations identified in this research, thereby advancing the potential of gyro accelerometer sensors for a wide range of respiration applications, such as monitoring respiratory health and sleep patterns
Expert System for Early Detection of Thalassemia Disease Using Case-Based Reasoning Method Setiani, Rahma; Djatmiko, Wahyu; Kurniawan, Rozali Arsyad; Abdullayev, Vugar
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 7 No. 1 (2025): February
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/gt9h2k22

Abstract

Thalassemia is a blood disorder characterized by abnormalities in globin chain formation. In Banyumas Regency, the prevalence of thalassemia continues to increase yearly, while detection processes are often delayed due to limited access to experts. This study aims to develop a web-based expert system for the early detection of thalassemia using the Case-Based Reasoning (CBR) method with the K-Nearest Neighbor (KNN) algorithm. The system is designed to help identify individuals who may carry the thalassemia gene trait, enabling faster and more accurate treatment. The system was tested using the black box method to ensure all features function properly across all user roles, including general users, administrators, and experts. Accuracy evaluation was conducted using a confusion matrix, achieving an accuracy rate of 95,23% based on 21 test data samples. The results indicate that this system provides highly accurate early detection and supports preventive efforts against thalassemia. Further development is recommended to create an Android-based application to enhance accessibility for the broader community. Additionally, continuous updates to the knowledge base are necessary to improve the system's accuracy and scope. This study is expected to contribute to the prevention and management of thalassemia, increase public awareness, and support better healthcare services in Indonesia
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 Herteno, Rudy 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 Rabani, Rifqi Ragimova, Nazila Sabarudin Saputra Saragih, Triando Hamonangan Sa’diah, Halimatus Setiani, Rahma Sumber, Sumber Triana Rahmawati Utama, Egan Graha Wahyu Caesarendra Wardhana, Farisy Azis Satria Yana Safitri, Yana