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All Journal Rotasi Bulletin of Electrical Engineering and Informatics Journal of Mechanical Engineering Science and Technology INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Knowledge Engineering and Data Science Journal of Energy, Mechanical, Material and Manufacturing Engineering Jurnal Elektronika dan Telekomunikasi Journal of Electronics, Electromedical Engineering, and Medical Informatics Buletin Ilmiah Sarjana Teknik Elektro Journal of Robotics and Control (JRC) Jurnal Teknik Informatika (JUTIF) International Journal of Robotics and Control Systems Mechanical Engineering for Society and Industry Proceeding International Pelita Bangsa Journal of Environment and Sustainability Education Control Systems and Optimization Letters Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Jurnal Teknokes
Wahyu Caesarendra
Dosen Jurusan Teknik Mesin, Fakultas Teknik, Universitas Diponegoro
Aerospace Engineering Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering Physics Transportation Other

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Analysis of the Effect of Red LED and Infrared Flip Flop Frequency on SpO2 Measurement Accuracy T P, Moch Prastawa Assalim; Titisari, Dyah; Prakoso, Bagas Angger; Caesarendra, Wahyu
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 4 No. 2 (2022): May
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v4i2.145

Abstract

Oxygen saturation is a vital parameter for the early detection of advanced oxygen deficiency. Spo2 is a tool that measures the amount of oxygen in the blood non-invasively. This equipment consists of ophotodiodeiode as a sensor as well as red and infrared LEDs with a flip flop driver circuit that has a certain frequency. In this case, several research projects and equipment on the market have various flip flop frequencies. This research aims to find the best frequency setting value for red and infrared led drivers on SpO2 devices. In this research, a SpO2 that can be adjusted with a flip flop frequency of 400 Hz to 1400 Hz was designed. The SPO2 reading from the sensor is presented on the OLED LCD panel using Arduino Mega as a data processor from the driver frequency output controller. Frequency adjustment for sensor drivers is also at 400 Hz to 1400 Hz. This tool was further used to measure the frequency variation of the flip flop. The measurement results on the subject's finger were then compared with the results of the standard SpO2 tool to see the effect of the frequency value on the level of accuracy of the tool. The results of the comparison data processing showed that the largest error of 0.35% occurred in the SPO2 measurement using the 600 Hz sensor frequency driver, and the smallest error value of 0.07%, occurred in the use of the driver frequency at 1400Hz. These results can be used in the initial design of the production of SpO2 equipment, the higher the frequency, the more accurate it will be. This study only discusses the frequency, whereas the intensity parameters of the red and infrared LEDs also vary.
Comparison of Two Designs of Wireless Electromyography Sensor Module Using Disposable Electrodes and Dry Electrodes in a Sit to Stand Motion Amrinsani, Farid; Wakidi, Levana Forra; Suryanta, Made Dwi Pandya; Wulandari, Dessy Tri; Caesarendra, Wahyu
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.v4i4.155

Abstract

Electromyography is one of the biosignals used to detect muscle signals in humans. Electromyography signals are widely used as input and are engineered to help people with disabilities or assist them in post-stroke therapy recovery. Based on this phenomenon, a lot of electromyography module sensor designs were made to support various purposes in accordance with research. The purpose of this study was to compare the electromyography sensor module using a disposable electrode and a dry electrode using a wireless serial communication system. The results of this study was based on the experiment carried out in the movement from sitting to standing. Therefore, the difference would be more visible by looking at the Mean Power (MNP) value than the mean frequency (MNF). In this case, the tests were conducted using a disposable electrode, all Bluetooth test distances, relaxed conditions with a mean power value of 0.000453, and contraction with a mean power value of 0.000494. In addition, the researchers also compared serial communication transmissions using cables in relaxed conditions with a mean power value of 0.000460 and contraction with a mean power value of 0.000496. Furthermore, trials were further conducted using dry electrodes, all Bluetooth test distances, relaxed conditions with a mean power value of 0.000455, and contraction with a mean power value of 0.000503. In this case, the researchers compared serial communication transmissions using cables in relaxed conditions with a mean power value of 0.000454 and contraction with a mean power value of 0.000499. It was concluded that the design built and analyzed using mean power (MNP), obtained results that were not much different between electromyography modules using wired and wireless serial communications. It was also obtained that the electromyography module design in this study had no problem with the information.
Analyzing the Relationship between Dialysate Flow Rate Stability and Hemodialysis Machine Efficiency Baharsyah, Baharudin Adi; Setioningsih, Endang Dian; Luthfiyah, Sari; Caesarendra, Wahyu
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.v5i2.169

Abstract

Chronic kidney disease (CKD) is a condition characterized by impaired kidney function, leading to disruptions in metabolism, fluid balance, and electrolyte regulation. Hemodialysis serves as a supportive therapy for individuals with CKD, prolonging life but unable to fully restore kidney function. Factors influencing urea and creatinine levels in hemodialysis patients include blood flow velocity, dialysis duration, and dialyzer selection. This research aims to establish a standard for calculating the dialysate flow rate, thereby enhancing dialysis efficiency. The study employs a pre-experimental "one group post-test" design, lacking baseline measurements and randomization, although a control group was utilized. The design's weakness lies in the absence of an initial condition assessment, making conclusive results challenging. Measurement comparisons between the module and the instrument yielded a 5.30% difference, while the difference between the hemodialysis machine and standard equipment was 4.02%. Furthermore, six module measurements against three comparison tools showed a 0.17% difference for the hemodialysis machine with standard equipment, and a 0.18% difference for the module with standard equipment, with a 0.23% discrepancy between the two. Further analysis is necessary to understand the clinical significance and implications of these measurement variations on overall dialysis efficacy
Embedded Machine Learning on ESP32 for Upper-Limb Exoskeletons Based on EMG Triwiyanto, Triwiyanto; Maghfiroh, Anita Miftahul; Forra Wakidi, Levana; Dita Musvika, Syevana; Utomo, Bedjo; Sumber, Sumber; Nugraha, Priyambada Cahya; Caesarendra, Wahyu
Jurnal Teknokes Vol. 18 No. 4 (2025): Desember
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jteknokes.v18i4.134

Abstract

Stroke remains one of the primary causes of long-term disability worldwide and frequently results in persistent impairment of upper limb motor function. To support more effective and intensive rehabilitation, there is a need for wearable devices that can interpret muscle activity and autonomously assist limb movement without relying on an external computer. This study aims to design and implement an upper-limb rehabilitation exoskeleton that is driven by electromyography (EMG) signal classification using machine learning and by real-time elbow angle monitoring, with all models deployed directly on an ESP32 microcontroller. The proposed exoskeleton is built from lightweight, ergonomic 3D-printed components and operates in both unilateral and bilateral modes. Its main contributions include: (1) embedding real-time EMG classification models on the ESP32 so that the device can function independently, (2) integrating EMG-based motor control with elbow angle feedback from an MPU6050 inertial measurement unit, and (3) incorporating a load cell to estimate biceps force during training. EMG signals from the forearm flexor muscles are processed to extract statistical features such as variance (VAR), waveform length (WL), integrated EMG (IEMG), and root mean square (RMS). These features are used to train Random Forest, Decision Tree, Support Vector Machine (SVM), and XGBoost classifiers. The trained models are converted to C code using the micromlgen library for execution on the ESP32. System evaluation involved thirty male participants aged 20–25 years with body weights between 50–85 kg. All tested models achieved 100% accuracy in distinguishing relaxed versus grasping muscle contractions, while the correlation of elbow angles between unilateral and bilateral ESP32 systems reached 0.9469, indicating highly consistent motion detection. The Decision Tree model was selected for deployment due to its superior memory efficiency on the microcontroller. These results demonstrate that the developed ESP32-based exoskeleton provides a practical, efficient, and easily integrable solution for wearable stroke rehabilitation
Feature Selection and Class Imbalance Machine Learning for Early Detection of Thyroid Cancer Recurrence: A Performance-Based Analysis Wantoro, Agus; Caesarendra, Wahyu; Syarif, Admi; Soetanto, Hari
Jurnal Elektronika dan Telekomunikasi Vol 25, No 2 (2025)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/jet.758

Abstract

Early detection of thyroid cancer recurrence is a crucial factor in patient survival and treatment effectiveness. Misdetection results in disease severity, high cost, recovery time, and decreased service quality. In addition, the main challenges in developing a Machine Learning (ML)-based detection decision support system are class imbalance in medical data and high feature dimensions that can affect model accuracy and efficiency. This study proposes a feature selection-based approach and class imbalance handling to improve the performance of early detection of Thyroid cancer. Several feature selection techniques, such as Information Gain (IG), Gain Ratio (GR), Gini Decrease (GD), and Chi-Square (CS), can select features based on weighted ranking. In addition, to overcome the imbalanced class distribution, we use the Synthetic Minority Over-Sampling Technique (SMOTE). ML classification models such as k-NN, Tree, SVM, Naive Bayes, AdaBoost, Neural Network (NN), and Logistic Regression (LR) are tested and evaluated based on a confusion matrix, including accuracy, precision, recall, time, and log loss. Experimental results show that the combination of imbalanced class handling strategies significantly improves the prediction performance of ML algorithms. In addition, we found that the combination of CS+NN feature selection techniques consistently showed optimal performance. This study emphasizes the importance of data pre-processing and proper algorithm selection in the development of a machine learning-based thyroid cancer detection system.
Co-Authors Abdullayev, Vugar Achmad Widodo Ade Silvia Handayani Admi Syarif Agus Sudarmanto Agus Wantoro Ahmad Rofii Ahmad Taqwa Ahmed, Abdussalam Ali Alfian Ma’arif Amrinsani, Farid Anant Athavale, Vijay Andini, Dwi Yana Ayu Anita Miftahul Maghfiroh Ariesma Githa Giovany Ariswati, Her Gumiwang Aryananda, Rangga Laksana Asriyadi Asriyadi Aviv Fitria Yulia Baharsyah, Baharudin Adi Brilliant, Muhammad Zidan Busono Soerowirdjo Dewi, Deshinta Arrova Dian Setioningsih, Endang Dian Setioningsih1 Dita Musvika, Syevana Dwi Kartini Dwi Kartini, Dwi DWI RAMADHANI Dyah Titisari, Dyah Edison, Rizki Edmi Endro Yulianto Eva Yulia Puspaningrum Fadillah, Wa Ode Nurul Faikul Umam Faiza, Linda Ziyadatul Fara Disa Durry Faris, Fakhri Al Fatma Indriani Fitriana, Lutfatul Forra Wakidi, Levana Furizal, Furizal Gołdasz, Iwona Gupta, Munish Kumar Hari Soetanto Herianto Herianto Hidayat, Fathur Rachman Humairah, Sayyidah Ichwan Dwi Nugraha Ikna Awaliyani Irwan Budiman Irwan Budiman Joga Dharma Setiawan Krolczyk, Grzegorz Kusnanto Mukti Wibowo Leni Novianti Luthfiyah, Sari Maharani, Siti Mutia Mahmood, Muhammad Azim Mahmud Mahmud MAJDOUBI, Rania Mas Diyasa, I Gede Susrama Mochammad Ariyanto Mochammad Denny Surindra Muhammad Abdillah Muhammad Fuad Muhammad Reza Faisal, Muhammad Reza Muliadi Nugraha, Priyambada Cahya Nyayu Latifah Husni, Nyayu Latifah Pamanasari, Elta Diah Prakoso, Bagas Angger Pranoto, Kirana Astari Putri, Farika Radityo Adi Nugroho Rahardja, Dimas Revindra Rahman, M. Arief Ramadhan, Bahrurrizki Ramadhan, Yogi Reza REKIK, Chokri Rozaq, Hasri Akbar Awal Rudi Irawan Sagita, Muhamad Rian Samudra, Alan Saragih, Triando Hamonangan Seno Darmanto Septiani, Fahira Setiawan, Joga D Setiawan, Nurman Setioningsih, Endang Dian Siena, Laifansan Silvian, Fawaida Sitompul, Carlos R Sri Hastuty, Sri Sri Utami Handayani Sumarti, Heni Sumber, Sumber Suryanta, Made Dwi Pandya Suwarno, Iswanto T P, Moch Prastawa Assalim Triwiyanto , Triwiyanto Triyanna Widiyaningtyas Utomo, Bedjo V.H, Abdullayev Wahyu Dwi Lestari Wakidi, Levana Forra Wulandari, Dessy Tri YILDIZ, Oktay Zy, Ahmad Turmudi