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All Journal International Journal of Electrical and Computer Engineering ELKHA : Jurnal Teknik Elektro Journal of Electronics, Electromedical Engineering, and Medical Informatics Indonesian Journal of Electrical Engineering and Computer Science Sewagati
Setiawan, Rachmad
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Arts Humanities Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Public Health Social Sciences Other

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Designing a Firing Control System on S-60 57mm Cannon As'ad, Kurnia Gunadi; Setiawan, Rachmad; Rameli, Moch
ELKHA : Jurnal Teknik Elektro Vol. 13 No. 2 October 2021
Publisher : Faculty of Engineering, Universitas Tanjungpura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/elkha.v13i2.47343

Abstract

The firing system on the S-60 57mm cannon uses the foot of the cannon crew, which is very dangerous with the position of the crew on top of the cannon when firing. So, a firing system that can be remotely controlled by a computer is required. The design of the S-60 57mm gun firing control system uses a personal computer (PC) as the firing command input, with data communication using WiFi received by the Atmega8535 microcontroller as a voltage regulator for solenoids. The solenoid has a tensile force to drive the hydraulic system where the actuator functions to drive the firing cylinder. Accelero sensor MMA7361, as a variable controller in firing, provides input data simulating the tilt position of the cannon, the position of the 0g sensor is simulated by the cannon in a balanced position. From the test results, there is a difference in sensor designation data with arc angles i.e., angle X by 2.83 degrees and angle Y by 1.86 degrees. The magnetic field produced by the solenoid 0.53 T can attract a maximum load of 20 kg. By changing the distance ratio of mechanical lever to 39.11 cm and 8.89 cm, the solenoid can drive an 88-kg firing cylinder.
Design of Low Vision Electronic Glasses with Image Processing Capabilities Using Raspberry Pi Setiawan, Rachmad; Rayhan Akmal Fadlurahman; Nada Fitrieyatul Hikmah
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 5 No 2 (2023): April
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v5i2.294

Abstract

Poor vision is one of the most common eye health issues worldwide. Low vision patients are typically treated with optical devices or by substituting hearing or touch for visual capabilities. Head-mounted displays are currently the most promising form of low-vision assistive technology since they utilize the user's remaining natural visual capabilities. In this work, a prototype head-mounted display-based low-vision tool in the form of electronic glasses was designed utilizing a Raspberry Pi computer. The prototype was created using a Raspberry Pi 4 B coupled with cameras to allow real-time video acquisition. The LCD on the electronic eyewear frame as the camera showed the video recording. The prototype also included software utilizing five image processing modes—magnification, brightness enhancement, adaptive contrast enhancement, edge enhancement, and text detection and recognition- to help persons with limited vision acquire visual information more effectively. OpenCV was used with Python to create the software system. Average framerate measurements of 30–40 FPS for brightness and contrast improvement modes, 20 FPS for zooming and edge enhancement modes, and 1.3 FPS for text identification modes showed that the concept of electronic spectacles was successfully implemented in this research.
Pemanfaatan Teknologi Aquaponic pada Pondok Pesantren sebagai Upaya Pemberdayaan untuk Kemandirian Pondok Pesantren di Turirejo, Lawang, Kab. Malang, Jawa Timur Zaini, Ahmad; Muhtadin; Pramunanto, Eko; Boedinoegroho, Hanny; Setiawan, Rachmad; Kurniawan, Arief; Yuniarno, Eko Mulyanto
Sewagati Vol 8 No 6 (2024)
Publisher : Pusat Publikasi ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j26139960.v8i6.2229

Abstract

Pondok pesantren merupakan lembaga pendidikan nirlaba yang umumnya berbiaya rendah atau bahkan gratis. Operasionalnya bergantung pada donasi masyarakat dan unit usaha yang dimiliki. Namun, kemampuan setiap pesantren berbeda-beda; hanya pesantren besar dengan sejarah panjang yang mampu mandiri dalam memenuhi kebutuhan operasionalnya. Sebaliknya, banyak pesantren kecil menghadapi tantangan finansial untuk tetap menjaga kualitas layanan, karena donasi yang diterima sering kali tidak mencukupi. Dalam program pengabdian masyarakat ini, dilakukan pelatihan pemanfaatan teknologi aquaponic di pesantren sebagai solusi inovatif. Teknologi ini dipilih karena tidak memerlukan lahan yang luas serta mudah dikelola, sehingga cocok untuk memenuhi kebutuhan konsumsi sehari-hari santri, seperti sayuran dan ikan air tawar. Selain itu, kelebihan hasil produksi dapat dijual untuk menambah pendapatan pesantren. Pelatihan ini memberikan dampak positif, terutama dalam meningkatkan keterampilan santri mengelola aquaponic, yang dapat menjadi bekal wirausaha di masa depan. Lebih jauh, hasil panen dari dua kali produksi memberikan kontribusi nyata bagi operasional pesantren, dengan total pendapatan sebesar Rp11.400.000 dalam bentuk in kind dan in cash. Program ini menunjukkan potensi kemandirian finansial bagi pesantren secara berkelanjutan.
Enhanced embedded system for various synthetic electrocardiogram generation using McSharry’s dynamic equation Hikmah, Nada Fitrieyatul; Setiawan, Rachmad; Andanis, Nafila Cahya; Pranata, Aldo
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i2.pp1620-1631

Abstract

n electrocardiogram (ECG) is a signal that describes the heart’s electrical activity. Signal processing techniques are necessary to extract meaningful information from ECG signals. Researchers often use large databases like the PhysioNet database to evaluate the performance of algorithms. However, these databases have limitations concerning the lack of temporal or morphological variations. This study addresses this limitation by introducing a synthetic ECG capable of producing both normal 12-lead ECG signals and abnormal ECG signals and implementing it into the microcontroller. The primary contribution involves developing a synthetic ECG model using McSharry's dynamic equation model and implementing it using Mikromedia 5 for STM32F4 Capacitive as a microcontroller. This model enables users to set the desired heart rate and accurately replicates ECG waveforms using parameters ????????, ????????, and ????????, each determines the peak’s magnitude, the peak’s time duration, and the angular velocity of the trajectory. The synthetic ECG was evaluated qualitatively and quantitatively, demonstrating waveform similarity to the ECG signals. This study implies that the synthetic ECG model serves as a valuable tool for researchers and practitioners in electrocardiography. It enables the generation of normal and abnormal ECG signals, aiding in algorithm development and potentially enhancing the understanding and diagnosis of heart conditions.
Non-contact breathing rate monitoring using infrared thermography and machine learning Salsabila, Anadya Ghina; Setiawan, Rachmad; Hikmah, Nada Fitrieyatul; Syulthoni, Zain Budi
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 1: July 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i1.pp669-680

Abstract

Monitoring vital physiological parameters such as breathing rate (BR) is crucial for assessing patient health. However, current contact-based measurement methods often cause discomfort, particularly in infants or burn patients. This study aims to develop a non-contact system for monitoring BR using infrared thermography (IRT). This approach permits to detects and tracks the nose from thermal video, extracts temperature variations into a breathing signal, and processes this signal to estimate BR. The estimated BR is then classified into three health categories (bradypnea/normal/tachypnea) using k-nearest neighbors (k-NN). To evaluate system accuracy and robustness, experiments were conducted under three conditions: (i) stationary breathing, (ii) breathing with head movements, and (iii) specific breathing patterns. Results demonstrated high consistency with contact-based photoplethysmography (PPG) measurements, achieving complement of the absolute normalized difference (CAND) index values of 94.57%, 93.71%, and 96.06% across the three conditions and mean absolute BR errors of 1.045 bpm, 1.259 bpm, and 0.607 bpm. The k-NN classifier demonstrated high performance with training, validation, and testing accuracies of 100%, 100%, and 99.2%, respectively. Sensitivity, specificity, precision, and F-measure results confirm system reliability for non-contact BR monitoring in clinical and practical settings.
A Mattress-Integrated ECG System for Home Detection of Obstructive Sleep Apnea Through HRV Analysis Using Wavelet Transform and XGBoost Classification Fitrieyatul Hikmah, Nada; Setiawan, Rachmad; Amalia, Rima; Syulthoni, Zain Budi; Nugroho, Dwi Oktavianto Wahyu; Syakir, Mu’afa Ali
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 7 No 4 (2025): October
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v7i4.1022

Abstract

Obstructive Sleep Apnea (OSA) is a potentially life-threatening sleep disorder that often remains undiagnosed due to the complexity of conventional diagnostic methods such as polysomnography (PSG). Currently, there is a lack of accessible, non-invasive diagnostic solutions suitable for home use. This study proposes a novel approach to automate OSA detection using single-lead electrocardiogram (ECG) signals acquired through non-contact conductive fabric electrodes embedded in a mattress, enabling unobtrusive monitoring during sleep. The main contributions of the proposed study are a mattress-embedded contactless ECG monitoring system eliminating the discomfort of traditional electrodes, and an advanced signal processing framework integrating wavelet decomposition with machine learning for precise OSA identification. ECG signals from 35 subjects (30 male, 5 females, aged 27-63 years) diagnosed with OSA were obtained from the PhysioNet Apnea-ECG database, originally sampled at 100 Hz and up-sampled to 250 Hz for consistency with experimental recordings from healthy volunteers tested in various sleep positions. Signals were recorded non-invasively during sleep in various body positions and processed using the Discrete Wavelet Transform (DWT) up to the third level of decomposition. The processing of ECG signals involved Heart Rate Variability (HRV) analysis, which was applied to extract information in the time domain, frequency domain, and non-linear properties. By analyzing HRV on the respiratory sinus arrhythmia spectrum, the respiration signal was obtained from ECG-derived respiration (EDR). Feature selection was performed using ANOVA, resulting in a set of key features including respiratory rate, SD2, SDNN, LF/HF ratio, and pNN50. These features were classified using the XGBoost algorithm to determine the presence of OSA. The proposed system achieved a detection accuracy of 96.7%, demonstrating its potential for reliable home-based OSA diagnosis. This method improves comfort through non-contact sensing and supports early intervention by delivering timely alerts for high-risk patients
Co-Authors Ahmad Zaini Amalia, Rima Andanis, Nafila Cahya Arief Kurniawan As'ad, Kurnia Gunadi Boedinoegroho, Hanny Eko Mulyanto Yuniarno Fitrieyatul Hikmah, Nada Hikmah, Nada Fitrieyatul Muhtadin Nada Fitrieyatul Hikmah Nugroho, Dwi Oktavianto Wahyu Pramunanto, Eko PRANATA, ALDO Rameli, Moch Rayhan Akmal Fadlurahman Salsabila, Anadya Ghina Syakir, Mu’afa Ali Syulthoni, Zain Budi