Jurnal Teknokes
Aims JURNAL TEKNOKES aims to become a forum for publicizing ideas and thoughts on health science and engineering in the form of research and review articles from academics, analysts, practitioners, and those interested in providing literature on biomedical engineering in all aspects. Scope: 1. Medical Electronics Technology and Biomedical Engineering: Biomedical Signal Processing and Control, Artificial intelligence in biomedical imaging, Machine learning, and Pattern Recognition in a biomedical signal, Medical Diagnostic Instrumentation, Laboratorium Instrumentation, Medical Calibrator Design, Intelligent Systems, Neural Networks, Machine Learning, Fuzzy Systems, Digital Signal Processing, Image Processing, prosthetics, orthotics, rehabilitation sciences, Mobility Assistive Technology (MAT), Internet of Things (IoT), and Artificial Intelligence (AI) in the prosthetics and orthotics field, Breast Imaging, Cardiovascular Imaging, Chest Radiology, Computed Tomography, Diagnostic Imaging, Gastrointestinal Imaging, Genitourinary, Radiology, Head & Neck, Imaging Sciences, Magnetic Resonance Imaging, Musculoskeletal Radiology, Neuroimaging and Head & Neck, Neuro-Radiology, Nuclear Medicine, Pediatric Imaging, Positron Emission Tomography, Radiation Oncology, Ultrasound, X-ray Radiography, etc. 2. Medical Laboratory Technology: Hematology and clinical chemistry departments, microbiology section of the laboratory, parasitology, bacteriology, virology, hematology, clinical chemistry, toxicology, food and beverage chemistry. 3. Environmental Health Science, Engineering and Technology: Papers focus on design, development of engineering methods, management, governmental policies, and societal impacts of wastewater collection and treatment; the fate and transport of contaminants on watersheds, in surface waters, in groundwater, in soil, and in the atmosphere; environmental biology, microbiology, chemistry, fluid mechanics, and physical processes that control natural concentrations and dispersion of wastes in air, water, and soil; nonpoint-source pollution on watersheds, in streams, in groundwater, in lakes, and in estuaries and coastal areas; treatment, management, and control of hazardous wastes; control and monitoring of air pollution and acid deposition; airshed management; and design and management of solid waste facilities, detection of micropollutants, nanoparticles and microplastic, antimicrobial resistance, greenhouse gas mitigation technologies, novel disinfection methods, zero or minimal liquid discharge technologies, biofuel production, advanced water analytics 4. Health Information System and Technology The journal presents and discusses hot subjects including but not limited to patient safety, patient empowerment, disease surveillance and management, e-health and issues concerning data security, privacy, reliability and management, data mining and knowledge exchange as well as health prevention. The journal also addresses the medical, financial, social, educational, and safety aspects of health technologies as well as health technology assessment and management, including issues such as security, efficacy, the cost in comparison to the benefit, as well as social, legal, and ethical implications. This journal also discussed Intelligent Biomedical Informatics, Computer-aided medical decision support systems using a heuristic, Educational computer-based programs pertaining to medical informatics.
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<![CDATA[Analysis of the Accuracy of Differential Pressure Sensor in a Portable Spirometry with FVC, FEV1 and PEF Parameters ]]>
<![CDATA[Nopriyandi, Nopriyandi]]>;
<![CDATA[Hari Wisana, I Dewa Gede]]>;
<![CDATA[Setioningsih, Endang Dian]]>;
<![CDATA[Rizal, Achmad]]>
<![CDATA[ Jurnal Teknokes Vol. 17 No. 3 (2024): September ]]>
Publisher : <![CDATA[Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia ]]>
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<![CDATA[Accurate measurement of lung function is essential for diagnosing and monitoring respiratory diseases such as chronic obstructive pulmonary disease (COPD), asthma, and cystic fibrosis. Traditional spirometry methods often face challenges related to accuracy and sensitivity, which can lead to misdiagnosis and inappropriate treatment. This study aims to evaluate the performance of the DF-Robot differential pressure sensor as a portable spirometry tool, focusing on key parameters including Forced Vital Capacity (FVC), Forced Expiratory Volume in 1 second (FEV1), and Peak Expiratory Flow (PEF). The research was conducted at the Surabaya Electromedical Engineering Department, utilizing a pre-experimental design with a single group. The DF-Robot sensor's output was compared against a Hans Rudolph 5530 Syringe Calibrator to determine its accuracy. Data collection involved three different tube sizes, with ten repetitions for each size using the calibrator, and five repetitions with human subjects to assess real-world applicability. Results indicated that the DF-Robot sensor demonstrated high accuracy, with the smallest tube size yielding a minimal error of 0.9%. In contrast, larger tube sizes resulted in significantly higher error rates, with the largest tube showing an error of 33%. The study concluded that the DF-Robot differential pressure sensor is a promising alternative for portable spirometry applications, providing reliable measurements of lung function parameters. The findings underscore the importance of sensor selection in spirometry, as theaccuracy of measurements directly impacts patient diagnosis and treatment. This research contributes valuable insights intothe development of portable spirometry devices, potentially enhancing the diagnostic capabilities for respiratory diseases andimproving patient outcomes in clinical practice. Future studies should explore further refinements in sensor technology andmethodologies to optimize spirometry accuracy and reliability.]]>
<![CDATA[Exploration of Biomechanical and Biooptical Sensors on Cardiac Monitor on Carotid Pulse ]]>
<![CDATA[Putra, Wahyu Ramadhan]]>;
<![CDATA[Yulianto, Endro]]>;
<![CDATA[Faizal, Ajesh]]>
<![CDATA[ Jurnal Teknokes Vol. 17 No. 3 (2024): September ]]>
Publisher : <![CDATA[Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia ]]>
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<![CDATA[Cardiovascular diseases are a significant global health concern, often requiring timely and accurate monitoring for effective management. Traditional heart monitoring devices face limitations, such as inadequate real-time data and suboptimal accuracy. This study aims to enhance the detection of carotid pulse signals by comparing the performance of two sensor types: piezoelectric sensors and SEN0203 sensors. The methodology involved designing a cardiac monitoring device that integrates both sensors to simultaneously capture carotid pulse and phonocardiograph (PCG) signals. Data collection was conducted on 10 respondents, where both sensors were applied alternately to the carotid artery, and the signals were analyzed using an oscilloscope. The results demonstrated that the piezoelectric sensor outperformed the SEN0203 sensor in terms of signal clarity and amplitude. Specifically, the average amplitude of the carotid pulse recorded by the piezoelectric sensor was 5.3 mV, while the SEN0203 sensor recorded an average amplitudeof only 3.2 mV. Additionally, the correlation analysis revealed a strong relationship between the carotid pulse and PCGsignals, with a correlation coefficient of 0.87, indicating a high degree of reliability in the measurements obtained fromthe piezoelectric sensor. In conclusion, the findings of this study suggest that piezoelectric sensors are more effectivefor monitoring carotid pulse signals compared to SEN0203 sensors, providing clearer and more reliable data. Thisadvancement in sensor technology has the potential to improve early detection of cardiovascular abnormalities, leadingto better patient outcomes. Future research should focus on the development of portable monitoring devices thatincorporate these sensors, facilitating widespread clinical application and enhancing the overall quality of cardiovascularcare.]]>
<![CDATA[Growth Monitoring System Using Infant Length to Determine Nutritional Status in Children Aged 0-12 Months. ]]>
<![CDATA[Yulia Ningrum, Churie Nurhaeni]]>;
<![CDATA[Yulianto, Endro]]>;
<![CDATA[Rahmawati, Triana]]>
<![CDATA[ Jurnal Teknokes Vol. 17 No. 3 (2024): September ]]>
Publisher : <![CDATA[Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia ]]>
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<![CDATA[This research addresses the pressing issue of monitoring the growth and nutritional status of infants aged 0-12months, a critical period for health and development. Inadequate growth monitoring can lead to undetected nutritionaldeficiencies and long-term health consequences. To tackle this problem, the study developed an innovative Growth MonitoringSystem that utilizes length, weight, and head circumference as key indicators of nutritional status. The system integratesadvanced technology, including an ESP-32 microcontroller, load cell sensors for weight measurement, ultrasonic sensors forheight measurement, and infrared sensors for head circumference measurement. The methodology involved collecting datafrom 30 respondents, where the system automatically recorded measurements and generated growth curves displayed on aweb-based platform. The accuracy of the measurements was evaluated, revealing significant variability in error rates.Specifically, the highest error in head circumference measurement was recorded at 25.38%, while the weight measurementexhibited a lower error rate of -20.47%. These results highlight the challenges in achieving precise measurements but alsodemonstrate the system's capability to provide essential data for assessing infant growth. In conclusion, the developed GrowthMonitoring System represents a significant advancement in child health monitoring, offering a reliable and efficient methodfor tracking the growth of infants. Despite the observed measurement errors, the system's automated data collection andanalysis capabilities provide valuable insights into nutritional status. The research emphasizes the potential for broaderimplementation of such systems in pediatric clinics and national health programs, ultimately contributing to improved healthoutcomes for infants. By enhancing the accuracy and accessibility of growth monitoring, this research paves the way for moreeffective interventions in early childhood nutrition and health.]]>
<![CDATA[IoT-Based Insulin Pump Design Analysis Using Flowrate Monitoring ]]>
<![CDATA[Isfahani, Ghina]]>;
<![CDATA[Syaifudin, Syaifudin]]>;
<![CDATA[Utomo, Bedjo]]>;
<![CDATA[Ragimova, Nazila]]>
<![CDATA[ Jurnal Teknokes Vol. 17 No. 3 (2024): September ]]>
Publisher : <![CDATA[Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia ]]>
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<![CDATA[The management of diabetes, particularly for individuals requiring insulin therapy, presents significant challengesin ensuring accurate and timely insulin delivery. Traditional insulin pumps often lack the precision and adaptability needed foreffective glucose control, leading to potential complications. This study addresses these issues by developing an IoT-basedinsulin pump that utilizes flowrate monitoring to enhance the accuracy of insulin administration. The research employed theESP8266 microcontroller for data processing and control, coupled with the SLF3S-0600F liquid flow sensor to monitor insulinflow rates. The Blynk application was utilized for remote monitoring and dose adjustments, allowing users to manage theirinsulin delivery conveniently via an Android device. The experimental methodology involved conducting five repeatedmeasurements to assess flow rate accuracy, volume delivery, and motor speed. Results indicated that the insulin pump achieveda flow rate measurement error of only 0.0051% at a setting of 1.5 ml/min, while the largest error recorded was 0.0391% at 3ml/min. Additionally, the volume measurement error was minimal, with the smallest error at a 2 ml setting of 0.016% and thelargest at 1 ml with an error of 0.152%. The average motor speed was recorded at 21.22 rpm for auto settings and 49.88 rpm forbolus settings. In conclusion, the developed IoT-based insulin pump demonstrates significant potential for improving diabetesmanagement through precise insulin delivery and real-time monitoring capabilities. The integration of IoT technology not onlyenhances the accuracy of insulin administration but also provides users with greater flexibility and control over their treatment. This research contributes to the ongoing efforts to innovate diabetes care solutions, ultimately aiming to reduce the risk of long-term complications associated with the disease.]]>
<![CDATA[Analysis Infinite Impulse Response Filter for Reducing Motion Artifacts in Heart Rate Signals Based on Photoplethysmography ]]>
<![CDATA[Fadillah, Wa Ode Nurul]]>;
<![CDATA[Ariswati, Her Gumiwang]]>;
<![CDATA[Caesarendra, Wahyu]]>
<![CDATA[ Jurnal Teknokes Vol. 17 No. 3 (2024): September ]]>
Publisher : <![CDATA[Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia ]]>
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<![CDATA[The increasing prevalence of motion artifacts (MA) in photoplethysmography (PPG) signals poses significantchallenges for accurate heart rate monitoring, particularly in dynamic environments. This study addresses the problem of MAinterference in PPG signals, which can lead to erroneous heart rate readings and compromised patient monitoring. To mitigatethis issue, we employed an Infinite Impulse Response (IIR) filter to enhance the quality of PPG signals by effectively reducingthe impact of motion artifacts. The methodology involved collecting PPG signals from a sample of participants during variousphysical activities. The raw signals were subjected to both filtering and non-filtering processes using MATLAB, allowing fora comparative analysis of the signal quality. The filtering process was designed to suppress unwanted frequencies associatedwith motion while preserving the physiological signals of interest. The performance of the IIR filter was evaluated based onthe Signal-to-Noise Ratio (SNR) and the accuracy of heart rate extraction. Results indicated a significant improvement insignal quality post-filtering, with the SNR increasing from an average of 5.2 dB to 15.8 dB, demonstrating a substantialenhancement in the clarity of the PPG signals. Furthermore, the heart rate extraction accuracy improved from 78% to 95%after applying the IIR filter, showcasing the effectiveness of the proposed method in real-time applications. In conclusion, theapplication of the IIR filter in processing PPG signals effectively reduces motion artifacts, leading to more accurate heart ratemonitoring. This research highlights the potential for improved patient outcomes in clinical settings and suggests furtherexploration of advanced filtering techniques to enhance the reliability of wearable health monitoring devices. The findingsunderscore the importance of addressing motion artifacts in the development of robust biomedical sensing technologies.]]>
