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All Journal Rotasi Bulletin of Electrical Engineering and Informatics INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Knowledge Engineering and Data Science Journal of Energy, Mechanical, Material and Manufacturing Engineering 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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Comparison of Time Series Algorithms Using SARIMA and Prophet in Predicting Short-Term Bitcoin Prices Brilliant, Muhammad Zidan; Widiyaningtyas, Triyanna; Caesarendra, Wahyu
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.4.4773

Abstract

Digital finance, particularly Bitcoin, has become a global phenomenon with high volatility, posing great challenges for traders in predicting short-term prices. This study compares the performance of the SARIMA and Prophet algorithms in predicting short-term Bitcoin prices using daily closing price data from October 1, 2014, to October 1, 2024. The study utilizes two different data timeframes, a 10-year dataset (2014-2024) and the last 5 years (2019-2024) for comparative analysis. The SEMMA methodology is used to analyze and compare the two algorithms, which consist of the stages Sample, Explore, Modify, Model, and Assess. The experimental results show that SARIMA provides more stable and consistent results with an MAPE value of 1.24% and RMSE of 896.15 in Scenario 1 and an MAPE value of 1.27% and RMSE of 920.24 in Scenario 2. In contrast, Prophet shows different performance in each scenario. In Scenario 1, Prophet shows optimal results but not so good with an average MAPE of 1.74% and an RMSE value of 1214.86. On the other hand, Prophet showed good performance in Scenario 2 with a lower average MAPE of 0.71% and a smaller RMSE of 489.94, indicating Prophet's ability to handle newer and more dynamic datasets. Both models show their respective advantages; SARIMA is better for long and stable historical data, while Prophet is more effective for shorter and dynamic data. This research provides practical insights for traders and investors in choosing the right prediction model, with results for further study in predicting crypto asset prices.
Optimizing Type 2 Diabetes Classification with Feature Selection and Class Balancing in Machine Learning Wantoro, Agus; Yuliana, Aviv Fitria; Andini, Dwi Yana Ayu; Awaliyani, Ikna; Caesarendra, Wahyu
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.4.5166

Abstract

Type 2 Diabetes (T2DM) is a crucial factor in patient survival and treatment effectiveness. Errors in diabetes detection lead to disease severity, high costs, prolonged healing time, and a decline in service quality. Additionally, a major challenge in developing Machine Learning (ML)-based detection decision support systems is the class imbalance in medical data as well as the high feature dimensionality that can affect the accuracy and efficiency of the model. This research proposes an approach based on feature selection (FS) and handling class imbalance to improve performance in type 2 diabetes. Several feature selection techniques such as Information Gain (IG), Gain Ratio (GR), Gini Decrease (GD), Chi-Square (CS), Relief-F, and FCBF can perform feature selection based on weighting ranking. Furthermore, to address the imbalanced class distribution, we utilize the Synthetic Minority Over-Sampling Technique (SMOTE). ML classification models such as Support Vector Machine (SVM), Gradient Boosting (GB), Tree, Neural Network (NN), Random Forest (RF), and AdaBoost were tested and evaluated based on the confusion matrix including accuracy, precision, recall, and time. The experimental results show that the combination of strategies for handling imbalanced classes significantly improves the predictive performance of ML algorithms. In addition, we found that the combination of feature selection techniques IG+AdaBoost consistently demonstrates optimal performance. This study emphasizes the importance of data preprocessing and the selection of the right algorithms in the development of machine learning-based T2DM detection systems. Accurate detection can reduce the severity of disease, lower treatment costs, speed up the healing process, and improve healthcare services.
Development of a High Flow Oxygen Analyzer for Monitoring Oxygen Therapy in Adults Using High Flow Nasal Cannula (HFNC) Silvian, Fawaida; Dian Setioningsih, Endang Dian Setioningsih1; Triwiyanto , Triwiyanto; Caesarendra, Wahyu
Jurnal Teknokes Vol. 17 No. 2 (2024): June
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

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Abstract

Side effects of using HFNC include gastric insufflation (air entry into the stomach) because HFNC increases positive airway. The next side effect of using HFNC is complications of pneumothorax and pneumomediastinum. This complication occurs in the case of children. In these cases, oxygen administration was reported to exceed the recommended protocol. Although the incidence of air leaks in the use of HFNC for adults has not been reported, similar events may also occur in adults, so close monitoring is needed, especially on oxygen flow. Making the design of the High Flow Oxygen Analyzer can be used for monitoring the flow and oxygen concentration in HFNC. This study uses an Arduino microcontroller to process the oxygen concentration output from the OOA101-1 oxygen concentration sensor, then the processed oxygen concentration will be displayed on the TFT LCD. The variable in this study is the oxygen concentration setting value, while the independent variable is the OOA101-1 oxygen concentration sensor. The concentration value was adjusted using an oxygen blender, while the comparison tool used was gas flow analysis (Citrex H3). In the testing phase, the measurement value is 50% to 100% with a time of 1 minute at each point. Based on the measurements that have been made, the largest error value is obtained at a concentration of 50%, which is 3.07% and the smallest error value is at 100%, which is 0.40%. Dataretrieval using a compressor and central oxygen is very influential on the results of the flow and oxygen concentration. The results obtained are more stable than without the use of a compressor and central oxygen. From these results, the calibrator module has an error (value) which is still within the relative limits of the conclusion, which is ±5%. And also the design of this tool is portable and low cost and made for use in hospital companies as maintenance of HFNC equipment.
Analysis Infinite Impulse Response Filter for Reducing Motion Artifacts in Heart Rate Signals Based on Photoplethysmography Fadillah, Wa Ode Nurul; Ariswati, Her Gumiwang; Caesarendra, Wahyu
Jurnal Teknokes Vol. 17 No. 3 (2024): September
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

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Abstract

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.
Exploring Digital Filters for Cardiac Monitoring: A Focus on Carotid Pulse and Phonocardiogram Signals Ramadhan, Bahrurrizki; Yulianto, Endro; Luthfiyah, Sari; Caesarendra, Wahyu
Jurnal Teknokes Vol. 17 No. 1 (2024): March
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

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Abstract

Heart defect early detection and correct diagnosis have become important healthcare priorities. Tools for monitoring cardiac problems are constantly being developed, with the PCG (Phonocardiogram) and Cardiac Monitor via Carotid Pulse essential for heart evaluation. Although condenser microphones embedded in electric stethoscopes have been used in past research as PCG sensors, more advancements are still required to reduce received noise. This study investigates how well a Chebyshev type-II digital filter works to reduce noise on the cardiac monitor using PCG and carotid pulse. The PCG sensor is the GY-MAX 9814 module, which is interfaced with an Arduino Uno microcontroller. Matlab, Visual Studio (used as a graph viewer), and Doppler Simulator (used as a phantom cardiac signal) are used. SNR (Signal to Noise Ratio) is used in the analysis to assess the effectiveness of two digital filter orders. The average SNR value for the Doppler Simulator is 0.001404 dB at order 2, however, it climbs dramatically to 18.60023 dB at level 4 according to the results of the SNR analysis. The average SNR value in human signals is 11.50718 dB before the filter, 0.001404 dB after the post-order 2 filter, and 12.0009 dB after the post-order 4 filter. According to the results, the digital filter of order 4 is more effective in reducing noise. This study highlights the possibility of an order 4 digital filter to improve the Cardiac Monitor through PCG and Carotid Pulse. Through enhanced signal quality, the creation of this gadget holds the potential for streamlining the identification of cardiac problems. Future developments in this technology could lead to more precise and trustworthy cardiac exams, which would help with early diagnosis and treatment of cardiovascular health.
Comparative Analysis of LSTM and GRU for River Water Level Prediction Faris, Fakhri Al; Taqwa, Ahmad; Handayani, Ade Silvia; Husni, Nyayu Latifah; Caesarendra, Wahyu; Asriyadi, Asriyadi; Novianti, Leni; Rahman, M. Arief
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.5.5054

Abstract

Accurate river water level prediction is essential for flood management, especially in tropical areas like Palembang. This study systematically analyzes the performance of two deep learning models, Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), for real-time water level forecasting using hourly rainfall and water level data collected from automatic sensors. A series of experiments were conducted by varying window sizes (10, 20, 30) and the number of layers (1, 2, 3) for both models, with model performance assessed using RMSE, MAE, MAPE, and NSE. The results demonstrate that both window size and network depth significantly influence prediction accuracy and computational efficiency. The LSTM model achieved its highest accuracy with a window size of 30 and a single layer, while the GRU model performed best with a window size of 20 and two layers. This work contributes by systematically analyzing hyperparameter configurations of LSTM and GRU models on hourly rainfall and water level time series for flood-prone regions, offering empirical insight into parameter tuning in recurrent neural architectures for hydrological forecasting. These findings highlight the importance of careful parameter selection in developing reliable early warning systems for flood risk management.
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
EEG Classification while Listening to Murottal Al-Quran and Classical Music using Random Forest Method Sumarti, Heni; Septiani, Fahira; Sudarmanto, Agus; Caesarendra, Wahyu; Edison, Rizki Edmi
Knowledge Engineering and Data Science
Publisher : citeus

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

Abstract

This study is aimed to classify the brain activity of adolescents associated with audio stimuli; murottal Al-Quran and classical music. The raw data were filtered using Independent Component Analisys (ICA) and followed by band-pass filter in Python on the Google Colab Extraction was processed with Power Spectral Density (PSD) and the Random Forest Method in Weka Machine Learning was used for classification. The research results showed the same results between the two types of stimulation, namely the order of brain waves from highest to lowest were delta, alpha, theta and beta. The average brain waves of teenagers when given murottal al-Quran stimulation were 45.32% delta, 31.60% alpha, 17.02 theta and 6.05% beta. Meanwhile, the average brain waves of teenagers when given classical music stimulation were 46.54% delta, 28.64% alpha, 19.21% theta and 5.50% beta. Classification is obtained with the best value that frequently appears (mode) from the prediction results for each sample using random forest methods. The accuracy, precision, and recall of classifying adolescent brain waves when given murottal and classical music stimuli using the Random Forest method with cross-validation technique (optimum at k-fold=5) were 65.38%, 76.92%, and 70.00%, respectively. The results of this study show that stimulation using murottal al-Quran and classical music effectively improves adolescent relaxation conditions.
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 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 Dwi Kartini Dwi Kartini, Dwi DWI RAMADHANI Dyah Titisari, Dyah Endro Yulianto Eva Yulia Puspaningrum Fadillah, Wa Ode Nurul Faikul Umam Faiza, Linda Ziyadatul Fara Disa Durry Faris, Fakhri Al Fatma Indriani Fitriana, Lutfatul Furizal, Furizal Gołdasz, Iwona Gupta, Munish Kumar Hari Soetanto Herianto Herianto Hidayat, Fathur Rachman Humairah, Sayyidah I Gede Susrama Mas Diyasa 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 Mochammad Ariyanto Mochammad Denny Surindra Muhammad Fuad Muhammad Reza Faisal, Muhammad Reza Muliadi Nurman Setiawan Nyayu Latifah Husni, Nyayu Latifah Pamanasari, Elta Diah Prakoso, Bagas Angger Pranoto, Kirana Astari Putri, Farika Radityo Adi Nugroho Rahman, M. Arief Ramadhan, Bahrurrizki Ramadhan, Yogi Reza REKIK, Chokri Rizki Edmi Edison Rozaq, Hasri Akbar Awal Rudi Irawan Sagita, Muhamad Rian Samudra, Alan Saragih, Triando Hamonangan Septiani, Fahira Setiawan, Joga D Setioningsih, Endang Dian Siena, Laifansan Silvian, Fawaida Sitompul, Carlos R Sri Hastuty, Sri Sumarti, Heni 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