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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 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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Implementation of C5.0 Algorithm using Chi-Square Feature Selection for Early Detection of Hepatitis C Disease MAHMUD, Mahmud; BUDİMAN, Irwan; INDRİANİ, Fatma; KARTİNİ, Dwi; FAİSAL, Mohammad Reza; ROZAQ, Hasri Akbar Awal; YILDIZ, Oktay; Caesarendra, Wahyu
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 6 No 2 (2024): April
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v6i2.384

Abstract

Hepatitis C, a significant global health challenge, affects 71 million people worldwide, with severe complications such as cirrhosis and hepatocellular carcinoma. Despite its prevalence and availability in rapid diagnostic tests (RDTs), the need for accurate early detection methods remains critical. This research aims to enhance hepatitis C virus classification accuracy by integrating the C5.0 algorithm with Chi-Square feature selection, addressing the limitations of current diagnostic approaches and potentially reducing diagnostic errors. This research explores the development of a machine learning model for hepatitis C prediction, utilizing a publicly available dataset from Kaggle. It encompasses preprocessing techniques such as label encoding, handling missing values, normalization, feature selection, model development, and evaluation to ensure the model's efficacy and accuracy in diagnosing hepatitis C. The findings of this study reveal that implementing Chi-Square feature selection significantly enhances the effectiveness of machine learning algorithms. Specifically, the combination of the C5.0 algorithm and Chi-Square feature selection yielded a remarkable accuracy of 96.75%, surpassing previous research benchmarks. This highlights the potent synergy between advanced feature selection techniques and machine learning algorithms in improving diagnostic precision. The study conclusively demonstrates that machine learning is an effective tool for detecting hepatitis C, showcasing the potential to enhance diagnostic accuracy significantly. As a future recommendation, adopting AutoML is suggested to periodically automate the selection of the optimal algorithm, promising further improvements in detection capabilities.
Utilize the Prediction Results from the Neural Network Gate Recurrent Unit (GRU) Model to Optimize Reactive Power Usage in High-Rise Buildings Rofii, Ahmad; Soerowirdjo, Busono; Irawan, Rudi; Caesarendra, Wahyu
International Journal of Robotics and Control Systems Vol 4, No 2 (2024)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v4i2.1351

Abstract

The growing urbanization and the construction sector, efficient use of electric energy becomes important, especially the use of reactive power. If excessive use causes decreased efficiency and increased operational costs. Decreased efficiency contributes to increasing exhaust gas volumes and greenhouse emissions. Efficient energy can achieved if planning and predictions are correct. This research applies the GRU neural network method with grid search initialization as a novelty predictive model for energy-use high-rise buildings in form fast training without multiple iterations because optimal hyperparameters are obtained. Experimental show the MAE and RMSE performance metrics of the GRU better than LSTM in predicting energy consumption data peak loads, off-peak loads and reactive power. The accuracy of GRU predictions can optimize the use of energy to contribute to saving the environment from exhaust emissions and the greenhouse effect in urban systems. Experimental results demonstrate the superiority of GRU over LSTM, proof of the much lower MAE and RMSE values. This metric shows the accuracy of GRU in generalizing data both during peak and off-peak hours, as well as in reactive power usage. By Utilizing GRU's capabilities, building management can manage reactive power usage effectively, allocate reactive power resources appropriately, and mitigate peak load times and the power factor within the threshold, thus avoiding additional costs and electrical system efficiency and contributing to reducing the carbon footprint and gas emissions greenhouse. Research on GRU is widely open in the high-rise building sector, including its integration with sensors to automatically control energy use.
Motion System of a Four-Wheeled Robot Using a PID Controller Based on MPU and Rotary Encoder Sensors Sagita, Muhamad Rian; Ma’arif, Alfian; Furizal, Furizal; Rekik, Chokri; Caesarendra, Wahyu; Majdoubi, Rania
Control Systems and Optimization Letters Vol 2, No 2 (2024)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v2i2.150

Abstract

This research addresses the challenge of developing an effective motion system for a four-wheeled omnidirectional robot configured with wheels at a 45-degree angle, allowing for holonomic movement—motion in any direction without changing orientation. In this system, inverse kinematics calculates each wheel's angular velocity to optimize movement. PID control is implemented to stabilize motor speeds, while odometry guides and determines the robot’s position using initial and target coordinates. The robot operates on a 12-volt power supply and two STM32F103C microcontrollers, utilizing an MPU6050 sensor to maintain orientation and optical rotary encoders for accurate positional tracking. Experimental results demonstrate that the robot achieves optimal motion on x and y axes with PID settings of kP = 0.8, kI = 1.0, and kD = 0.08. This configuration yields a rise time of 0.95 seconds, overshoot of 7.36%, and steady-state error of -0.5 RPM at a setpoint of 350 RPM. Using odometry, the robot successfully navigates various movement patterns with average position errors of 1.2% on the x-axis and 1.6% on the y-axis for rectangular patterns, 2.1% on the x-axis and 2.2% on the y-axis for zig-zag patterns, and 1.75% on the x-axis and 1.15% on the y-axis for triangular patterns. The MPU6050 sensor maintains orientation with an error of 0.65% in triangular patterns and 0.85% in rectangular patterns. Through inverse kinematics, PID control, and sensor integration, the robot reliably follows designated coordinate points.
Innovations in Additive Manufacturing for Socket Fabrication: An Overview Faiza, Linda Ziyadatul; Caesarendra, Wahyu; Lestari, Wahyu Dwi
Journal of Mechanical Engineering Science and Technology (JMEST) Vol 8, No 2 (2024)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um016v8i22024p287

Abstract

Additive Manufacturing (AM) has transformed the prosthetics industry, particularly in socket production, which plays a critical role in the comfort, fit, and functionality of prosthetic limbs. This article examines the latest advancements in AM technologies and their applications in socket fabrication. Key techniques like stereolithography (SLA), selective laser sintering (SLS), and fused deposition modeling (FDM) have facilitated the production of highly personalized, lightweight, and durable prosthetic sockets. These methods not only improve design precision but also allow for the use of biocompatible, flexible materials, enhancing both comfort and functionality. Digital design tools have streamlined the production process, reducing lead times and costs, while improving accuracy and repeatability in socket manufacturing. This review explores the current state of AM in prosthetic socket development, emphasizing the benefits, challenges, and future directions of this fast-evolving field. By analyzing recent research and case studies, the article provides insights into how AM is reshaping prosthetics, offering more accessible solutions for individuals needing prosthetic limbs. It also discusses the challenges of material selection, regulatory considerations, and the potential for scaling production for broader use.
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 Vol 6, No 2 (2023)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um018v6i22023p157-169

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.
Challenges of implementing Industry 4.0 in developed and developing countries: A comparative review Surindra, Mochamad Denny; Caesarendra, Wahyu; Krolczyk, Grzegorz; Gupta, Munish Kumar
Mechanical Engineering for Society and Industry Vol 4 No 3 (2024): Special Issue on Technology Update 2024
Publisher : Universitas Muhammadiyah Magelang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31603/mesi.12177

Abstract

Indonesia could transform the manufacturing industry by making Indonesia 4.0, despite the many uncertainties of implementing Industry 4.0 due to high investment costs and unclear returns. Therefore, looking at neighboring countries such as Germany, the country that initiated Industry 4.0, and China, the country taking the lead in implementing Industry 4.0, it is considered essential for the manufacturing industry in Indonesia to understand how towards the revolution and identify the development of the Industry 4.0 program. Germany is confident in its capabilities in the field of manufacturing technology. It makes the main challenge in carrying out Industry 4.0 'Investment Capital, Employee Qualifications, and Security of Data Transfer and Legislation'. On the other hand, China faces significant challenges in Manufacturing Capabilities, Research and Development (R&D), and Human Capital. To adopt the transformation technology and self-assess the internal resources, Indonesia created a tool, namely the Indonesia Industry 4.0 Readiness Index (INDI 4.0). This article presents a comparative review of the Industry 4.0 readiness index from the perspective of Germany and Singapore as a developed country compared to developing countries such as China, Malaysia, and Indonesia. This study aims to provide awareness related to the readiness index, which can be used to inform industries whether they are suitable for applying Industry 4.0 and how to measure whether their employees are capable of it. In general, the INDI 4.0 measuring instrument shows the readiness of companies in Indonesia, and according to the recent assessment, the industries in Indonesia are at a moderate level, especially in the field of technology application and operation.
Implementation of Kalman Filter on Pid Control System for DC Motor Under Noisy Condition Setiawan, Nurman; Caesarendra, Wahyu; Majdoubi, Rania
Buletin Ilmiah Sarjana Teknik Elektro Vol. 6 No. 3 (2024): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v6i3.11236

Abstract

DC motors are actuators that are widely used in various fields. The reason is that DC motors are easy to control, high torque at low speed, and fast response. Angular velocity of DC motor is regulated automatically by using certain controls method, the most commonly used of which is PID control. The performance of the control system decreases in the presence of disturbance or noise. The presence of noise give has negative impacts such as instability in control response, decreased accuracy, and difficulty in tuning PID gain. The most common disturbance comes from the inaccuracy data due to measurement noise and process noise. In this study, the Kalman filter is proposed as a state estimator to reduce the influence of noise, both process noise and measurement noise. The Kalman filter provides an optimal estimate of the angular velocity of DC motor by minimizing the mean squared error. The estimated angular velocity from Kalman Filter is utilized as input for PID control. Simulation results show that the Kalman filter is capable to reduces the influence of measurement noise. In nominal condition, PID control give an Integral Absolute Error (IAE) of 344.56. Under noisy condition, PID control (without Kalman filter) has an IAE of 517.27, while Kalman filter-based PID control has an IAE of 345.25. The IAE reduction of 99.6% indicates that the proposed control system effectively minimizes errors, resulting in better performance and stability.
Analysis of Differences in Image Quality and Anatomical Information of Head CT Scan Examination in Non-Hemorrhagic Stroke Cases Using Sinogram Affirmed Iterative Reconstruction (SAFIRE) Samudra, Alan; Fitriana, Lutfatul; Hidayat, Fathur Rachman; Wibowo, Kusnanto Mukti; Ariesma Githa Giovany; Caesarendra, Wahyu
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.v7i2.629

Abstract

SAFIRE should be utilized to its full potential, as this innovative image reconstruction algorithm can significantly reduce image noise without loss of sharpness, preserving image quality and anatomical information. This is particularly important in the case of non-hemorrhagic stroke, where image noise can obscure small lesions, potentially leading to misdiagnosis and inappropriate treatment. SAFIRE has five variations of strength, making it essential to identify the most optimal SAFIRE Strength for head CT Scan examinations in non-hemorrhagic stroke cases. The aim of this study is to determine differences in image quality and anatomical information in head CT Scan of non-hemorrhagic stroke cases using SAFIRE variations to identify the most optimal SAFIRE Strength. This experimental quantitative study involved a sample of 30 patients, with each case reconstructed using five SAFIRE Strength variations. Image quality was assessed using the IndoQCT application, while anatomical information was evaluated through the visual grading analysis method by three radiologists. Image quality data were analyzed using the Friedman statistical test, which resulted in a p-value of 0.000 (p < 0.05), indicating significant differences among the SAFIRE Strength variations. Similarly, anatomical information data were analyzed using the Kruskal-Wallis statistical test, yielding a p-value of 0.000 (p < 0.05), confirming significant differences across the variations. The results of the study showed that there are significant differences in image quality and anatomical information among the five SAFIRE Strength variations. SAFIRE Strength 3 was identified as the most optimal for head CT Scan examinations in non-hemorrhagic stroke cases, as it produces images with minimal noise and higher detail, providing clearer anatomical information compared to the other SAFIRE Strength variations.
Performance Comparison of Extreme Learning Machine (ELM) and Hierarchical Extreme Learning Machine (H-ELM) Methods for Heart Failure Classification on Clinical Health Datasets Ichwan Dwi Nugraha; Triando Hamonangan Saragih; Irwan Budiman; Dwi Kartini; Fatma Indriani; Caesarendra, Wahyu
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 7 No 3 (2025): July
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v7i3.904

Abstract

Heart failure is one of the leading causes of death worldwide and requires accurate and timely diagnosis to improve patient outcomes. However, early detection remains a significant challenge due to the complexity of clinical data, high dimensionality of features, and variability in patient conditions. Traditional clinical methods often fall short in identifying subtle patterns that indicate early stages of heart failure, motivating the need for intelligent computational techniques to support diagnostic decisions. This study aims to enhance predictive modeling for heart failure classification by comparing two supervised machine learning approaches: Extreme Learning Machine (ELM) and Hierarchical Extreme Learning Machine (HELM). The main contribution of this research is the empirical evaluation of HELM's performance improvements over conventional ELM using 10-fold cross-validation on a publicly available clinical dataset. Unlike traditional neural networks, ELM offers fast training by randomly assigning weights and analytically computing output connections, while HELM extends this with a multi-layer structure that allows for more complex feature representation and improved generalization. Both models were assessed based on classification accuracy and Area Under the Curve (AUC), two critical metrics in medical classification tasks. The ELM model achieved an accuracy of 73.95% ± 8.07 and an AUC of 0.7614 ± 0.093, whereas the HELM model obtained a comparable accuracy of 73.55% ± 7.85 but with a higher AUC of 0.7776 ± 0.085. In several validation folds, HELM outperformed ELM, notably reaching 90% accuracy and 0.9250 AUC in specific cases. In conclusion, HELM demonstrates improved robustness and discriminatory capability in identifying heart failure cases. These findings suggest that HELM is a promising candidate for implementation in clinical decision support systems. Future research may incorporate feature selection, hyperparameter optimization, and evaluation across multi-center datasets to improve generalizability and real-world applicability.
Multisensor monitoring system for detecting changes in weather conditions and air quality in agricultural environments Ramadhani, Dwi; Taqwa, Ahmad; Handayani, Ade Silvia; Caesarendra, Wahyu; Husni, Nyayu Latifah; Sitompul, Carlos R
Journal of Environment and Sustainability Education Vol. 3 No. 2 (2025)
Publisher : Education and Development Research

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62672/joease.v3i2.103

Abstract

The increasing impact of climate change and the need for precision agriculture demand reliable environmental monitoring solutions.This study aims to develop a real-time, multisensor-based environmental monitoring system that displays data via an I2C LCD and a user-friendly web interface. The system utilizes an ESP32 microcontroller connected to a range of sensors, including the DHT22 (for temperature and humidity), MQ-7 and MQ-135 (for CO and CO₂), LDR (for light intensity), a rain sensor, and an anemometer (for wind speed). Testing was conducted over eight hours under various environmental conditions, both indoors and outdoors. Validation was performed by comparing the sensor readings with those from standard measuring instruments. The results showed that the DHT22 sensor had a low error rate of 0.62% for temperature and 0.38% for humidity. Other sensors demonstrated low standard deviation values, indicating stable and consistent measurements. The system also exhibited responsive and accurate performance in detecting changes in environmental parameters. Therefore, this system is effective as an environmental monitoring tool for agricultural applications and can support early decision-making based on environmental condition changes.
Co-Authors Abdullayev, Vugar Achmad Widodo Ade Silvia Handayani Agus Sudarmanto Agus Wantoro Ahmad Rofii Ahmad Taqwa Ahmed, Abdussalam Ali Alfian Ma’arif Anant Athavale, Vijay Andini, Dwi Yana Ayu Ariesma Githa Giovany Ariswati, Her Gumiwang Aryananda, Rangga Laksana Asriyadi Asriyadi Brilliant, Muhammad Zidan Busono Soerowirdjo Dewi, Deshinta Arrova Dian Setioningsih, Endang Dian Setioningsih1 Dwi Kartini Dwi Kartini, Dwi DWI RAMADHANI 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 Furizal, Furizal Gołdasz, Iwona Gupta, Munish Kumar Herianto Herianto Hidayat, Fathur Rachman Humairah, Sayyidah Ichwan Dwi Nugraha Ikna Awaliyani Irwan Budiman Irwan Budiman Joga Dharma Setiawan Krolczyk, Grzegorz 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 Nyayu Latifah Husni, Nyayu Latifah Pamanasari, Elta Diah 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 Siena, Laifansan Silvian, Fawaida Sitompul, Carlos R Sri Hastuty, Sri Sri Utami Handayani Sumarti, Heni Suwarno, Iswanto Triwiyanto , Triwiyanto Triyanna Widiyaningtyas Utomo, Bedjo V.H, Abdullayev W, Kusnanto Mukti Wahyu Dwi Lestari YILDIZ, Oktay Yuliana, Aviv Fitria Zy, Ahmad Turmudi