My Journal Universitas Muhammadiyah Yogyakarta (UMY) merupakan aplikasi yang digunakan untuk media informasi jurnal yang diterbitkan oleh Universitas Muhammadiyah Yogyakarta.
Research aims: Employee IT engagement is a critical component in the successful implementation of digital transformation. Both prior IT experience and support from upper management significantly influence the extent of employee involvement in IT. This study aims to explore the relationship between employee IT engagement, management's support, and understanding of employees' IT experiences, and how these factors impact employee performance.Design/Methodology/Approach: A quantitative method was employed through a survey involving 227 employees of PTNBH, examining the relationships between Individual IT Experience, Management Support, Employee IT Engagement, and Employee Performance. Research findings: The analysis reveals a strong correlation between management support, employee IT engagement and experience; all of which positively influence employee performance. Employee performance is enhanced through both direct and indirect effects of management support and individual’s experiences on IT engagement. The correlation between employees’ IT involvement and performance is statistically significant. Furthermore, the study underscores the critical role of management in promoting IT utilization for optimal performance outcomes and highlights the substantial impact of employees' individual IT experiences on their performance.Theoretical Contribution/Originality: This study contributes theoretically by presenting an integrative model and practically by providing recommendations for PTNBH managers to accelerate digital transformation success.Practitioners/Policy Implications: The practical implications include recommendations for PTNBH managers to drive digital transformation successfully.Research Limitations/Implications: The research is limited to personnel at legal entity universities. Future studies should expand the sample to include employees from universities, whether academic institutions or government agencies.
Enhanced Angle Estimation Using Optimized Artificial Neural Networks with Temporal Averaging in IMU-Based Motion Tracking
Accurate angle estimate is crucial for motion tracking systems, especially in biomedical applications like rehabilitation, prosthesis control, and wearable health monitoring. Traditional filters, such as the Kalman filter, frequently encounter difficulties with nonlinear noise and dynamic variations, hence constraining their resilience. This study presents feedforward artificial neural network (ANN) models as a highly accurate alternative by utilizing IMU sensor data from a gyroscope and accelerometer. The research contribution encompasses: (1) the creation of ANN architectures of diverse complexity, featuring an innovative (4×8) structure with temporal averaging for enhanced noise resilience; (2) a simulation-based assessment in comparison to the Kalman filter utilizing consistent performance metrics; and (3) an evaluation of execution-time viability for embedded applications. A dataset including 3,599 samples was acquired from an MPU6050 IMU and partitioned into 70% for training, 15% for validation, and 15% for testing. Model assessment was conducted utilizing mean absolute error (MAE) and root mean squared error (RMSE). The NN (4×8 + Averaging) model produced a minimum MAE of 0.2657 and an RMSE of 0.3691, indicating a 68% enhancement compared to the Kalman filter. Although compact models (2×4, 2×8) exhibited marginal improvements, deeper architectures demonstrated superior generalization and resilience, especially during dynamic motion phases. These results show that ANN-based estimators offer better accuracy and adaptability, making them a good choice for real-time biomedical uses. Future research will investigate hybrid ANN-Kalman designs and assess their performance across diverse motion types, including gait cycles and robotics.
Adaptive Neural Network Control for Load-Varying Two-Link Robots Using Honey Badger Optimization
This paper illustrates a proportional-integral-derivative based neural network (PID-NN) controller to manipulate the angular position of the two-link robot considering the load variation on the system. The two-link robot system's dynamic equations were derived using the Lagrange method. To improve the tuning process of the design coefficients of the controller, the learning process was framed as an optimization task. Subsequently, to determine the optimal weight values, the honey badger algorithm (HBA) was introduced. To analyze how well the proposed controller performs, Simulations in MATLAB were carried out to compare the PID-NN controller against a PI-PD controller. The findings revealed superior performance of the PID-NN controller in standard conditions. Furthermore, the PID-NN demonstrated a substantial enhancement when a load variation was augmented.
Comparison of Adaptive Sliding Mode Controllers in Earthquake Induced Vibrations
Alaa Al-Tamimi Al-Tamimi, T. MohammadRidha
Published: 01 May 2025 by Universitas Muhammadiyah Yogyakarta in Journal of Robotics and Control (JRC)
This study aims to reduce earthquake-related vibrations in buildings. This is achieved by designing two different robust adaptive control algorithms to control the damping force of the dampers. This design is used in mitigating the structural vibrations of a three-story prototype building exposed to two different scaled earthquakes. Two cases are considered where two damping systems are employed and mounted on the top floor: an Active Tuned Mass Damper (ATMD), the second damper is a semi-active Magnetorheological Damper (MRD). The first damper depends entirely on the control algorithm to correct structural movement; the second one operates as a passive damper under minor vibrations and becomes active under stronger vibrations. The results showed that one of the adaptive algorithms give a better displacement reduction and error indices across all floors. Furthermore, integrating that controller with MRD demonstrated higher accuracy in tracking the structural response with less control effort compared to ATMD. To validate the method effectiveness, it was compared to another robust sliding mode controller from the literature. The results show a significant improvement in displacement reduction and less control effort by 40.23% better than the control effort of the previous work. These findings highlight the potential of combining advanced control strategies with semi-active damping systems for effective vibration mitigation and energy efficiency.
Application of an Adaptive Dynamic Sliding Surface Controller with Traction Tracking for a Mecanum Wheel Mobile Robot
Ha Vo Thu, Thuong Than Thi, Thanh Nguyen Thi, Binh Nguyen Hai, Dung Vu Van
Published: 30 April 2025 by Universitas Muhammadiyah Yogyakarta in Journal of Robotics and Control (JRC)
The paper introduces an algorithm application of an adaptive Dynamic Sliding Surface Controller that integrates neural networks and fuzzy logic systems with traction tracking for a Mecanum Wheel Mobile Robot. In this framework, neural networks are employed to approximate the uncertain nonlinear numerical aspects of MWMR, while fuzzy logic systems are utilized to adaptively. The stability of the closed-loop system is investigated using the Lyapunov criterion. The proposed controller is verified by numerical simulation. The simulation results show that the proposed controller performs better than the backstepping sliding controller in the case of uncertain model parameters and when there is an impact disturbance.
Integration of Sparrow Search Optimization with Terminal Synergetic Control for Permanent Magnet Linear Synchronous Motors
Mohanad Nawfal, Ammar A. Yahya, Rawnaq A. Mahmod, Huthaifa Al-Khazraji
Published: 29 April 2025 by Universitas Muhammadiyah Yogyakarta in Journal of Robotics and Control (JRC)
This paper proposes a theoretical framework of the procedure to design an optimal robust terminal synergetic control (TSC) for the permanent magnet linear synchronous motors (PMLSMs). The general component and the mathematical equations of the PMLSM are first introduced. Based on the established model of the PMLSM, the control law of the TSC is developed. The tuning process of the TSC gains is enhanced by employing sparrow search optimization (SSO) based on the Integral Time of Absolute Errors (ITAE). The effectiveness of the proposed control algorithm has been verified by numerical simulations using MATLAB software for a step input. Additionally, the results have been compared with the classical synergetic control (CSC). The comparison shows that the TSC exhibits a good performance in normal operation and in a robustness test involving system parameters’ changes as compared to the CSC.
Disturbance Handling and Efficiency Optimization for SPWM-Three Phase Inverter by Using PID Controller System
Suaad Makki Jiaad, Salam Waley Shneen, Rajaa Khalaf Gaber
Published: 27 April 2025 by Universitas Muhammadiyah Yogyakarta in Journal of Robotics and Control (JRC)
The importance of studying inverters in electrical systems is highlighted by their role as one of the most important electronic power devices used in numerous applications in industry, as well as in generation, transmission, and distribution, most notably in renewable energy generation systems. An inverter converts direct current power into alternating current power to power loads or connect solar energy sources to the grid. Inverters are built using electronic switches such as thyristors or transistors such as IGPTs and MOSFET transistors. A number of switches are used to build the inverter, depending on the type of inverter, whether single-phase or three-phase. It can also be half-wave or full-wave. The current study proposed a bridge inverter consisting of six electronic switches of the IGBT transistor type arranged in two rows and three columns. To operate the inverter, pulse width modulation (PWM) technology was used to regulate the inverter operation and obtain the required output to supply a three-phase resistive load. In addition, an LC filter was connected to obtain a pure sine wave. Due to the different and variable operating conditions and to overcome disturbances, a conventional control unit was added to improve performance and raise the efficiency of the system. After conducting the proposed tests, the possibility of obtaining an inverter that operates with an efficient system to cover the load requirements under variable operating conditions was verified.
Effectual Energy Optimization, Fault-Tolerant Attack Detection, and Data Aggregation in Healthcare IoT Using Enhanced Waterwheel Archimedes and Deep Siamese Maxout Forward Harmonic Networks
Ganesh Srinivasa Shetty, Raghu N
Published: 25 April 2025 by Universitas Muhammadiyah Yogyakarta in Journal of Robotics and Control (JRC)
The Internet of Medical Things (IoMT) has emerged as a transformative technology for improving healthcare delivery and patient outcomes. However, IoMT systems face significant challenges, including high latency, energy inefficiency, and vulnerability to cyberattacks, which compromise data security and patient privacy. Existing methods for attack detection and secure routing in IoMT often suffer from high latency, limited fault tolerance, and insufficient accuracy in identifying sophisticated attacks. To address these challenges, this paper proposes two novel approaches: the Improved Waterwheel Archimedes Optimization Algorithm (WWAOA) for secure routing and the Deep Siamese Maxout Forward Harmonic Network (DSMFHN) for attack detection in healthcare IoT. The Improved WWAOA integrates the Waterwheel Plant Algorithm (WWPA) with the Archimedes Optimization Algorithm (AOA) to optimize cluster head (CH) selection and secure routing. It considers key fitness parameters such as energy consumption, link lifetime (LLT), trust, delay, distance, and fault tolerance to enhance network efficiency and resilience. The DSMFHN combines Siamese Neural Networks (SNN) and Deep Maxout Networks (DMN) with forward harmonic analysis to detect attacks with high accuracy and low false positive rates. Additionally, data aggregation is performed using Bidirectional Long Short-Term Memory (BiLSTM) with adaptive weightage based on fault and malicious node detection. Experimental results demonstrate that the proposed methods outperform existing techniques. The Improved WWAOA achieves a minimal delay of 0.557 ms, maximal energy efficiency of 0.182 J, a packet delivery ratio (PDR) of 93.894%, and a trust value of 87.152. Meanwhile, the DSMFHN achieves a high accuracy of 92.598%, a true positive rate (TPR) of 91.643%, and a low false positive rate (FPR) of 0.156. These results highlight the effectiveness of the proposed methods in addressing the critical challenges of latency, energy efficiency, and security in healthcare IoT systems.
Novel Hybrid SM Strategy Based on Speed Control and Disturbances Rejection for High Performance DSIM Drives
Ngoc Thuy Pham
Published: 22 April 2025 by Universitas Muhammadiyah Yogyakarta in Journal of Robotics and Control (JRC)
In control theory and applications, disturbance cancellation is a critical challenge in the control of nonlinear drive systems, particularly in applications involving Dual Star Induction Motors (DSIM). This paper proposes a new adaptive hybrid sliding mode (SM) strategy that integrates a Repetitive Control (RC) scheme into an improved Second-Order Sliding Mode (SOSM) structure. The goal is to enhance tracking accuracy and periodic harmonic disturbance rejection in DSIM drive systems. The strategy also incorporates a load torque disturbance estimator that efficiently identifies and cancels disturbances, further improving system performance. System stability is guaranteed using Lyapunov theory, ensuring that the virtual control vectors for speed and current loops maintain stability throughout the operation. Simulation results using MATLAB confirm the effectiveness of the proposed control strategy, demonstrating improved tracking performance, harmonic disturbance rejection, and robust operation of the DSIM under varying conditions.
Advancing Cardiovascular Risk Prediction: A Review of Machine Learning Models and Their Clinical Potential
Rona Regen, Hendra Setiawan
Published: 21 April 2025 by Universitas Muhammadiyah Yogyakarta in Journal of Electrical Technology UMY
This study conducts a systematic literature review on the application of machine learning technology in predicting heart disease risk. A total of 20 recent articles were identified and analyzed to evaluate the most used algorithms and their performance. The results show that Random Forest (RF), Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), and K-Nearest Neighbors (KNN) are the most frequently applied models, with average accuracies of 89.56%, 83.14%, 83.14%, 82.57%, and 79.40%, respectively. In addition to comparing accuracy, this review also evaluates the strengths, weaknesses, and potential challenges of implementing each algorithm in clinical applications. The analysis reveals that Random Forest demonstrates high stability and accuracy, making it the leading candidate for large-scale clinical heart disease risk prediction applications. These findings are expected to provide new insights for the development of more accurate, reliable, and clinically deployable machine learning predictive models to support medical decision-making.
Pleasure Reading, Better Understanding: A Young EFL Learner’s Journey in Digital Extensive Reading
Background: The struggle of a young English as a foreign language (EFL) learner in the midst of advanced technology to find pleasure and develop good comprehension in reading English materials necessitates the need to employ effective approaches in their reading experience.
Objective: This study investigated the implementation of digital extensive reading (DER) with a young EFL learner in Indonesia, exploring which established principles of extensive reading (ER) by Day and Bamford (1998) could be utilized in a digital context to foster reading for pleasure and comprehension.
Methods: This study employed a qualitative case study research design involving a seven-year-old young EFL learner in Indonesia. The data were obtained through multiple sources, including a learner diary, audio-recorded observations of ten DER sessions, and an interview with the learner’s father. The data were analyzed using thematic analysis, referencing Day and Bamford’s (1998) top ten principles of ER.
Findings: The findings revealed that eight ER principles could be effectively implemented in the young EFL learner’s DER experience, showcasing the learner’s positive attitudes towards reading and enhanced reading comprehension.
Conclusion: Considering the result, the study recommends future studies to dig deeper into the effectiveness of DER across diverse learners by taking into account factors such as different ages, language proficiencies, learning styles, and technology access.
Integrating Multi-Sensors and AI to Develop Improved Surveillance Systems
Preeti Mohanty, Manu S R, Shreyas M, Vishnumahanthi Uttam, Bhagya R Navada, Sravani V, Santhosh K V
Published: 19 April 2025 by Universitas Muhammadiyah Yogyakarta in Journal of Robotics and Control (JRC)
This paper explores advancements in surveillance systems, focusing on the integration of multisensory and AI technologies in urban and environmental monitoring. It highlights the fusion of data sources such as video feeds, LiDAR, and wireless networks for enhanced real-time surveillance in complex environments. Artificial intelligence (AI) plays a critical role in anomaly detection, object identification, and behavior analysis, improving response times in high-traffic and security-sensitive areas. However, these technologies raise privacy concerns, emphasizing the need for responsible data management and ethical frameworks. Also, there is probability of false positives which can lead to unnecessary action disturbing the normal mode of life. These technologies involve high financial requirements hence must be used judiciously. In current study human surveillance is carried out in indoor environments by two AI algorithms: YOLOV5 and R-CNN. The results of these algorithms can be fused with LiDAR data for better decision making. R-CNN produced better results than YOLOV5 but the fusion with sensor data led to accurate detection of humans in indoor environments. R-CNN showcased better results than YOLOV5. The future of surveillance should focus on balancing safety and personal rights while adapting policies to ensure privacy and accountability in an increasingly tech-driven world.
Design of a Novel Observer-Based SMC for WECS System Using PMSG to Obtain Maximum Energy
Dang Quoc Du, Tran Duc Chuyen
Published: 17 April 2025 by Universitas Muhammadiyah Yogyakarta in Journal of Robotics and Control (JRC)
This paper studies and proposes a new sliding mode controller (SMC) for a wind energy conversion system (WECS) using a permanent magnet synchronous wind turbine generator (PMSG) to harvest maximum energy when the wind speed changes. In addition, the paper introduces a nonlinear disturbance observer (NDOB) to estimate the actual wind speed, to provide input to the proposed controller that the authors have studied. The control scheme proposed in this study not only considers the changes in the system parameters, but also considers the randomness of the wind speed changes. The effectiveness of the new SMC design and control scheme is demonstrated by simulation results on Matlab/Simulink software. These results are shown in the change of wind speed deformation, wind deformations that the turbine receives, turbulence assessment, the observer also estimated the non-snow parameters, the system also takes into account maximum power point tracking (MPPT), always consistent with the proposed control law. Moreover, the research results also show that the system works stably, the output is always close to the set value, the system works with high quality, thereby proving that the research results have been studied by the authors are suitable, bringing great benefits in the control process.
Early Detection of Diabetes Mellitus in Women via Machine Learning
Ahmad Zaki Arrayyan, Sisdarmanto Adinandra
Published: 17 April 2025 by Universitas Muhammadiyah Yogyakarta in Journal of Electrical Technology UMY
Diabetes Mellitus (DM) is a major global health concern, responsible for 6.7 million deaths in 2021, equivalent to one death every five seconds. In Indonesia, it was the third leading cause of death in 2019, with a mortality rate of approximately 57.42 per 100,000 people. This study focuses on developing a diabetes prediction model using machine learning, aiming for an accuracy of at least 85%, and incorporates a chatbot-based system to identify potential diabetes in women. The research utilizes primary data, including glucose levels, blood pressure, body mass index, and age, as well as secondary data, such as pregnancy-related metrics, from the UCI Pima Indians Diabetes Database, which contains 768 records with eight attributes. The study evaluates the performance of three machine learning algorithms: Decision Tree, Logistic Regression, and Random Forest, using metrics such as accuracy, precision, recall, and F1-score. Among these models, the Decision Tree demonstrates excellent performance for Class 0, with precision, recall, and F1-score all at 0.97. However, its performance for Class 1, while decent, leaves room for improvement, achieving a precision of 0.80 and a recall of 0.84, resulting in an F1-score of 0.82. Logistic Regression also performs well for Class 0, with a precision of 0.95 and a recall of 0.99, yielding an F1-score of 0.97. Yet, it struggles with Class 1, where its precision is high at 0.93, but its recall drops significantly to 0.68, producing an F1-score of 0.79. Lastly, Random Forest emerges as the best-performing model overall, achieving an accuracy of 0.96. It excels for Class 0, with a precision of 0.96 and a recall of 0.99, leading to an F1-score of 0.97. For Class 1, it maintains high precision (0.93) but exhibits moderate recall (0.74), resulting in an F1-score of 0.82.
Design and Implementation of Solid Medical Waste Sorting System Based on Inductive Proximity Sensor and Radio Frequency Identification (RFID)
Medical waste separation is a crucial issue in Indonesia, because of the potential dangers it poses, such as the risk of spreading diseases and environmental pollution. This research aims to make innovations in solid medical waste sorting systems, to sort waste automatically using Inductive Proximity sensors and Radio Frequency Identification RFID. The results of the inductive proximity sensor test are able to accurately detect metal medical waste with an average detection time of 117.5 ms. RFID is also able to read ID cards accurately. The system offers innovative solutions to improve occupational safety and sustainable separation of solid medical waste.
Optimal TID Tracking Control for Industrial Delta Robot Based on Harmony Search
Donia Saleem, Hasan Eleashy, Mohamed A. Shamseldin
Published: 16 April 2025 by Universitas Muhammadiyah Yogyakarta in Journal of Robotics and Control (JRC)
This paper seeks to enhance the delta robot accuracy using the Tilt-Integral-Derivative (TID) technique. A CAD model for a real delta robot was developed on SolidWorks®. Then a Simscape model was generated using MATLAB® to apply the proposed control technique. The proposed TID technique was compared with the Proportional integral derivative (PID) control to ensure robustness. The harmony search (HS) optimization was used to find the optimal parameters of the PID and the TID controllers based on an effective objective function. Several operating points of robot angles were applied to investigate the accuracy of each control technique. The results show that the TID based on harmony search had the best settling time, rise time, minimum overshoot, and minimum steady-state error.
Revitalizing green economic capability to maintain the financial stability of MSMEs in Bira Beach
ST Salmah Sharon, Monalisa Monalisa, Muchtar Muchtar, Afrizal Firman, Mustika Kusuma Basir, Muh Arif
Published: 16 April 2025 by Universitas Muhammadiyah Yogyakarta in Jurnal Ekonomi & Studi Pembangunan
To maintain the financial stability of Micro, Small and Medium Enterprises (MSMEs) is a vital issue which needs a revitalization of green economic capability. This study explores the impact of Green Economy Capability (GEC) on the financial stability of MSMEs in the coastal region of Bira Beach, with a focus on the mediating role of government support. A SmartPLS-SEM used in this study to investigate the survey involving 150 MSMEs. The key variables measured include GEC, financial stability, and government support, with the data analyzed through descriptive and inferential statistical techniques. The findings indicate that GEC significantly influences government support, which in turn has a positive effect on financial stability. However, GEC does not have a direct impact on MSMEs' financial stability. These findings underscore the critical role of government policies in supporting the adoption of sustainable practices among MSMEs, particularly in regions heavily dependent on tourism. This research contributes to the literature by providing empirical evidence of the indirect relationship between GEC and financial stability through government support in the coastal MSME sector. In results, we offered two solutions. First, the policymakers must prioritize initiatives that strengthen MSME’s capacity for sustainable practices. Second, the need for tailored support systems in coastal areas like the adoption of green practices which must be integrated with local economic strategies to yield both environmental and financial benefits.
Understanding payment switching behavior to QRIS in Southwest Papua: A push-pull-mooring study
Nurul Hidayah, Latifah Dian Iriani
Published: 16 April 2025 by Universitas Muhammadiyah Yogyakarta in Jurnal Ekonomi & Studi Pembangunan
As the global economy changes, it's important to examine why people in Southwest Papua switch from cash to the Quick Response Code Indonesian Standard (QRIS) system. This region is notoriously behind in infrastructure and human development, so studying this area is crucial to expanding the national economy. This study investigates the variables that influence the switching behavior of users in Southwest Papua toward the Quick Response Code Indonesian Standard (QRIS) digital payment. The study investigates the impact of push factors (e.g., perceived trouble, perceived no record for transactions, and difficulty paying cash in large amounts), pull factors (e.g., perceived convenience, promotional benefits, and time savings), and mooring factors (e.g., habit and switching costs) on the transition from cash to digital payments, utilizing the Push-Pull-Mooring (PPM) framework and transaction cost theory. The research uses a method called structural equation modeling to study how QRIS users behave in three areas of Southwest Papua Province: Sorong City, Sorong Regency, and Raja Ampat, by selecting participants randomly from different groups. The results indicate that the adoption of QRIS is significantly influenced by both push and pull factors, with pull factors playing a more prominent role. Habit also significantly influences switching behavior, while switching costs show a negative but statistically insignificant effect. To expedite the adoption of digital payments in Southwest Papua, these insights provide policymakers and financial institutions with practical advice. They indicate that strategies that emphasize the convenience and advantages of QRIS over cash are more effective in increasing user adoption.
Optimizing Proportional Integral (PI) Controller Using Particle Swarm Optimization (PSO) Method in Active Rectifier
Muhammad Fahril Anam, Trias Andromeda, Iwan Setiawan
Published: 16 April 2025 by Universitas Muhammadiyah Yogyakarta in Journal of Electrical Technology UMY
In the era of Industry 4.0, power converters such as Active Rectifiers have become crucial for converting AC voltage to adjustable DC voltage. While implementing a DC Constant Power Load is beneficial, it introduces additional complexity in maintaining power system stability. This research optimizes PI control on Active Rectifiers using the PSO method to address this challenge. The results indicate that the PI controller optimized with PSO achieved a Kp of 0.4509 and a Ki of 2.7611 in tests with resistive loads, and a Kp of 3.1364 and a Ki of 6.8141 in tests with constant power loads. Using constant power loads showed a faster response with lower rise time but often resulted in higher overshoot compared to resistive loads. Nevertheless, both testing conditions demonstrated a stable system without undershoot, confirming the effectiveness of the PI-PSO controller in optimizing the performance of active rectifiers for more responsive and efficient power electronics applications.
Optimizing the Capacity of 150 kV Transmission Lines Through the Addition of Shunt Capacitors: Case Study at PT. PLN (Persero) West Sumatra Subsystem
This study evaluates the impact of shunt capacitor installations on the voltage stability in the electrical network of the West Sumatra subsystem, focusing on the Pauh Limo, PIP, and Simpang Haru substations. Although the Simpang Haru Substation was initially identified as the optimal location for enhancing voltage regulation, practical considerations, such as land availability and cost, led to its installation at the Pauh Limo Substation. The required shunt capacitor capacities to maintain a nominal voltage of 150 kV during peak loads were calculated as 155.57 MVAr for Pauh Limo, 82 MVAr for PIP, and 78.15 MVAr for Simpang Haru. However, PT PLN implemented a uniform capacity of 25 MVAr across these substations. Despite this deviation from the calculated requirements, the installations effectively maintained the voltage within standard limits, with post-installation voltages at 144.3 kV, 144.9 kV, and 143.9 kV for the respective substations. This study demonstrates the necessity of balancing theoretical ideals with practical constraints in electrical engineering, highlighting that while optimal solutions are desired, real-world limitations often guide implementation strategies.
Addressing Rogue Nodes and Trust Management: Leveraging Deep Learning-Enhanced Hybrid Trust to Optimize Wireless Sensor Networks Management
Santosh Anand, Anantha Narayanan V
Published: 14 April 2025 by Universitas Muhammadiyah Yogyakarta in Journal of Robotics and Control (JRC)
Comprising a multiplicity of AdHoc sensors working in concert to monitor a range of environmental and physical factors for the targeted area, wireless sensor networks (WSNs). These sensors are used to provide continuous environmental status like temperature, pressure, and humidity by forwarding vital data to the internet through a base station. Aiming to greatly increase the security and performance of WSNs, this study presents a new framework that is a combination of the Deep Learning-Enhanced Hybrid Trust (DLEHT) model and the Machine Learning-Enhanced Fuzzy-Based Routing Protocol (ML-EFBRP). In this research, enhanced packet delivery, packet drop reduction, and the rogue nodes addressed in WSN from source to sink using a probabilistic approach, which depends on the experience of data with the integration of a sum-rule weight mechanism in HMM (Hidden Markov Model). Integration methodology played a major role in deep learning to observe the normal and abnormal node behavior with historic data. It enhanced the throughput and lowered latency with successful detection and addressing of rogue nodes by the integrated strategy. The proposed work, reflects an improvement in performance, both in terms of throughput and latency. The delay hyperparameters are observed, which vary from 7.48 to 26.22 ms with an average of 15.855 ms. And the packet is controlled and decreased by 7%, showcasing more improvement compared to existing work. Simulation results show considerable improvements in network accuracy, reliability, energy efficiency, and resistance during node failures and security concerns for network correctness. These findings show the combination of DLEHT and ML-EFBRP models provides stronger monitoring systems, hence enhancing operational efficiency in settings with limited resources.
A Software-Centric Evaluation of the VEINS Framework in Vehicular Ad-Hoc Networks
Jalal Mohammed Hachim Altmemi, Faris K. AL-Shammri, Zainab Marid Alzamili, Mahmood A. Al-Shareeda, Mohammed Amin Almaiah, Rami Shehab, Md Asri Bin Ngadi, Abdulaziz Zaid A. Aljarwan
Published: 12 April 2025 by Universitas Muhammadiyah Yogyakarta in Journal of Robotics and Control (JRC)
The Large Communication Substitution between vehicles to infrastructure (V2I) or between vehicles (V2V), called as Vehicular Ad-Hoc Networks (VANETs), in new Tale Intelligent Transportation Systems (ITS) are degree and stop developing for extensive traffic manage, highway safety, and driverless cars. VEINS is a simulator framework that couples OMNeT++ and SUMO, widely used for assessing performance of VANET protocols and network architectures. However, it was observed that no existing research reviewed VEINS applications, limitations, or emerging trends in a structured manner. In this paper we provide a software-oriented summary of VEINS-based VANET studies. This research adds a taxonomy-oriented classification of studies published from 2011 to 2022, focusing on IEEE Xplore, ScienceDirect, and Scopus categorized security, safety, and other VANET applications. It identifies some gaps, right from the scalability, and computational overhead aspect to the limited integration of the next-generation technologies like 5G, Blockchain, and AI. A well-defined article selection strategy, extensive data extraction, and a comparative analysis of published VEINS studies is followed throughout the study. Statistical analyses show a growing percentage of VEINS but also point out obstacles to its real world usage. Key Insights point to an emphasis on security and safety, with little focus on emerging technologies and real-world validations. This review adds value to the body of knowledge by (1) establishing a systematic taxonomy of VEINS-based research, (2) highlighting gaps in research and methodological limits, and (3) providing future research directions focused on VEINS scalability enhancement, real-world validation frameworks, and AI-enabled VANET optimizations. The study is expected to be a useful reference for researchers and practitioners who intend to improve VEINS-based simulations of VANETs and accelerate development in the field of ITS.
Cultivating Digital Learning Culture: Perspectives of Pre-Service English Teachers at a Private Islamic University in Yogyakarta
Suryanto Suryanto, Tyas Panuntun
Published: 12 April 2025 by Universitas Muhammadiyah Yogyakarta in Journal of Foreign Languange Teaching and Learning
Background: The transformation of education in the 21st century demands the development of a digital learning culture that goes beyond merely providing access to technology. It also requires the integration of academic ethics, collaborative practices, and digital literacy into the teaching and learning process. In response to these demands, this study is needed to explore the practices and challenges of fostering a contextual and sustainable digital learning culture among prospective English teachers.
Objective: This paper investigates the practices and challenges in the development of a digital learning culture among pre-service English teachers at a private Islamic university in Yogyakarta.
Methods: Utilizing a descriptive qualitative design, the research captures the perceptions and experiences of six participants from the 2021 cohort, selected through purposive sampling. Data were gathered via online interviews conducted in Indonesian to ensure clarity and comfort, followed by rigorous analysis involving transcription, member checking, and systematic coding (open, analytical, axial, and selective).
Findings: Findings reveal six key practices for fostering a digital learning culture: adherence to academic ethics, staying updated with digital tools, promoting digital literacy, employing student-centered learning, participating in training programs, and fostering collaboration. Three significant challenges were identified: managing digital learning activities, fears of digitization replacing teachers, and negative perceptions of digital learning’s value.
Conclusion: The study concludes that addressing these challenges through targeted training, ethical practices, and institutional support is essential for effectively implementing a digital learning culture in English education.
A Comparative Analysis of Numerical Techniques: Euler-Maclaurin vs. Runge-Kutta Methods
Mohammad W. Alomari, Iqbal M. Batiha, Abeer Al-Nana, Mohammad Odeh, Nidal Anakira, Shaher Momani
Published: 12 April 2025 by Universitas Muhammadiyah Yogyakarta in Journal of Robotics and Control (JRC)
This study introduces a novel higher-order implicit correction method derived from the Euler-Maclaurin formula to enhance the approximation of initial value problems. The proposed method surpasses the Runge-Kutta approach in accuracy, stability, and convergence. An error bound is established to demonstrate its theoretical reliability. To validate its effectiveness, numerical experiments are conducted, showcasing its superior performance compared to conventional methods. The results consistently confirm that the proposed method outperforms the Runge-Kutta method across various practical applications.
Extreme Learning Machine-Based Repetitive Proportional Derivative Controller for Robust Tracking and Disturbance Rejection in Rotational Systems
Enggar Banifa Pratiwi, Prawito Prajitno, Edi Kurniawan
Published: 10 April 2025 by Universitas Muhammadiyah Yogyakarta in Journal of Robotics and Control (JRC)
Tracking periodic signals and rejecting periodic disturbances are common applications of repetitive control (RC). However, traditional RC methods struggle to compensate for aperiodic disturbances and adapt to system uncertainties, limiting their real-world effectiveness. Existing hybrid approaches often require extensive parameter tuning or suffer from high computational costs, creating a research gap in achieving both adaptability and efficiency. This paper proposes an improved control strategy called extreme learning machine repetitive proportional derivative control (ELMRPDC), which integrates repetitive proportional derivative control (RPDC) with an extreme learning machine (ELM). RPDC ensures accurate tracking of periodic signals, while ELM estimates and compensates for disturbances, enhancing overall performance. Unlike conventional neural network-based controllers, ELM enables rapid adaptation with minimal computational overhead, making it more suitable for real-time applications on resource-constrained systems. The proposed method is analyzed for stability using the Lyapunov approach, ensuring convergence of tracking errors. Extensive simulations are conducted on both rotational and linear dynamic systems under various disturbance conditions, including periodic, time-varying, multi-periodic, and aperiodic disturbances, such as vibration-induced disruptions in machinery. The study also evaluates the impact of hidden layer neuron variations in ELM on disturbance rejection. The best performance is observed for multi-period sinusoidal disturbances, achieving an RMSE of 1.8630 degrees at 1500 neurons, reducing error by 67.47% compared to conventional RPDC. These results highlight ELMRPDC’s advantages in computational efficiency, real-time feasibility, and robustness against complex disturbances. The approach holds significant promise for precise reference tracking and disturbance rejection across diverse industrial applications.
Pengembangan Sistem Pengukuran Tingkat Stres Menggunakan Sensor GSR dengan Perbandingan Metode PSS
Rechi Yudha Apza, Rino Ferdian Surakusumah
Published: 10 April 2025 by Universitas Muhammadiyah Yogyakarta in Medika Teknika Jurnal Teknik Elektromedik Indonesia
Stres merupakan faktor yang dapat mempengaruhi kesejahteraan dan kinerja akademik mahasiswa, sehingga diperlukan metode pengukuran yang akurat dan praktis. Penelitian ini bertujuan untuk mengembangkan sistem pengukuran tingkat stres berbasis sensor Galvanic Skin Response (GSR) yang terintegrasi dengan Internet of Things (IoT) dan membandingkan hasilnya dengan metode Perceived Stress Scale (PSS). Sistem yang dikembangkan memungkinkan pemantauan stres secara real-time melalui aplikasi smartphone. Metode penelitian menggunakan pendekatan kuantitatif dengan tahap perancangan, pengembangan, serta evaluasi validitas dan reliabilitas sistem. Pengujian dilakukan terhadap 20 mahasiswa semester akhir, dengan hasil menunjukkan bahwa 35% responden dalam kondisi normal, 35% mengalami stres ringan, 10% stres sedang, 5% stres berat, dan 15% mengalami error dalam pengukuran. Perbandingan antara sensor GSR dan metode PSS menunjukkan tingkat kesesuaian sebesar 83,33%, dengan rata-rata selisih nilai sebesar 16,67%, di mana metode PSS cenderung memberikan skor stres yang lebih tinggi dibandingkan sensor GSR. Selain itu, evaluasi usability menunjukkan bahwa sistem memiliki tingkat kepuasan pengguna yang tinggi, dengan skor rata-rata usability 4,48, simplicity 4,35, dan interactivity 4,31 dari skala 5. Kesimpulannya, sistem berbasis sensor GSR yang dikembangkan telah terbukti dapat mengukur tingkat stres dengan tingkat akurasi yang cukup baik dan memiliki potensi sebagai alat pemantauan stres yang objektif, praktis, serta mudah digunakan.
Analisis Fitur Citra untuk Deteksi Kanker Prostat Menggunakan GLCM dan T-Test
Hendra Setiawan, Mhd. Hanafi, Yessi Jusman
Published: 10 April 2025 by Universitas Muhammadiyah Yogyakarta in Medika Teknika Jurnal Teknik Elektromedik Indonesia
Kanker prostat merupakan salah satu kelainan paling umum pada kelenjar prostat yang dapat menyebabkan gangguan buang air kecil hingga nyeri tulang akibat penyebaran ke tulang. Penelitian ini bertujuan untuk menganalisis perbedaan antara sel prostat normal dan abnormal menggunakan metode pengolahan citra Gray Level Co-occurrence Matrix (GLCM) dan klasifikasi statistik T-Test. Data citra yang digunakan memiliki resolusi 1024 × 1024 piksel dalam format grayscale, diperoleh dari hasil pencitraan mikroskop cahaya. Citra tersebut kemudian diekstraksi menggunakan GLCM untuk mendapatkan nilai tekstur seperti kontras, korelasi, energi, dan homogenitas. Proses klasifikasi dilakukan menggunakan uji T-Test dengan parameter P-value sebagai acuan validitas fitur dalam membedakan sel normal dan abnormal. Hasil penelitian menunjukkan bahwa dari 16 fitur yang diekstraksi, sebanyak 14 fitur memiliki nilai P-value < 0,05 yang berarti dapat membedakan sel normal dan abnormal, sedangkan 2 fitur (Energy2 dan Energy4) tidak signifikan dalam klasifikasi. Dengan demikian, metode GLCM dan T-Test terbukti efektif dalam analisis citra sel prostat dan dapat diintegrasikan ke dalam sistem diagnosis berbasis citra untuk mendukung deteksi dini kanker prostat.
Implementasi Logika Fuzzy pada Kontrol Suhu Showcase Reagen
Suci Imani Putri, Rahmalisa Suhartina, Nanda Ferdana
Published: 10 April 2025 by Universitas Muhammadiyah Yogyakarta in Medika Teknika Jurnal Teknik Elektromedik Indonesia
Alat pengontrol suhu untuk showcase reagen merupakan tujuan dari penelitian ini dilakukan,dimana pentingnya untuk menjaga kualitas dan stabilitas bahan kimia sangat diperlukan. Dengan menggunakan pendekatan fuzzy, sistem diharapkan dapat mengatasi ketidakpastian dan variabilitas suhu lingkungan, sehingga memungkinkan penyesuaian suhu yang lebih responsif dan akurat. Melalui pengujian yang dilakukan, hasil menunjukkan bahwa kontrol suhu berbasis logika fuzzy mampu mempertahankan suhu sesuai batas yang ditentukan yaitu 2 hingga 7 ℃. Alat kontrol suhu ini dibuat berbasis IoT dengan menggunakan sensor suhu dan sensor kelembaban dengan jenis sensor yaitu DS18B20 dan SHT 21 yang juga berperan sebagai masukan dalam perhitungan logika fuzzy. Hasil perhitungan logika fuzzy adalah waktu yang diperlukan untuk menyalakan dan mematikan kompresor jika suhu terlalu rendah atau terlalu tinggi. Dari hasil pengujian didapatkan jika sistem dapat menjaga suhu di rentang yang diinginkan dengan kesalahan 1,2 %. Hasil ini menunjukkan jika alat dapat meningkatkan efisiensi dan memperpanjang umur simpan reagen. Penelitian ini menggarisbawahi potensi penerapan logika fuzzy dalam pengelolaan suhu di lingkungan laboratorium.
Exploring Figurative Language in the Album ‘Luxury Disease’ by A Japanese Rock Band
The integration of songs into the learning process is a widely adopted practice aimed at enhancing students' creativity and critical thinking skills. However, the complexity of figurative language often poses a significant challenge for students. This research seeks to facilitate the learning process by analysing the figurative language used in a Japanese Rock Band, ONE OK ROCK's album "Luxury Disease" and identifying the types of figurative meanings present in its lyrics. A qualitative research method was employed, utilizing content analysis to extract reliable and valid insights from the texts within their context of use. The data source comprised 13 songs from the "Luxury Disease" album. Descriptive analyses were conducted to collect the data. The findings revealed the presence of eight types of figurative language in the album: 1) hyperbole, 2) irony, 3) metaphor, 4) personification, 5) simile, 6) litotes, 7) metonymy, and 8) oxymoron. Specifically, the analysis identified 7 instances of hyperbole, 1 of irony, 29 of metaphor, 7 of personification, 1 of simile, 1 of litotes, 1 of metonymy, and 5 of oxymoron. Additionally, the study uncovered four types of figurative meanings in the album: 1) affective meaning, 2) reflected meaning, 3) connotative meaning, and 4) social meaning. Figurative language in song lyrics is able to serve as a powerful tool for the Japanese Rock Band, ONE OK ROCK, to convey their messages.
Enhancing Collision Avoidance in Mobile Robots Using YOLOv5: A Lightweight Approach for Unstructured Environments
Saleel H. Abood, Hussein. M. H. Al-Khafaji, Mohanned M. H. Al-Khafaji
Published: 09 April 2025 by Universitas Muhammadiyah Yogyakarta in Journal of Robotics and Control (JRC)
Mobile robots play a crucial role in Industry 4.0, particularly in dynamic and unstructured environments where moving obstacles present significant challenges. This study applies the YOLOv5 object detection algorithm to enhance robotic perception and obstacle avoidance. The primary objective is to improve the accuracy and speed of object detection in real-time scenarios, ensuring safer and more efficient navigation for robots. The research contribution lies in developing a lightweight YOLOv5 model optimised for robotic applications, capable of detecting objects with high accuracy. The model was trained on a diverse dataset of 10,700 images, including static and dynamic objects such as chairs, fans, fire extinguishers, and humans, captured under various conditions and orientations. The dataset was divided into training (70%), validation (15%), and testing (15%) subsets. The proposed model achieved a mean average precision (mAP) of 0.73 at a confidence threshold of 0.374, demonstrating superior performance compared to the YOLOv4 model in terms of accuracy and processing speed. Notably, the model excelled in detecting static objects such as chairs, achieving a perfect recognition rate of 1.00, while encountering challenges with moving objects such as humans due to motion blur and rapid changes in body posture. These findings highlight the model’s potential for real-time applications in industrial and unstructured environments. In conclusion, this study demonstrates that the enhanced YOLOv5 model significantly improves object detection and collision avoidance capabilities in robotic systems.
