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Toddlers are a vulnerable age group to various types of diseases due to their immune systems that are still developing. Limited utilization of medical record data and the lack of structured information regarding disease patterns in toddlers based on age and causative factors have resulted in suboptimal prevention and treatment efforts. Therefore, an approach is needed to systematically classify toddler disease data. This study aims to apply data mining techniques using the clustering method with the K-Means algorithm to group types of diseases in toddlers based on age and causative factors. The variables used in this study include toddler age, type of disease, and causative factors. The data were obtained from RSUD Dr. R. M. Djoelham Binjai and processed using MATLAB software with three clusters. The results show that the K-Means algorithm successfully groups toddler disease data into three clusters with different characteristics. The first cluster is dominated by toddlers aged 0–11 months with appendicitis caused by genetic factors. The second cluster is dominated by toddlers aged 1–3 years with diarrhea caused by environmental factors and has the largest number of members. Meanwhile, the third cluster is dominated by toddlers aged 0–11 months with sore throat caused by environmental factors. The clustering results indicate a relationship between toddler age, disease type, and causative factors, which can be used as supporting information for decision-making in the prevention and treatment of toddler diseases.

The braking system is a crucial component in a vehicle, where its performance is highly influenced by the wheel's rotational speed and the geometry of the brake pad. This study aims to analyze the impact of wheel rotational speed and variations in pad geometry on the temperature of the brake pad. The braking process generates heat due to friction, which, if not properly managed, can reduce braking performance and accelerate brake pad wear. The experiment was conducted at four levels of wheel rotational speed: 1000 RPM, 1500 RPM, 2000 RPM, and 2500 RPM. The testing system was designed using a braking system simulator equipped with a speed sensor (LM393) and a temperature sensor (K-type thermocouple), which were connected to an Arduino microcontroller and displayed in real-time through a Graphical User Interface (GUI) in MATLAB. The test results indicated that both the geometric shape of the brake pads and the wheel rotation speed significantly affected the resulting temperature. Standard brake pads produced the highest temperature at a speed of 2500 RPM, reaching 63.33°C. In contrast, brake pads with holes offered the best performance by maintaining a lower temperature of only 43.00°C. Furthermore, an increase in wheel rotation speed led to a noticeable rise in temperature; for standard pads, the temperature increased from 36.67°C at 1000 RPM to 63.33°C at 2500 RPM. This demonstrates that RPM is a major factor in heat generation due to friction. The MATLAB GUI effectively visualized the relationship between RPM and temperature, facilitating the analysis and evaluation of the data.

The old bridge structure, are subjects that must be monitored in bridge maintenance. Bridge detection supervision is very necessary to determine structural deformation caused by normal operations or environmental impacts such as temperature, humidity and heavy vehicle loads. Monitoring the structure as a whole also needs to be carried out after extreme conditions occur, such as an earthquake. Research on structural dynamics analysis of steel frame bridge conditions to determine the behavior of the structure. Vibration data is read using a wireless sensor network or accelerometer. The vibration mode signal obtained is processed using Fast Fourier Transform (FFT) analysis via MATLAB software, which will produce a graph of the relationship between the frequency domain and the time domain. Through this graph, the dynamic characteristics of the bridge structure can be analyzed, with several variations in the condition of the bridge structure.

The DC motor serves as the main drive of the vessel and is equipped with a rotary encoder that functions to regulate the movement of the sensor in measuring sediment levels. This rotary encoder is also used to monitor and represent the rotational speed of the DC motor. System testing was carried out by implementing a Fuzzy Logic Controller (FLC) algorithm to control the DC motor speed in moving the vessel, ensuring stable motion. This fuzzy logic–based approach is expected to improve accuracy and efficiency in sediment volume calculations, while also reducing potential errors that commonly occur in manual methods. Simulating motor speed control using the fuzzy logic algorithm in MATLAB, the best test results were achieved over several trials, with a rise time of 376.310 ms and an overshoot of 83.33%. Motor speed measurements using both a tachometer and Arduino produced consistent results, with an average relative error of 0.18%.

Anxiety disorders are common mental health problems in society, often unrecognized by the sufferer. Identifying the type of anxiety disorder and its influencing factors is crucial for proper treatment. This research aims to apply the K-Nearest Neighbor (K-NN) method in identifying types of anxiety disorders based on influencing factors, focusing on patient data from Sylvani Hospital, Binjai. The K-NN method was chosen because of its ability to classify based on data proximity. This study used medical record data of patients with anxiety disorders, which were processed using MATLAB and Microsoft Excel software. The results show that the K-NN method is effective in identifying types of anxiety disorders, with a high level of accuracy, especially in the identification of Panic Disorder (K05) and Social Anxiety Disorder (K03). The use of MATLAB simplified the identification process by automating results, while data processing in Excel improved classification accuracy. This study concludes that the K-NN method can be an effective alternative in identifying anxiety disorder types based on the factors that influence them. It is recommended for future research to involve more variables and mental health experts for a more comprehensive validation of the results.

The use of buck converters as DC step-down voltage regulators is increasingly important in various power electronics applications. However, the quality of the output voltage is often disturbed by the presence of ripple, which is influenced by variations in the duty cycle. This study aims to analyze the effect of duty cycle variations on the output voltage and ripple of a buck converter using MATLAB/Simulink simulation. The method used is quantitative simulation by varying the duty cycle from 10% to 90% in a buck converter circuit with fixed parameters: input voltage 30 V, switching frequency 40 kHz, inductor 176.25 μH, and capacitor 44.33 μF. The simulation results show that the output voltage is proportional to the duty cycle, increasing from 3.245 V at D=10% to 26.82 V at D=90%. The highest ripple occurred at D=40% with a value of 0.07 V, while the lowest ripple was at D=50% with a value of 0.0003 V. These findings indicate the existence of an optimal operating point where the system works most stably. This study provides practical guidance in designing efficient and stable buck converters for applications such as battery charging and renewable energy systems.

This study discusses the optimization of the allocation of productive waqf funds using MATLAB through a case study on the Waqf Board of the Islamic University of Indonesia (UII) Yogyakarta. Productive waqf has an important role in supporting socio-economic empowerment, but its management often faces challenges in determining the efficient distribution of funds. The purpose of this research is to develop a mathematical model to optimize the allocation of waqf funds in various sectors such as education, health, micro enterprises, and social programs. The research method uses a quantitative approach with optimization techniques implemented in MATLAB to simulate various fund distribution scenarios. The results of the study show that the optimization model is able to provide a more effective distribution of waqf funds compared to the actual allocation pattern, thereby increasing efficiency and impact on community welfare. Thus, the use of MATLAB has been proven to help waqf institutions in formulating accurate data-based decisions and mathematical models. In addition, this study emphasizes that the application of modern computing technology can strengthen the role of waqf as an instrument of sustainable social development. The implications of this study show that the integration of mathematical modeling with modern computing tools can be an important strategy for Islamic philanthropic institutions in maximizing the potential of productive waqf, especially in Yogyakarta. This research also contributes to the development of Islamic economic literature, offers practical recommendations for waqf managers, and shows the application of MATLAB as a tool in economic decision-making. With this approach, productive waqf is expected to provide wider and sustainable benefits for the community, as well as be an inspiration for other waqf institutions in Indonesia to adopt a similar strategy.

Stable temperature regulation is essential in various industries to maintain product quality and energy efficiency. This study analyzed the water temperature regulation system using PID control through MATLAB simulation and the actual implementation using Thermocontrol and PT100 sensors. In the simulation, the PID control parameters used were Kp = 3.0, Ki = 240, and Kd = 60, while the tuning results in the actual implementation were Kp = 3.0, Ki = 0.5, and Kd = 1.2. The simulation results showed that the system could reach the setpoint faster (±330 seconds), but with a high overshoot (±20.8°C). In contrast, the actual implementation shows more stable performance, with a much lower overshoot (±0.8°C) and a time to the setpoint of about ±345 seconds. These differences in performance can be influenced by environmental factors and the physical characteristics of real systems that are not fully reflected in the simulation. Although the simulation provides faster results, the actual implementation is more adaptive to changing conditions and better able to maintain temperature stability. This research can provide important insights into the development of more effective and reliable temperature control systems for industrial applications, as well as demonstrate the importance of proper tuning in PID control systems to achieve optimal performance.

Magnetic Resonance Cholangio Pancreatography (MRCP) is an important non-invasive imaging technique for the diagnosis of abnormalities in the biliary and pancreatic systems, including pancreatic mass and colletiasis. The use of an additional sequence of Diffusion Weighted Imaging (DWI) with b-value variations and image segmentation is thought to improve the accuracy of mass limit measurements on MRCP checks. This study aims to analyze the effect of b-value variation and image segmentation on the additional sequence of DWI in the MRCP examination of the accuracy of the mass limit measurement. The research used quantitative methods with MRCP image data capture equipped with a DWI sequence with b-value variations, using the matlab method. Image segmentation is performed to identify mass boundaries. Measurement accuracy is analyzed and compared between the variation in b-value and the segmentation techniques used. Research results show that variation of b-value 800 and image segmentation in additional DWI sequences have a significant effect on the improvement of accuracy of mass limit measurement on MRCP examinations. The b-value 800 variation is more optimal than the b-value 50 and the appropriate segmentation method can clarify the mass limit so that it supports a more accurate diagnosis. Sequence variations in b-value and image segmentation in the additional DWI sequences in MRCP examinations play an important role in improving the accuracy of mass limit measurements, which can aid in the diagnosis and management of diseases especially in lesion cases.

Nutritional issues among toddlers continue to be a pressing public health challenge in Indonesia, including in Kelurahan Pekan Kuala, where although anthropometric data have been systematically collected through the e-PPGBM application, they have not been thoroughly explored in terms of clustering patterns that may provide deeper insights. This study seeks to classify toddler nutritional status by applying the K-Means Clustering method to anthropometric indicators such as age, weight, height, and weight-to-height index. A dataset consisting of 648 entries recorded between January and March 2025 was processed using MATLAB R2014b with cluster variations set at 5, 7, and 9. The analysis revealed that the majority of toddlers were categorized as having good nutritional status, while a portion of the sample was identified as undernourished and some at risk of overnutrition, indicating the diverse nutritional challenges faced by this community. Furthermore, testing the variance across cluster configurations demonstrated that the 9-cluster model yielded the lowest variance score of 0.20, thereby representing the most optimal solution since it produced more homogeneous, balanced, and stable clusters compared to other configurations. These outcomes highlight the importance of data-driven approaches in public health planning, as the clustering results not only provide a clearer picture of nutritional distribution among toddlers but also serve as a foundation for more evidence-based and targeted intervention strategies. By offering a more granular understanding of nutritional variations, this research is expected to support local health authorities in developing customized nutrition programs, allocating resources more effectively, and ultimately improving child health outcomes in Kelurahan Pekan Kuala and similar communities across Indonesia, where malnutrition and overnutrition risks continue to coexist.

The rise in motor vehicle theft cases in various regions indicates the weakness of the security systems implemented by most users. Systems such as manual locks and alarms often fail to prevent crime, either because they are easily hacked conventionally or due to user negligence in their operation. In today's technological era, a system is needed that is not only secure, but also intelligent and practical. One promising solution is the implementation of a facial recognition-based security system. This study aims to design and test a vehicle security simulation system using facial recognition technology integrated with Arduino Uno and MATLAB. This system utilizes a laptop camera to capture the user's facial image, then performs a detection and verification process using the FaceNet algorithm. If the face is recognized and verified with data stored in the database, the Arduino will activate the actuator components in the form of a DC motor to simulate starting the engine, and a servo motor to simulate opening the vehicle door. This study uses a quantitative experimental approach to analyze the effect of variations in distance (30, 40, and 50 cm) and lighting brightness levels (10–20, 21–30, and 31–40 lux) on the system's response time. A total of 27 combinations of conditions were tested, and the data obtained were analyzed using Microsoft Excel and ANOVA tests in Minitab software. The results of the analysis showed that the optimal response time was obtained at a distance of 40 cm with a medium level of illumination (21–30 lux). In addition, both distance, brightness, and the interaction between the two factors were shown to have a significant effect on the system's response time (P-Value < 0.05). These findings indicate that the system is quite sensitive to environmental changes, so further testing is highly recommended, especially to measure the actual delay, the detection error rate, and the development of a more robust face detection algorithm so that the system can be used reliably in various lighting conditions and face capture angles in the real world.

Pulmonary tuberculosis remains a pressing public health problem, particularly in the work area of the Duduk Health Center (UPT Puskesmas). Effective management of this disease requires a thorough understanding of the characteristics of the causes of pulmonary TB in patients. This study aims to classify pulmonary TB cases based on the main causes such as diabetes mellitus, irritant factors, pleural effusion, and family environmental conditions. The research method used is a clustering technique with the K-Means algorithm. The data used are data on pulmonary TB patients in 2020–2025 with variables of age, gender, and causative factors collected from medical records. The analysis process was carried out using MATLAB R2014b software. The clustering model was carried out in 3, 4, and 5 clusters to compare the level of segmentation efficiency. Based on the calculation results, the model with 5 clusters showed the lowest cluster variance value of 0.4889 compared to the 3-cluster model (0.7333) and 4-cluster models (0.6151), which indicates that the division into 5 clusters produces the most compact and representative data group. Each cluster shows a different combination of characteristics of pulmonary TB patients, for example: (1) elderly male patients with comorbid diabetes; (2) adolescent females with the negative influence of environmental factors; (3) adult males exposed to irritants; (4) patients with pleural effusion; and (5) groups with multiple factors. The results of this study can provide strategic input for the Finished Community Health Center UPT in formulating more targeted and targeted intervention policies in order to prevent, control, and handle pulmonary tuberculosis cases in a sustainable and effective manner.  

PT. PLN (Persero) UP3 Binjai faces challenges in handling electricity usage violations that increase every year. Lack of utilization of data violations that can be utilized to produce useful information in supporting strategic decision making by PLN, especially in the implementation of Electricity Usage Control (P2TL) activities. This study aims to identify customer violation patterns based on rayon, power, and type of violation with data mining methods using the K-Means Clustering algorithm. The results of the study show that the 3rd cluster represents the most violation-prone area, namely in the West Binjai Rayon, with a power of 450 VA and the most frequent type of P2 violation. The results of the study show that the K-Means algorithm with the Elbow method is able to systematically group data violations based on certain characteristics. The results of this study can provide recommendations to PLN UP3 Binjai to improve the effectiveness of monitoring and enforcement strategies through a more targeted approach.  

In many wireless sensor network (WSN) applications, nodes are randomly deployed and self-organize into a wireless network to perform tasks. In practice, recharging the batteries of network nodes after deployment is often difficult. Network nodes often operate autonomously, so the main focus is on increasing the node lifetime. Data redundancy is another limitation that makes nodes inefficient. In most cases, densely deployed nodes in a monitoring area will have redundant data from neighboring nodes. Therefore, we propose a clustering technique to select the Cluster Head (CH) node in small-scale WSNs. Since transmission consumes more energy than data collection, this protocol enables reactive routing, where transmission occurs only when a certain threshold is reached. In addition, based on their heterogeneous energy levels, nodes can be grouped into three categories: Normal, Intermediate, and Advanced. Simulation results in MATLAB/Simulink show that, after approximately 3000 rounds, the proposed method successfully transmitted about 3.1 × 104 packets to the base station, compared to 2.3 × 104 packets for the Low Energy Adaptive Clustering Hierarchy (LEACH) protocol. In addition, the time when the last node died was approximately 3,500 rounds, whereas the LEACH protocol only maintained about 1,500 rounds. The results have shown the effectiveness of this technique in reducing the dead node rate and increasing packet transmission efficiency.

Solving electrical circuits often requires finding solutions to systems of linear equations, which are derived from fundamental laws like The laws of Kirchhoff and Ohm's law. In this study, two widely known numerical methods are used to solve these systems, Gaussian elimination and matrix inversion. Both methods are applied to the linear equations that represent the electrical circuit network. MATLAB is used to model and solve the circuits with these techniques, making the calculations more efficient and accurate. By employing both methods, we can solve systems of equations for various circuit configurations, determining the voltage and current values for each component. The results demonstrate that both methods are reliable and fast, providing valuable insights for electrical engineering and circuit analysis.

The processing of natural fibers as raw materials for paper has grown over the past few years. However, the use of composite paper as a printing substrate has several drawbacks. One of them is the low color reproduction quality of printed images on paper made from natural fibers, caused by the insufficient whiteness of the paper. This study aims to investigate the effect of titanium dioxide (TiO₂) addition on the color reproduction quality of composite paper based on sugarcane bagasse fiber. TiO₂ was varied at concentrations of 0%, 10%, and 20%. Printing process was carried out using an inkjet printer with standard CMYK and RGB color patches. After that, the printed results were measured using colorimeter with D65 illuminant. Color distribution analysis was processed using MATLAB software. The results showed that the addition of TiO₂ increased the whiteness of the paper, as indicated by the higher L* values. The color gamut visualization demonstrated that the gamut area expanded as the TiO₂ content increased. This result shows that the addition of TiO₂ affects the color reproduction quality of composite paper.

Automatic object detection is one of the crucial aspects in the field of digital image processing that plays a vital role in various modern applications, ranging from security systems to pattern recognition in medical and industrial fields. This study aims to implement an automatic object detection method with a digital image representation-based approach using MATLAB software. The main focus is directed at the pixel-based image processing process, where each image element is processed to extract relevant visual information. In this study, stages are carried out starting from image acquisition, color conversion, image quality enhancement, threshold-based segmentation, to extraction of targeted object features. Digital images are analyzed through transformation into grayscale and binary forms to facilitate the detection process. The use of MATLAB provides flexibility in numerical and visual data processing, and supports various efficient image processing libraries.

Control systems with time delays introduce system stability problems because time delays cause exponential effects to the system response. Conventional root-locus methods cannot be used directly on systems with delays due to irrational mathematical forms. This study analyzes the shifting effect of time delay on the stability of linear control systems by using the first-order Padé approach to enable the application of the root-locus method. The system model used is a second-order linear system with a transfer function of , and is analyzed under conditions without delay and with delays of 0.5, 1, and 1.5 seconds. Simulations were performed using MATLAB software. The results show that the addition of delay causes a right pole shift of the imaginary axis, reduces the stability margin of the system, and results in a more oscillative response as well as a longer time for the system to stabilize. The first-order Padé approach is shown to be effective in facilitating the visual analysis of stability in time-delayed systems. The findings make a practical contribution in adapting classical analysis techniques to the needs of modern control systems and can be widely applied in the development of network-based control systems, industrial automation, and real-time control.

This study investigates the solutions of electrical current in linear circuit systems using two numerical methods implemented in MATLAB: the Matrix Inverse Method and the Gauss-Jordan Elimination Method. The objective is to analyze the effectiveness, accuracy, and computational efficiency of both techniques in solving systems of linear equations derived from Kirchhoff's laws. Several circuit models with varying levels of complexity are tested to compare results obtained from each method. The findings indicate that both methods yield consistent solutions, although differences in computational steps and processing time are observed. This research highlights the practicality of MATLAB as a powerful tool for electrical circuit analysis and provides insights into the selection of appropriate numerical methods for solving engineering problems.

Computational thinking skills are one of the important competencies in the world of education in the 21st century, especially in the fields of science, technology, engineering, and mathematics (STEM). GNU Octave as an open source software similar to MATLAB offers a numeric programming environment that can be used to train and develop students' computational thinking skills. Researchers use a literature study method that aims to see the extent to which GNU Octave can be used in learning to improve students' computational thinking skills. The literature sources used come from national and international journal articles, conference proceedings, and other trusted sources over the past 10 years. The results of the review show that GNU Octave is effective as a learning tool for numeric programming, problem solving, and mathematical modeling, and has a positive contribution to honing students' algorithmic and analytical thinking skills.