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Background: Trigeminal neuralgia (TN) is often associated with neurovascular compression in the trigeminal nerve root entry zone, necessitating the simultaneous visualisation of nerves and blood vessels. Fusion of 3D SPACE and 3D TOF MRA images provides an integrated neurovascular view; however, not all hospitals have fusion software. This study developed a MATLAB-based image fusion method as an alternative and evaluated its equivalence to hospital-based fusion software.Methods: This study employed a descriptive quantitative research design, conducted in November 2025 at Diponegoro National Hospital and Dr Kariadi General Hospital in Semarang. A total of 16 brain MRI datasets (3D SPACE and 3D TOF MRA) were fused using hospital software and the MATLAB fusion application (MATLAB R2025b GUI). The fusion results were assessed by specialist radiologists. Diagnostic performance metrics (sensitivity, specificity, NDP, NDN, accuracy) were calculated, and paired differences were tested using the McNemar test. Intra-observer reliability was assessed using percentage agreement and Cohen’s Kappa.Results: MATLAB fusion yielded a sensitivity of 90.91%, specificity of 80.00%, NDP of 90.91%, NDN of 80.00%, and accuracy of 87.50%; the McNemar test (p=1.000) indicated no significant difference. Intra-observer reliability was very good (percent agreement 94%; Kappa 0.875). These findings indicate that MATLAB-based fusion is equivalent to hospital software fusion on the study data and has the potential to serve as an alternative in facilities without fusion software, provided that registration standardisation and user training are in place.

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 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.

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 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.  

Student learning evaluation is a crucial aspect of measuring the level of understanding and achievement of competencies. Traditional assessments are often limited to quantitative scores, such as exams and assignments, which do not consider other aspects like creativity and deep comprehension. This study aims to develop and implement a Fuzzy Sugeno-based evaluation system for the Interactive Media Design course at the Vocational High School (SMK) level. This method integrates various factors, such as assignment and exam scores, using Fuzzy rules to produce a more comprehensive assessment.The research was conducted using MATLAB software for simulating and implementing the Fuzzy Inference System (FIS). The results demonstrated that the method is effective in providing recommendations and assisting teachers in categorizing student scores. Data analysis revealed that the majority of final student scores fall into the Highly Adequate category.The Fuzzy Sugeno method has proven to be flexible in handling uncertainties and offers a fair, transparent, and holistic solution for evaluations. Therefore, this method is recommended as a valuable tool to enhance the quality of student learning assessments in education.

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.

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.

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.

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.

This research aims to analyze the ability of FMIPA Mathematics students at Medan State University (Unimed) in solving problems of convergence and divergence of real number sequences, as well as evaluating the effectiveness of using MATLAB software in supporting understanding of these concepts. This research is based on the aim of overcoming students' difficulties in understanding the concepts of convergence and divergence. The research method used was qualitative, with a sample of 20 students selected using purposive sampling. Data was collected through written tests and observations of the use of MATLAB which were distributed via Google Form in solving questions. The results show that 55% of students are in the "very understanding" category, with MATLAB proving to be very effective in making problem solving easier and deepening understanding.

The solutions of limit problems is one of fundamental concepts in real analysis, applicable in various fields of mathematics and other sciences. However, determining limit can sometimes present challenges, particularly when the fuction in question is complex and difficult to solve manually. This study demostrates the use of MATLAB as a tool to assist in solving such problems numerically. The findings show that MATLAB is realible in calculating the limits of specific fuctions, offering accurate solutions more efficiently and quickly compared to manual methods.

Handling of dental disease problems requires that it be handled quickly and correctly, but not all teams of dental experts can carry out treatment quickly due to the lack of a team of dental experts who are in the workplace or hospital 24 hours a day.  Apart from that, the public also has very little knowledge of information about dental disease, so that to treat dental disease, people have to consult a dentist. To classify images of dental disease, feature extraction is needed. Feature extraction is taking characteristics of an object that can describe the image. One example of image feature extraction used is Red, Green, Blue (RGB). This feature extraction is often used to identify or classify an image. Dental image data that will be used in the classification process are tooth abrasion, anterior crosbite, cavities and gingivitis. K-Nears Neigbor is the simplest data mining algorithm.  The aim of this algorithm is to find the results of the closest distance classification for each object.  In determining the distance, the data is initially divided into two parts, namely training data and testing data. After receiving the training data and testing data, the distance from each testing data (Equilidence Distance) to the training data is calculated. The K-Nearest Neighbors method can be applied to classify dental disease based on images of types of dental disease using Matlab software. As a result of the image data training process, 40 image data were input, training results obtained were 100%.

This study analyzes the effect of atmospheric drag perturbation at two different magnetic index  (active and severe storm) conditions, on the motion of NOAA 15 weather satellite, the Celestial Mechanics software results were plotted with the assistance of the MATLAB program. From the results it is noticed that for short period of time the effect of atmospheric suppression disturbance at different geomagnetic activity had the same effect on the orbital elements and orbital motion components ,  for a long period there is a slight variation in the effect of the perturbation at two different geomagnetic activities, which can be seen in the orbit's inclination as well as its size and shape. The results demonstrated that motion components were significantly impacted by geomagnetic activity over relatively extended time scales.

Diseases in moringa leaves pose a serious threat to PT. Marada Kelor Sumba, a moringa processing company located in Kelurahan Temu, Kanatang District, East Sumba Regency. The main products from moringa processing are moringa powder and moringa tea bags, which are highly beneficial as supplementary food for infants and pregnant women. The company collaborates with the East Sumba Government for the prevention and handling of stunting. The primary objective of this study is to optimize image segmentation methods to identify diseases in moringa leaves with significant implications using MATLAB software. The image segmentation method using Thresholding is applied to separate diseased moringa leaves from healthy ones. Additionally, filters are applied to enhance disease image segmentation capabilities and manipulate images. This research focuses on the image segmentation stage using MATLAB. The benefits of this research are crucial in agriculture and technology development, including early segmentation of diseases in moringa leaves, improvement of disease image segmentation process efficiency, contribution to image-based agricultural technology, and enrichment of scientific knowledge and understanding of disease image segmentation in moringa plants. The segmentation accuracy obtained from testing all samples is 66,67%.  

MatLab Matrix Laboratory mathematics software is a platform with a programming language created with the aim of being a tool for complex calculations or simulating a system that you want to simulate. This literature study aims to find out whether MatLab mathematics software as a learning medium can improve students' computing abilities through a review of related literature studies. The research method used is a Systematic Literature Review with literature sources used from 2020-2024 and relevant to the research topic. Researchers analyzed thoroughly so that 9 main articles were taken for comprehensive analysis. The results of this SLR research show that MatLab mathematics software as a learning medium can improve students' computing abilities. 

To support green energy and reduce the effects of greenhouse gases, it is necessary to conceptualize and explore the use of renewable energy. In this research, the use of new renewable energy, especially photovoltaics (PV), will be applied to educational buildings, especially at ITS PKU Muhammadiyah Surakarta. This system is intended to reduce the use of conventional electrical energy. This system will be installed on the roofs of campus buildings with installation plans to meet the power requirement of 4.8 kW. This photovoltaic (PV) system will be modeled to determine the number of PV panels needed. Based on the simulation results, the number of panels required is 36 panels (2 series, 18 parallel) to produce 4.85kWh of power. Modeling and analysis of this system was carried out using RETScreen and MATLAB SIMULINK software.

Kruskal's algorithm in searching for minimum spanning trees can be applied to pipelines installed at the location of PDAM Tirtanadi Tuasan where problem identification starts with the water discharge reaching the consumer is small but the discharge flowing from the reservoir is sufficient, so this research is used as a solution to this problem and also as an optimization of the clean water distribution network in the Tirtanadi Regional Drinking Water Company (PDAM) of the Tuasan Branch with the intention of cutting the direction of the pipe flow to overcome this problem. The data obtained from PDAM Tirtanadi Tuasan Branch is in the form of a floor plan and formed into a weighted graph. After the data is obtained, then it is calculated manually that the length of the installed water pipe is 32,645 m with 86 vertices and 100 edges, then the pipe length is represented as a set of paths and the pipe connection ends are represented as nodes. The pipe length obtained using Kruskal's algorithm and inspection of iterations using the QM for windows software is 22,095 m, with 86 vertices and 85 edges. So, using the Kruskal Algorithm and the help of the QM for windows software, the difference in pipe length obtained is 10,610 m.

This study aims to compile a Lecture Schedule at the Mathematics Education Study Program, Faculty of Mathematics Natural and Earth Sciences Manado State University using the Welch-Powell Algorithm. The scheduling of lectures at the Mathematics Education Study Program, Faculty of Mathematics and Natural Sciences, Manado State University often collides because several lecturers have different numbers of courses and credit rights. This it can be seen that the method of preparing lectures used is still not effective and efficient. Preparation of a course schedule is an action taken so that time can be managed as effectively and efficiently as possible. The preparation of this lecture schedule will use a simple graph with input data, namely the number of lecturers, number of courses, credits for each course, days used in the week, time slots and number of classes used. After getting the input data, point coloring is carried out in coloring on the graph using the Welch-Powell Algorithm assisted by MATLAB software to get an effective and efficient class schedule. Lecture Schedule Arrangement in the Mathematics Education Study Program, Department of Mathematics, Faculty of Mathematics, Natural and Earth Sciences, Manado State University using the Welch-Powell Algorithm produces a schedule that does not collide, so that the Welch-Powell Algorithm can help prepare an effective and efficient class schedule .