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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.

Wireless communication, in its infrastructure nature, faces many challenges such as fading, data coverage, and interference issues. Therefore, High-Fidelity or (Li-Fi) is utilized due to its ability to naturally provide high-density wireless data coverage in closure’s particularly helpful for application(s) in some areas while the radio interference conditions are concern. This article illustrates an advanced Li-Fi approach performing high-speed data transmission between two Personal Computers (PCs) utilizing the Arduino Nano-based technique. In the experimental phase, data is mainly used to be transmitted over red laser diode (630 nm) through (30 cm) in distance, a distance of 30 cm, achieving a high peak speed reach to about (512Bps). The proposed approach performance is computed by evaluating the most important and related metrics like Signal-to-Noise Ratio (SNR), Bit-Error-Rate (BER), and influence of throughput on input data over various light circumstance. The proposed approach mainly utilizes a keypad as a user input and two related detection models for both a solar cell and a photodetector in order to make a powerful comparison in terms of performance. the results showed that when the photodetector applies a higher-detection efficiency (via BER enhancement which reaches to 20% over solar-cell), the solar-cell clarify outstanding power and cost-activity. The mentioned findings are propped by elaborated statistical-analyses and MATLAB simulation to design, simulate and visualize the validate functionalities of the robustness and scalability properties of the proposed Li-Fi approach.

In image detection processes where there is a variation in brightness between pixels, techniques are required to obtain optimal and adaptable threshold values for these variations. Therefore, a comparison between the binary thresholding technique and the adaptive method of Otsu is made, in videos with dynamic and static background, weighing the response time of the algorithm, memory used, requirement of the central processing unit and hits in the detections, in the languages of Python and M (Matlab). The techniques in Python present better results in terms of response time and memory space; while, when using Matlab, the lowest percentage of machine requirement is presented. Also, the Otsu method improves the percentage of hits in 12.89 % and 11.3 % for videos with dynamic and static background, with respect to the binary thresholding technique.

The need for urban communities to consume vegetables is increasing. This has caused people to start cultivating vegetables using hydroponic techniques. However, due to their busy activities, they do not have enough time to monitor and control hydroponics, which must always be in ideal conditions. This paper designs and implements an Internet of Things-based monitoring system to help hydroponic owners monitor their hydroponics anywhere and anytime. The built system requires a monitoring device assembled using a NodeMCU ESP8266 microcontroller, a pH detection detector sensor, and a DHT22 temperature and humidity sensor. This system uses the Mamdani Fuzzy Logic algorithm to determine warnings to be displayed on the application interface when the water pH, temperature, and humidity are in certain conditions. The Mamdani Fuzzy Logic algorithm can interpret environmental data into a warning that humans can easily understand, even if they lack technical expertise. In addition to being able to help monitor, this system also allows owners to find out what elements need to be added or changed for their hydroponic place. Our evaluation results show that the defuzzification stage in the application has high accuracy, which is 99.92%, compared to Matlab’s results.

The Fast Fourier Transform (FFT) method for solving the 1-D heat diffusion equation offers an efficient approach for resolving partial differential equations (PDEs) with various time steps . FFT is used to transform the 1-D heat diffusion equation into the frequency domain and back to the time domain through inverse FFT. Using mathematical modeling with initial and Dirichlet boundary conditions, the numerical solutions produced by FFT are compared with analytical solutions. The accuracy of the method is validated using MAE and MSE calculated in Matlab. At several time intervals , the obtained MAE and MSE values indicate a good agreement between the numerical and analytical solutions, with very small errors. Numerical stability analysis confirms the reliability of the FFT method across various  The variation in time step  has a significant impact on the accuracy and stability of the solution. Smaller time steps improve accuracy and stability but require longer computation times. The optimal time step selected in this study is  Increasing the number of discretization points  also enhances accuracy but implies an increase in computational load and memory usage. The FFT method demonstrates good numerical consistency with increasing  

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.

This research studies the application of the nth order Runge-kutta method as a numerical solution to ordinary differential equations. This method was chosen because it is able to provide high accuracy and flexibility in various PDB problems. We implement the nth-order Runge-Kutta algorithm in MATLAB and compare with other numerical methods, such as Euler's method. The results show that the nth order Runge-Kutta method is able to produce more accurate solutions, especially for nonlinear systems. This research makes a significant contribution to the development of numerical solutions for PDB and shows the potential of MATLAB as an effective tool for numerical simulation. Sensitivity analyzes of parameters and time steps were also performed to understand the impact of variations on stability and convergence.

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%.

Land and Building Tax (PBB) is a type of area regulated by the government in determining the amount of tax for implementation and development as well as increasing the prosperity and well-being of the people. Based on taxpayer compliance data in Tanjung Keliling Plantation, the results of tests carried out using the Clustering algorithm can determine the variables of ownership area, hamlet name and payment level. Clusters 1,2,3 of 600 PBB taxpayer data, namely where cluster 1 has 166 data, can be grouped based on the Ownership Area of "500,001-600,000m2" with the Hamlet Name "Ujung Bangun" and the Payment Level "Quite Good". Cluster 2 consists of 196 data, which can be grouped based on ownership area "200,001-300,000m2" with the hamlet name "Karang Jati" and payment level "fairly good".  And Cluster 3 with a total of 238 data, can be grouped based on the Ownership Area "400,001-500,000m2" with the Hamlet Name "Mojosari" and the Payment Level "Quite Good".

Every company is required to plan the need for goods as effectively as possible in order to maximize profits. Bintang Makmur Building Shop is a building shop that provides building materials, especially cement. Cement is one of the basic materials for buildings. The need for cement has recently continued to increase due to the large number of developments, both housing projects and road construction. In addition to the increasing demand for cement, cement prices also experienced price volatility which tended to fluctuate. This is done so that there is no stockpiling or even a shortage of cement. With prices that tend to go up and down if there is too much stock, it will cause losses if there is a price decrease. Vice versa if there is a shortage of cement stock, it can cause disappointment to customers. To deal with the above, it is necessary to build a prediction system that can predict cement needs in prosperous shops. The system that will be built uses an Artificial Neural Network (Artificial Neural Network) which is part of the science of artificial intelligence which has been widely used to solve various kinds of problems related to prediction or forecasting by utilizing the Backpropagation Method. The system is designed with the MATLAB programming application. From the results of the research that has been carried out, it was found that the total demand for Andalas cement for January of the following year is 0.2532 or 2532, thus the predicted total demand for Andalas cement is 2532 sacks.

The advancement of information technology and globalization has transformed shopping behaviors, with social media becoming the primary platform for online shopping. This study aims to analyze the online shopping preferences of residents in Binjai City through social media using clustering methods, specifically the K-Means algorithm. Data were collected via a questionnaire targeting 523 respondents in Binjai City, focusing on variables such as gender, age, and the social media platforms used. Clustering methods are employed to group online shopping data into representative clusters, helping identify community preferences for specific social media platforms for shopping. Matlab is used to process the data and generate relevant insights into online shopping patterns, facilitating decision-making regarding the selection of the most suitable social media platform for transactions.The findings of this study are expected to provide valuable insights for both sellers and buyers in determining the most effective social media platforms for online shopping. Additionally, the results will be useful for residents of Binjai City to understand and choose the social media platforms that best meet their online shopping needs.      

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.

This research designs a system to classify apple leaf diseases using RGB (red, green and blue) color feature extraction. The essence of this research is to design a system to recognize and determine disease on apple leaves based on RGB color features using the Matlab 2024 application. The data in this research uses apple leaf images from kaggle.com, which are then cropped and adjusted to the image shape and precision in the leaf image. , Increasing the contrast of the cropped image and converting it to a grayscale image, Determining the threshold for binarization and converting the grayscale image to a binary image, Detection of green, yellow, and black/gray pixels based on RGB values ​​and calculating the proportion of each color, Detection of pixels scab by filtering out black/grey pixels that do not include green or yellow pixels Classification of leaves based on the proportion of detected colors. With the method that has been passed and uses apple leaf data, namely Healthy, Rust and Scab, each data contains 20 images with a total of 60 images and the level of accuracy is determined using the labeling method for each data and reaches the final result with an accuracy level of 86, 6667% which has a fairly accurate meaning  

Pencucian pakaian merupakan salah satu proses yang penting dalam kehidupan sehari-hari. Meskipun mesin cuci otomatis telah memberikan kemudahan dalam melakukan proses pencucian, penggunaan teknologi terbaru seperti kontrol logika fuzzy dapat meningkatkan kinerja mesin cuci pintar untuk mencapai hasil pencucian yang optimal. Penelitian ini bertujuan untuk mengimplementasikan kontrol logika fuzzy dengan metode Mamdani pada mesin cuci pintar guna meningkatkan efisiensi dan kualitas pencucian. Metode penelitian ini terdiri dari beberapa tahap, pertama, melakukan analisis terhadap variabel-variabel yang mempengaruhi proses pencucian, seperti beban pakaian, ketebalan pakaian, tingkat kekotoran, dan suhu air. Selanjutnya merancang sistem kontrol logika fuzzy Mamdani dengan menentukan fungsi keanggotaan untuk setiap variabel serta membuat aturan-aturan fuzzy untuk menghubungkan variabel input dan output dan yang terakhir melakukan evaluasi hasil pencucian terhadap variabel input yang telah ditentukan. Hasil penelitian menunjukkan bahwa implementasi kontrol logika fuzzy Mamdani pada mesin cuci pintar dapat meningkatkan kualitas pencucian yang signifikan dan adaptif dalam menentukan parameter-parameter pencucian dengan menghasilkan kualitas pencucian yang lebih bersih. Dengan demikian integrasi kontrol logika fuzzy Mamdani pada mesin cuci cerdas memiliki potensi meningkatkan kinerja pencucian secara keseluruhan. Kesimpulannya, penggunaan kontrol logika fuzzy Mamdani pada mesin cuci pintar merupakan langkah yang efektif untuk meningkatkan efisiensi dan efektivitas pencucian pakaian. Penelitian ini memberikan kontribusi dalam pengembangan teknologi mesin cuci pintar yang lebih adaptif dan ramah lingkungan, serta memberikan kontribusi penting dalam pengembangan teknologi dalam bidang kontrol logika fuzzy untuk aplikasi rumah tangga pintar lainnya.

The operation of electromedical equipment in health care centers is very dependent on the quality of electrical power. Disturbances such as voltage sags, also known as dip voltage, can disrupt the performance of sensitive electronic devices and potentially threaten the stability of a power distribution system. This study suggests the use of a Dynamic Voltage Restorer (DVR) equipped with feedforward control to reduce voltage sags. With long flashes and significant voltage drops, simulations using MATLAB/Simulink show that the feedforward control on the DVR is able to compensate for voltage sags correctly and on time while still maintaining a stable load voltage.

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.