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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. 

The Binjai District Court is a government agency that has the duty and authority to receive, examine and decide every case registered at the Binjai District Court. The Binjai District Court handles many gambling cases, but data management is still not fast and accurate because it still uses manual methods, so the agency needs to implement an application system. To solve this problem, you can use data mining applications, namely by utilizing existing data to dig up new information. One of the techniques in data mining is clustering. Clustering was chosen because it can group data according to the desired characteristics, in this research it means grouping gambling data in the Binjai City area. The clustering algorithm used is K-Means Clustering integrated into a desktop-based programming application. The conclusion obtained is that the system designed has proven successful in grouping gambling data into 3 clusters (groups). The process using MATLAB R2014a obtained results in group 1 which amounted to 276 data with a data centroid center (6.92; 2.41; 4.33) including the category of low levels of gambling, group 2 which amounted to 337 data with a data centroid center (7.56 ; 2.10; 14.48) is included in the category of moderate level of gambling and group 3 which amounts to 387 data with the centroid data (7.56; 2.10; 28.02) is included in the category of high level of gambling.    

Determining the provision of bonuses or rewards to employees plays a crucial role in maintaining the quality and motivation of employees. One effort that can be made to establish a bonus policy is by implementing the fuzzy Mamdani method as a systematic approach in determining employee bonuses, particularly at PT. ABC. By utilizing the fuzzy Mamdani method to process subjectivity in evaluations, it generates membership levels that allow for a more contextual employee assessment. Through the analysis of data involving various performance variables and bonus criteria, this research aims to present a fuzzy Mamdani system model that can support accurate and adaptive decision-making in determining employee bonuses. The results of this research and the evaluation of this model are expected to contribute significantly to the development of more effective bonus policies in the workplace of PT.

The health Social Security Administering Agency (BPJS) is a legal entity specifically assigned by the government to administer social security. Seen in the vision and mission of the Long Term Develompment Plan For the Health Sector 2005-2025, namely that the community is expacted to have the ability to access quality health services and also obtain heald insurance, namely that the community gets protection in meeting their basic health needs. Especially for the people of Binjai city, to see the level of satisfaction of peolbe using BPJS Health in the city of Binjai, it is necessary to build a clustering that can group the level of satisfaction in each domicile. Data Mining Grouping using the K-Menas Clustering algoritma metdhod, which is a process of processing quite large amounts of data using statistical methods, this producing a group of data. It is hoped that clustering can complete the grouping of satisfaction levels of BPJS user commuites in the city of Binjai. There are 400 data from correspondent responses from the community regarding the level of satisfaction in using BPJS Health in the city of Binjai. From the results of trials with 400 data carried out with MATLAB, it was found that group 3, cluster 1, had 244 data, cluster 2 had 69 data, cluster 3 had 87 data, group 4 cluster 1 has 78 data, cluster 2 68 data, cluster 3 has 109 data, group 5 cluster 1 has 63 data, cluster 2 has 63 data, cluster 3 has 145 data, cluster 4 has 24 data, cluster 5 has 100 data.