This research focuses on using the K-Nearest Neighbor (KNN) algorithm to model student satisfaction with campus services. The study finds that the quality of the dataset strongly influences the accuracy of the KNN classification results. Factors such as data cleanliness, balanced class distribution, and sufficient training data volume are highlighted as crucial for a successful model. The research also emphasizes the significance of proper feature selection in enhancing classification performance, suggesting that irrelevant features can introduce noise and decrease model accuracy. The model was evaluated using a dataset of 1032 data points and K=5, achieving an accuracy of 93.72%. While the model performed well for certain classes such as "Very Good" and "None", challenges were encountered in classifying the "Fair" and "Deficient" classes. The study concludes that KNN is effective in identifying student satisfaction patterns but highlights the need for improvements in accurately classifying these challenging classes. Ultimately, the research underscores the importance of data quality and feature selection in enhancing the performance of classification models for student satisfaction analysis.

The information system for making invitation letters for guest stars at TVRI North Sumatra is designed to increase efficiency in managing invitations and guest information. This research aims to identify various challenges faced during the implementation of practical work at TVRI, such as administrative problems and employee delays. Through the use of web-based technology, this application is expected to provide practical and relevant solutions, facilitating the process of making invitations, managing guest data, and confirming attendance. The system development method used includes requirements analysis, user interface design, and implementation using HTML, CSS, JavaScript, and PHP for database management. It is hoped that the results of this research will be able to make a significant contribution to the efficiency of TVRI's internal processes, as well as creating valuable practical experience in the field of information technology.
 

Thyroid gland disease is a disorder that affects the thyroid gland, which plays a vital role in regulating the body's metabolism. Common symptoms associated with thyroid disease include tremors, concentration difficulties, changes in the menstrual cycle, and neck enlargement. At RSUD Dr. RM. Djoelham, Binjai, many patients struggle to understand and diagnose this disease early due to a lack of information and specific symptoms. To address this issue, an information technology-based system is needed to help the public recognize thyroid disease symptoms and provide an early diagnosis. One effective approach for designing such a system is using Case Based Reasoning (CBR), a method based on experience that solves problems by finding similar cases from existing data. This system can process symptoms entered by users, such as dry skin, anxiety, neck enlargement, and others. Based on previous cases, the system will calculate the percentage probability of the disease, thereby providing a more accurate early diagnosis. For example, if the selected symptoms are dry skin, neck enlargement, and shortness of breath, the system can give a 42% probability of a thyroid gland disorder.

The Internet of Things (IoT)-based automatic bell system is designed to enhance the efficiency of bell operations in educational institutions by utilizing modern technology. This research aims to develop a system that can control the bell automatically according to the class schedule, while also enabling remote control via a mobile application using the Blynk platform. The system is built using an ESP8266 as the main microcontroller, a DFPlayer Mini module for audio playback, and an RTC DS1307 for time management. The results show that the system functions as expected, both in automatic mode based on the schedule and in manual mode through the mobile application. Testing and debugging demonstrated that integration with WiFi networks allows for flexible and effective bell control. For further development, it is recommended to add a power backup feature, web interface, and push notifications to improve system reliability and flexibility. This system provides an efficient and practical IoT solution for automating bell operations in educational environments.

The development of Internet of Things (IoT) technology provides opportunities to automate various devices, including height measuring devices. This research aims to design and build an automatic height measuring device that is integrated with IoT technology. This tool is designed to measure body height automatically and send the measurement data to a cloud-based platform, making it easier for users to monitor data in real-time via smart devices. This system uses an ultrasonic sensor to detect body height, a microcontroller as a data processor, and a Wi-Fi module to send data to the server. Test results show that this tool is capable of measuring body height with a good level of accuracy and provides convenience in storing and monitoring measurement data remotely. Thus, this IoT-based height measuring device can be an innovative solution for the need for height measurement that is more efficient and integrated with digital systems.

Personal Protective Equipment (PPE) is essential for worker safety, especially in oil palm harvesting activities. PT Langkat Nusantara Kepong faces major challenges related to work safety, with analysis showing that work accidents still occur frequently in the Padang Brahrang Plantation. This indicates the need for an in-depth evaluation of the use of Personal Protective Equipment (PPE) to reduce the risk of work accidents. By using the clustering method to group data based on the type of Personal Protective Equipment (PPE) used and aims to provide recommendations for optimizing the use of personal protective equipment based on risk management and reducing the incidence of work accidents. From testing the results of cluster 3, cluster 4 and cluster 5 it can be concluded that clustering with 5 clusters provides the most efficient and precise results, followed by 4 clusters, while 3 clusters provide greater variation within clusters, indicating that clustering with fewer clusters is less able to capture subtle differences in the data.

This study aims to analyze the correlation between students' academic achievements and the effectiveness of campus promotion in attracting new prospective students. Academic performance is often used as an indicator of educational quality in higher education and is frequently highlighted in promotional strategies. However, the direct relationship between student academic achievement and campus promotion success has not been fully understood. This research utilizes the Apriori method in data mining to discover hidden patterns related to student academic performance and campus promotion. The data analyzed include undergraduate students from the 2017–2019 cohorts at STMIK Kaputama Binjai, with variables such as study programs, GPA, school background, and major during high school. The results indicate a significant correlation between academic achievement and the effectiveness of campus promotion, which can be used by institutions to develop more targeted promotional strategies. By leveraging data mining, universities can more effectively identify potential student segments and enhance the overall image of the institution.

 PT Hong Xin Printing Equipment is encountering obstacles in improving operational efficiency and managing human resources in the digital age. Despite the use of a fingerprint attendance system, there are concerns about errors and reduced efficiency due to manual data processing. Additional challenges arise when recording attendance for employees on business trips and manually calculating leave. To tackle these issues, the company is planning to create a web-based payroll system that will automate payroll, attendance, and leave management. The implementation of this system is anticipated to enhance efficiency and accuracy, thereby allowing the company to maintain its competitiveness in the market.

To compete with other stores, store owners need to design various strategies, one of which is understanding customer purchase patterns. This article examines the Squeezer algorithm and compares the performance of the Apriori and FP-Growth algorithms in forming customer purchase association patterns that can be used as a reference for store owners in planning sales strategies. The data mining process was carried out using Association Rules and Clustering methods. A total of 1256 sales transaction data samples were analyzed to understand the association patterns produced by each method. Based on the test results with a minimum support of 0.2 and a confidence of 0.6, the Apriori algorithm produced 194 association rules with a total rule strength of 1.16. Meanwhile, the FP-Growth algorithm produced 52 association rules with the same total rule strength of 1.16. The Clustering Method resulted in 7 clusters with a similarity value of 0.06322. After comparison, the FP-Growth algorithm proved to have better performance in generating association rules compared to the Apriori algorithm.

The application of information technology has been widely used in medicine. This application provides convenience and smoothness in the medical world to detect symptoms of various diseases, especially malaria. Malaria is still included in the endemic diseases suffered by the community in Binjai City, the more malaria patients, of course the more doctors are needed/work to diagnose patients. Artificial intelligence is one solution and helps doctors in supporting decision making for certain diseases. Building a system to diagnose malaria using the Case-Based Reasoning (CBR) method offers various significant advantages. CBR utilizes experience and knowledge from previous cases, allowing the system to provide a more accurate diagnosis based on patterns and symptoms that have occurred in the past.