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.

The development of information technology that is currently developing serves to facilitate, accelerate, benefit and provide other alternatives for people who have businesses and have a big influence in the future. One of the things that is very influential is the sale of MSMEs. MSMEs are productive businesses owned by individuals or business entities that have met the criteria as micro businesses that have an important role in the economy because they provide employment, encourage local economic growth, and create innovation. MSMEs still face challenges such as limited access to financing, digital readiness, and marketing access that hinder the development of MSMEs. Therefore, it is necessary to take action to predict MSME sales in Binjai City using the backpropagation method so that later it can create new innovations and encourage community economic growth. Based on the process carried out using the backpropagation method, it can be seen that the value obtained has reached more than the predetermined target with a target value (t) of 0.26, learningrate 0.2, maximum epoch 10000 target error 0.01.

This research aims to identify the patterns of students' interests and talents at Esa Prakarsa Junior High School and apply the Rough Set method in data analysis to determine the most appropriate theme of the Pancasila Student Profile Strengthening Project (P5). The study involved data collection from 178 students through a questionnaire designed to explore their interests and talents. The results of the analysis showed a significant correlation between the patterns of interest and talents of students with the selection of the P5 theme. The Rough Set method successfully identified relevant rules, such as students who have an interest in the field of art are more suitable for the theme of sustainable lifestyle, while talented students in the field of sports are more in line with the theme of Build the Soul and Body. The use of Rosetta software in data analysis provides recommendations for interesting and relevant P5 themes, supporting the achievement of national education goals in forming a young generation with character and competence. This research is expected to provide guidance for schools in developing P5 themes that are more relevant and interesting for students, as well as improving learning outcomes based on their interest and talent characteristics.

Cases of biological food poisoning can be caused by several causative factors, one of which is a food processing site that does not meet health requirements. According to the BPOM report (2016) cases of food poisoning in Indonesia in 2016 reached 1,068 cases. In 2016, 60 extraordinary events (KLB) of food poisoning were reported by 31 BB/BPOM throughout Indonesia. From the many cases of food poisoning that occur, it is necessary to take action in prevention by processing data on existing cases of poisoning to follow up on existing problems to reduce the number of cases of food poisoning by using a system on a computer so that the managed data can be processed quickly to obtain further information. Therefore the author wants to use a system with the clustering method to assist in processing data on biological poisoning cases grouping objects based on the characteristics of each object. Based on the research conducted, it can be seen that in cluster 2 in the dasta group of biological poisoning cases there are 11 data with centroid point age (x) 2, namely 12-16 years, centroid point on the type of poisoning (y) 6.36, namely sandwiches, and centroid point on the causative factor (z) 2.9, namely Gram-negative rod-shaped bacteria which are usually found in the intestines of humans and warm-blooded animals.

Education is an important place for students to develop their potential based on their intelligence. Multiple intelligences offer an approach that considers the various potentials of students in the learning process. SD Islamiyah, as an educational institution with a vision to produce intelligent and creative generations, faces challenges in delivering learning that meets the needs of students. To address this issue, a system is needed that can analyze and group students based on their intelligence levels using the clustering method. This study is inspired by the application of data mining in the educational context, particularly in adapting the clustering method as applied in other related research. Previous research has demonstrated the success of the clustering method in accurately grouping data, as seen in studies related to flood warnings and cesarean operations. By applying a similar approach, this research aims to assist SD Islamiyah in identifying and grouping students based on their potential, thereby facilitating a more effective learning process tailored to the individual needs of students. The results of this study are expected to contribute positively to improving the quality of education at SD Islamiyah and provide a foundation for the development of more advanced decision support systems in the future

A blood donor is someone who wants to donate their own blood to people in need without any element of coercion from anyone. Predicting the number of blood donors is very important and necessary to find out the number of blood donors in Langkat Regency in 2023-2024, and the prediction results can help PMI Langkat Regency in increasing the number of blood donors. The method applied in this prediction system is Linear Regression, where this analysis determines whether or not each variable is in accordance with the prediction results being tested and estimates that the value of the variable will increase or decrease each month. The prediction system is carried out using the RapidMiner application because this application is very appropriate for producing information output in the form of prediction results for the coming year. The prediction results obtained by testing using the Linear Regression method show increases and decreases every month. There are 11 months where there has been an increase and decrease in the predicted results and are in accordance with the data in 2023, then there is 1 month which has decreased in the predicted results and does not match the data in 2023. From the overall data results, it can be calculated the number of blood donors in Langkat Regency in 2023 and every month. Measuring the error level of prediction results using RMSE, the resulting accuracy level was 83.574%.

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.

Technological advancements in the era of globalization demand improvements in the quality of academic services and educational facilities in institutions. STMIK Kaputama is committed to creating a conducive academic environment by providing optimal facilities. This study aims to determine student satisfaction with campus facilities using the K-Means Clustering method. Data were obtained from recapitulated survey reports and questionnaires filled out by students in 2024. The K-Means Clustering method was chosen for its ability to group students based on their similar preferences for campus facilities. The results show that, in general, students are fairly satisfied, though their preferences for specific facilities vary. These findings can be used to make recommendations for the improvement and development of campus facilities, help STMIK Kaputama allocate resources more efficiently, and plan strategies to enhance the quality of facilities to meet student expectations.

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.