In today's digital era, technology plays a crucial role in supporting innovative, interactive, and engaging learning processes. Education is no longer limited to the use of conventional textbooks and whiteboards, but has been transformed through the integration of various digital media. In this context, digital literacy has become a key competency that students must possess. Digital literacy encompasses not only the ability to access and understand information from various digital sources but also involves critical thinking and collaborative skills, as well as the wise, ethical, and responsible use of technology. Studies show that the appropriate and targeted use of digital media can increase students' interest in reading materials and other learning activities. Interactive and visually appealing digital media can stimulate students' curiosity, enrich the learning experience, and increase their motivation. Furthermore, the use of digital technology also provides opportunities to develop creativity, communication skills, and problem-solving skills. Technology-based learning enables more effective learning differentiation, allowing students to learn at their own pace and learning style. This is highly relevant in the context of 21st-century education, which demands information literacy, technology literacy, and media literacy. As learning facilitators, teachers are required not only to be proficient in using technology but also to design learning that integrates curriculum content with available digital potential. Therefore, optimal use of digital technology can be a strategic solution in creating a generation that is digitally literate, creative, innovative, and able to adapt to increasingly dynamic and complex developments. To achieve this goal, collaboration between schools, the government, and parents is essential in providing adequate access to and training in sustainable educational technology.

Background: Anxiety before childbirth is a psychological disorder often experienced by pregnant women, especially in the third trimester of pregnancy. Fear of pain, worry about the condition of the fetus, and uncertainty about the delivery process are the main triggering factors. If not handled properly, this anxiety can have serious consequences, such as increased stress hormones (cortisol), sleep disorders, high blood pressure, and even labor complications. Therefore, interventions are needed that not only focus on physical aspects, but also support mental and emotional balance. Objective: The purpose of this activity is to provide education and practical training to pregnant women on anxiety management before childbirth through the application of complementary therapies, and to evaluate the effectiveness of this approach in increasing the readiness and calmness of participants. Method: The activity was carried out at PMB Lismarini, Tulungagung, involving 20 pregnant women in their third trimester. Education was carried out interactively using visual media and group discussions. Next, participants participated in the practice of three types of complementary therapies: (1) deep breathing relaxation with diaphragmatic breathing techniques, (2) aromatherapy using lavender as a relaxation agent, and (3) classical music with a slow rhythm played during the session. Results: This activity showed positive results. Eighty-five percent of participants experienced an increase in knowledge scores after the educational session, and 60% showed a significant decrease in anxiety levels based on questionnaire results. Most participants also reported feeling calmer, more prepared, and more confident in facing childbirth. The satisfaction rate with the activity reached 90%, indicating that the methods implemented were well-received by participants. Conclusion: Complementary therapy combined with education is effective in helping pregnant women manage anxiety before childbirth. This activity emphasizes the importance of a holistic approach to antenatal care.

Personnel Leave Application Information System Application: The Personnel Leave Information System at the Batu Bara Regency Regional General Hospital is an application built for the web-based leave application process. This research was conducted on the Staff Leave Information System at the Regional General Hospital of Batu Bara Regency using research methods, namely the Observation method, Library Research, Field Research, needs analysis, system design, and system implementation. Use Case Diagram, Activity Diagram, Data Flow Diagram ( DFD), Sequence Diagram, Class Diagram. This application was built using software, namely the PHP programming language, HTML, CodeIgniter, Composer database used by MySQL. The application built operates on the Windows Operating System. The WEB-based Application for the Information System for Applying for Employee Leave at the Batu Bara Regency Regional General Hospital has helped the Batu Bara Regency Regional General Hospital in registering and informing employee leave applications quickly and accurately, and can also provide information to employees to find out whether the leave process has been approved or not. approved.
 

Smart Door Lock is a development of a conventional manual home door lock or padlock. This study aims to create alternative technology at home by utilizing available lock technology. This study uses an Arduino Uno Microcontroller as the brain of the solenoid and as a power supply for the fingerprint scanner, and an esp 32 microcontroller as a tool for sending fingerprint data to firebase. The fingerprint scanner is run using the Arduino Ide to add fingerprints. Arduino programming is done using the Arduino IDE. The results of the study are described in the form of designing an automatic home door lock using a fingerprint scanner and Arduino as a solenoid control. The fingerprint data results will be visible in firebase

Andri Andriana Syatfle, Nim 148320719008 "DEVELOPMENT OF AN ELECTRONIC BOOK (E-BOOK) FOR EMERGENCY FIRST AID (PPGD) CLIMBING INJURY RISKS FOR UKM PA UNIMUDA SORONG EARTHQUAKE". Information Technology Education Study Program, Faculty of Exact Education, Muhammadiyah University of Education (UNIMUDA) Sorong. This research aims to develop learning media in the form of e-books which can be used as a varied learning media on Emergency First Aid (PPGD) material in the UNIMUDA Sorong Earthquake PA UKM. With this e-book learning media, Mapala Bumi members are more interested and active in learning. The research carried out by the author used the Addie model research and development (R&D) method in seven stages, namely potential and problems, data collection, product design, design validation, product design revision, product testing and product revision. Learning media in the form of e-books, the material that has been developed has been tested through a media validation questionnaire with an average assessment of 81.25% categorized as very feasible, the first material validation questionnaire with an average assessment of 97.5% categorized as very feasible, and a questionnaire second material validation with an average assessment of 75%, small group trials with an average assessment of 86.3% which were declared very feasible, and large group trials namely: large group trials which obtained an average assessment of 94 .5% is categorized as very feasible. This shows that the media is suitable for use in the learning process.

Hypertension is a disease that occurs to arteries that causes the supply of oxygen and nutrition that the body needs to be blocked. Hypertension is often called a silent killer, because it is a kind of disease that is very harmful but comes without awareness to its victim. People with hypertension in average are up to 40 years old and it happened all of his after life . In common hypertension caused by heredity, unhealthy lifestyle, and triggered by the more salty consumption, alcohol and stress. An expert system could be the solution to solve the problem because this system works just like an expert and was created by the naïve Bayes method with the rules and basic system that are the same just like the hyperantion desease. Through this application, users can consult with this system just like usually people consult with the expert to diagnose the sign that happened to the user and find the solution of what happened to themselves.

This research analyzes the factors that influence the success of e-commerce applications in increasing sales in the digital era. With the development of technology and changes in consumer behavior, e-commerce has become one of the main platforms for businesses to reach customers more widely. The main factors analyzed include user experience, transaction security, product quality, customer service, digital marketing strategy and innovation. The research results show that the combination of these factors significantly influences purchasing decisions and customer loyalty. Good user experience, guaranteed data security, and effective marketing strategies are proven to increase sales and consumer trust. This study provides important insights for business people in maximizing the potential for implementing e-commerce in the competitive digital era.

In the rapidly evolving digital era, the use of mobile applications has become a fundamental necessity across various sectors, including the construction industry. PT Home Center Indonesia, which specializes in providing and installing furniture for building projects, faces challenges in the QC Pass Land recording process, which is currently done manually. This practice poses risks of data loss, miscommunication with vendors, human errors, and data invalidity, ultimately disrupting work efficiency. This study aims to develop a mobile application to replace the manual recording system in the QC Pass Land process for the Sky House BSD project. The application is designed to enhance the efficiency, accuracy, and reliability of data recording while minimizing the risk of information loss through the implementation of a structured digital system. Additionally, the application is equipped with a feature that allows the inclusion of photo evidence, thereby improving data validity. The results of developing this mobile application are expected to accelerate the recording and furniture installation process, reduce the time required for data verification, and provide a solution that can be implemented in future projects. Through this practical work, the implementation of the application is anticipated to run effectively and contribute significantly to improving the QC Pass Land process at PT Home Center Indonesia.

The rapid advancement of information and communication technology has transformed and opened business perspectives and operations, particularly in UMKM the photography industry. As the demand for online photography services continues to rise, it is crucial for service providers to develop efficient information systems to manage photo sales effectively. The research titled "Development of the POVSHOT Mobile Application for Online Photo Sales Using Flutter and MySQL" aims to facilitate online photo sales through the implementation of Flutter technology and a MySQL database. The findings indicate that the "POVSHOT" application successfully integrates features such as photo uploading, payment management, and user administration within a user-friendly interface. This study suggests that the use of mobile applications can enhance security in the photo sales process and provide a better experience for users. The implications of this research highlight that photography service providers can leverage mobile technology to expand their market reach and improve customer satisfaction through a more integrated and responsive system.

This research focuses on developing a cayenne pepper quality detection system using the YOLO (You Only Look Once) V5 algorithm. The system is designed to address the limitations of manual post-harvest sorting by classifying cayenne peppers into three categories: “good” (ripe), “bad” (rotten or dry), and “raw” (green), based on their visual characteristics. A dataset consisting of 565 images was manually collected, labeled using Roboflow, and pre-processed through resizing and orientation standardization. Model training was conducted over 150 epochs, achieving high detection performance with a mean average precision (mAP) of 99.5%, precision of 99.6%, and recall of 99.9%. Real-time testing demonstrated the system’s capability to detect and classify cayenne peppers with exceptional accuracy. This research is expected to enhance the efficiency and accuracy of the cayenne pepper sorting process, while paving the way for the application of YOLO-based systems to other agricultural commodities. Further research is recommended to expand the dataset and optimize model parameters for improved system performance.

The rapid development of information and communication technology has driven the need for information services that are more relevant and adaptive to user behaviour. This research aims to integrate data analytics in the study of user behaviour to support the development of effective information services. The dataset used is Kaggle's Online Retail Dataset, which includes sales transaction data of online retail companies in the UK from December 2010 to December 2011. The analysis was conducted through customer segmentation using K-Means Clustering algorithm and predictive analysis with Association Rule Mining. The segmentation results successfully grouped customers into four main clusters, namely loyal customers, potential customers, passive customers, and low-spending customers. Model evaluation showed optimal performance with an accuracy rate of 85%, precision of 82%, recall of 78%, and F1-Score of 80%, and Silhouette Score of 0.62, indicating effective customer segmentation. The findings prove that the application of data analytics can provide deep insights into customer behaviour and support the development of more personalised and adaptive information services. This research is expected to be a reference in designing data-driven information service development strategies in various sectors.

Birth certificate data management is an important aspect of population administration. In the digital era, the need for efficient systems is increasing to overcome various challenges, such as manual errors, slow administrative processes, and difficulties in searching for data. This research focuses on designing a data management system for Indonesian citizens' birth certificates at the Batu Bara Regency Population and Civil Registration Service (Dukcapil). This system was designed using a systematic approach, starting from needs analysis to the implementation stage. The method used involves collecting data through interviews, literature study, and direct observation in the field. The result of this design is a digital-based system that makes it easier to record, store and manage birth certificate data efficiently. By implementing this system, it is hoped that it can improve the quality of public services, minimize data errors, and support the government's digitalization program.

The Jayapura City Transportation Agency faces significant challenges in managing traffic violation data due to rapid population and economic growth. The use of Excel-based systems has proven inadequate, so a more integrated and efficient information system update is needed. Thus, the research and development method to design a web-based data archiving information system application with a waterfall methodology approach. It is hoped that this application can improve the efficiency of filing and searching for violation data, as well as support better traffic control in Jayapura City.

This research aims to examine and analyze the work system implemented by the archives management section at the Batu Bara Regency Library Service. Archives are an important source of information to support the smooth running of administration and public services, so their management requires an efficient and organized system. This research uses a qualitative descriptive approach, with data collection through interviews, observation and documentation studies. The research results show that the archive management work system at the Batu Bara Regency Library Service still faces several obstacles, such as limited facilities and infrastructure, as well as a lack of human resources trained in the field of archive management. However, the archives management department attempts to overcome this problem by implementing a system that is in accordance with existing procedures. It is hoped that the results of this research can provide recommendations for improving the records management work system in related agencies, in order to increase efficiency and effectiveness in public services.

Puskesmas Kosambi employs Non-PNS staff whose discipline, particularly regarding attendance and work location, needs to be evaluated. The previous manual attendance system was found to be ineffective in monitoring staff discipline. This study aims to develop a mobile-based attendance system equipped with GPS radius and selfie photo features to improve the accuracy and management of attendance. The GPS radius feature ensures that staff can only clock in within the designated area, such as the Puskesmas area, while the selfie photo feature verifies the identity of the staff member clocking in. This study involved 24 Non-PNS staff members and used the K-Means Clustering algorithm to group staff based on their discipline levels. The results showed that the system was effective in improving staff discipline, with 11 employees categorized as highly disciplined, 10 as moderately disciplined, and 3 as lowly disciplined. The implications of this study suggest that the implementation of a mobile-based attendance system can improve attendance monitoring and enhance work discipline at Puskesmas Kosambi.

In  recent  years,  air  quality  has  become  an  increasingly  important  issue  in  various  cities  around  the  world.  Real-time  air  quality  monitoring  is  essential  for  identifying  pollution  problems  and  taking  appropriate  actions.  This  article  discusses  the  design  of  an  Internet  of  Things  (IoT)-based  air  quality  monitoring  system  using  the  prototype  method.  The  system  is  designed  to  monitor  air  quality  parameters  such  as  PM2.5,  PM10,  CO2,  and  temperature  in  real-time  and  present  the  data  to  users  through  a  web-based  application.

The rapid development of information technology has provided great opportunities for the service sector to improve efficiency and convenience, including in vehicle washing services. This research aims to develop a web-based system using the Laravel framework, which is designed to simplify the online ordering process and overcome queuing problems. The system features user authentication, location-based booking, payment gateway integration for real-time transactions, and an interactive dashboard for service providers. The case study was conducted at Carenmore, a vehicle wash service in Krian. The implementation results show that the system is able to reduce customer waiting time, provide a better user experience, and improve service efficiency. Further development is recommended to add a variety of features, improve interface design, and increase the system's ability to handle larger user loads.

Technological developments in the agricultural sector, especially in the hydroponic method, provide solutions for optimizing plant growth in urban environments. This research integrates ESP32 microcontroller technology with a DS18B20 temperature sensor and TDS sensor to monitor and control temperature and nutrient quality in a hydroponic system. In addition, the Sugeno fuzzy logic method is used for automatic water quality monitoring.

Assembling a personal computer and installing the Windows operating system are important activities in mastering information technology. This research aims to provide an in-depth understanding of the computer assembly process and operating system installation. The methods used include a Research and Development (R&D) approach, hands-on practice, and question-and-answer sessions. The research results show that systematic assembly steps and proper installation can produce a well-functioning computer. In addition, initial testing after assembly is crucial to ensure that all devices are detected correctly. It is hoped that this research can serve as a reference for information technology education and enhance students' practical skills in the field of computers.

Task scheduling in cloud computing environments is a crucial aspect in optimizing resource allocation and improving system efficiency. This research aims to analyze trends in task scheduling algorithms in cloud computing using a Systematic Literature Review (SLR) approach on various scientific publications published between 2018 and 2025. The results of the study show that Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Genetic Algorithm (GA) algorithms are the most commonly used methods in solving task scheduling problems. PSO stands out as an effective algorithm due to its ability to find global optimal solutions, handle non-linear and multimodal problems, and its efficiency in managing computational resources. Additionally, various studies have shown that optimization of scheduling algorithms can be achieved through a combination or modification of existing methods to improve system performance. This study provides in-depth insights into the development of scheduling algorithms in cloud computing and opens up opportunities for further research in developing more innovative and adaptive approaches.

This research aims to develop an expert system that can diagnose diseases related to brain tumors using the Case Based Reasoning (CBR) method. The CBR method works by comparing new cases with previous cases that have been stored in the database to provide appropriate diagnoses and treatment recommendations. This system is designed to assist medical personnel in analyzing patient symptoms, thus speeding up the process of identifying the type of brain tumor. In addition, the system is also equipped with a knowledge base obtained from real cases that have been validated by medical experts. Test results show that the diagnostic accuracy of this system reaches a fairly high level, especially in detecting frequently encountered types of brain tumors. Thus, this system has the potential to be an effective tool in the medical diagnosis process, especially in patients who show symptoms of brain tumors.

Hyperthermia is a condition characterized by symptoms such as dehydration, muscle spasms, dizziness, weakness, nausea, vomiting, and fatigue, which can harm the patient's condition. The causes of hyperthermia can vary, ranging from lack of fluids to excessive physical activity. RSU Putri Bidadari has doctors who are experts in treating various diseases, including hyperthermia. However, several obstacles often occur in the direct consultation process, such as long queues, long distances, limited time, and costs. Therefore, a technology-based system is needed that is able to manage hyperthermia symptom data and help diagnose the disease early, so that patients can get information and early treatment quickly. This method is used to manage the symptoms selected by the patient to determine the possibility of the disease with a high level of confidence. Based on the analysis of the selected symptoms, this system is able to produce the most accurate diagnosis with the case of hyperthermia type Heat exhaustion, with a confidence level of 50.26%.

Epilepsy, or apoplexy, is a chronic disease characterized by recurrent seizures and impaired consciousness due to disorders of the central nervous system. In developing countries, including in RSU Putri Bidadari, epilepsy management is often hampered by high consultation costs, resulting in suboptimal quality of treatment and patient recovery. To overcome this challenge, a system is needed that can facilitate the diagnosis and treatment of epilepsy more efficiently. By using this method, RSU Putri Bidadari can improve the precision of epilepsy diagnosis and determine more appropriate treatment steps, despite limited resources. The Bayes method, as a statistical approach, offers a potential solution to improve the accuracy of diagnosis through data-based probability estimation of diseases and symptoms reported by patients such as frequent hunger, thirst, urination, weight loss, vaginal infections, easy fatigue, tingling legs, and blurred vision. The analysis results of the system show an estimated probability of 73% for patients suffering from generalized epilepsy. The Bayes method-based system is expected to help RSU Putri Bidadari in providing more effective treatment and improving the overall quality of life of epilepsy patients.

Obsessive-Compulsive Disorder (OCD) is a psychiatric disorder characterized by uncontrollable obsessive thoughts and compulsive behaviors. The disorder triggers anxiety in sufferers that often drives them to avoid situations or places that can trigger obsessions, such as shaking hands or using public restrooms. Proper treatment is necessary to prevent further impact on the quality of life of OCD sufferers. However, early diagnosis is often constrained by limited time and access to medical experts. To overcome this, an expert system based on the Certainty Factor method was developed. This system mimics the thought process of a medical expert in diagnosing OCD using symptoms selected by the user. Certainty Factor is used to calculate the certainty level of each diagnosis based on the inputted symptoms. From the analysis, the system is able to provide diagnoses with high accuracy, even reaching 100% for some OCD cases. These results show that expert systems can be an effective tool in detecting OCD early, thus accelerating the process of proper handling and treatment

This research aims to design and build a web-based broadcast equipment inventory information system at TVRI North Sumatra. This information system was developed to overcome existing inventory management problems, such as tracking, managing and maintaining broadcast equipment. The development method used includes requirements stages, system design analysis, implementation, and testing using the black box method. This system was built using PHP as the programming language, MySQL as the database, and CSS and JavaScript for the user interface. Test results show that this system can manage equipment inventory effectively, including the functions of borrowing, returning and maintaining equipment. This system also allows integration with other existing systems at TVRI and provides long-term benefits such as increasing data accuracy and operational efficiency. The implementation of this information system is expected to support smooth operations and improve the quality of inventory management at TVRI North Sumatra.
 

Business legality is the identity of a business that legalizes a business so that it is recognized by the community. Business legality must be valid according to applicable laws and regulations so that the business can be protected by various documents that are valid in the eyes of the law. One of the supporting factors for the sustainability of a business is influenced by the existence of legal elements of the business being run. Business permits that must be owned by the community are a business establishment deed, business entity NPWP, trade business license (SIUP), company domicile certificate (SKDP) and business registration number (NIB). The increase in community businesses in Sei Bingai District, Langkat Regency has triggered many business permits that are not directly supervised by the local government. Community business permits are important documents in supervising the running of these community businesses. The types of businesses in Sei Bingai District also vary, such as tourism, C mining, trade, factories and so on.

This research analyzes the performance of the Academic Information System (SIAKAD) in supporting student tuition payments at STMIK Widuri using the PIECES method (Performance, Information, Economy, Control, Efficiency, Service). SIAKAD aims to streamline academic administration and enable online payments. However, issues such as system access delays, especially near payment deadlines, still occur, negatively impacting students' academic services and resulting in administrative penalties. The study employed data collection techniques through observation, interviews, questionnaires, and literature review. The PIECES framework was applied to evaluate six key indicators: performance, information, economy, control, efficiency, and service. The findings reveal that all indicators achieved an average score above 3.80, indicating a "good" system performance. Nonetheless, further improvements are necessary, particularly in terms of access speed and data security, to minimize user difficulties. The study concludes that optimizing the SIAKAD system is crucial for achieving effective and efficient payment processes. Recommendations include enhancing service quality, improving information accuracy, and strengthening data security. This optimization is expected to increase user satisfaction and support the institution's financial management stability.

Cyberbullying has become a significant problem among teenagers along with the rapid development of information and communication technology in the 21st century. This research aims to analyze the challenges faced by teenagers due to the cyberbullying phenomenon, including its impact on mental health, social relationships and daily behavior. Through qualitative and quantitative methods, data was collected from surveys and interviews with teenagers in various secondary schools. The research results showed that almost 40% of respondents had experienced or witnessed cyberbullying, with varying impacts, ranging from anxiety to depression. In addition, factors such as anonymity in cyberspace, low awareness of negative impacts, and the influence of social media contribute to increasing cases of cyberbullying. This research recommends educational approaches and community interventions to increase awareness and understanding of cyberbullying among adolescents, as well as the importance of support from parents and teachers in creating a safe and positive environment in the digital world.

Learning achievement is every learning activity carried out by students which will result in a change in themselves. The learning outcomes obtained by students are measured based on differences in behavior before and after learning is carried out. The economic conditions of students' families at SMP Negeri 2 Binjai have a significant influence on student learning achievement. Many students who come from families with economically disadvantaged backgrounds face various challenges that hinder the learning process. Financial limitations often mean they do not have adequate access to educational resources, such as books, the internet, and additional tutoring which can help improve understanding of subject matter. This research uses the Apriori method as a problem solving method, namely to correlate between Family Socio-Economics, Activities Students Outside the School Environment and Level of Student Learning Motivation with Student Achievement in class. If data A, G, K ? O with Support 30% and Confident 100% and S*C value 30%. So, if a student from a family with an income of less than Rp. 1,000,000 who take part in extracurricular activities outside of school, and have family-driven motivation, will have academic achievement with good report cards. This research indicates that family socio-economic conditions have a significant impact on student academic achievement. Through data analysis, it can be seen that factors such as family income, student activities outside the school environment and the level of student motivation to learn can influence the extent to which students can achieve higher academic achievement.

This research conducts a comparative study on the performance of modern JavaScript frameworks - React, Angular, and Vue.js - in the context of web application development. Using an experimental approach, this study measures various performance aspects including rendering time, bundle size, page load time, and memory usage. The research method involves developing web applications using all three frameworks in different scenarios, as well as interviews with experienced developers. Results show that Vue.js excels in rendering speed and memory efficiency, React demonstrates superiority in handling large-scale dynamic updates, while Angular offers a robust structured architecture. This study also reveals the importance of non-technical factors such as learning curve and community support in framework selection. In conclusion, the selection of an optimal framework should consider project-specific needs, team expertise, and long-term organizational goals. This research provides valuable insights for developers and organizations in choosing the appropriate JavaScript framework for their projects.
 

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.

The integration of augmented reality (AR) technology in education is advancing, as seen in a project at Atisa Dipamkara School. This project developed a chemistry learning application using Unity, Android XR Plugin, and ARCore to address laboratory limitations. The app uses interactive 3D visualizations to help students grasp complex chemistry concepts. Blackbox testing showed that all main modules work well, except for a bug in the compound reset module. The app effectively improves students' understanding, interest, and motivation in chemistry, making abstract concepts more tangible. This application is both a learning aid and an educational innovation, promoting a more interactive and enjoyable learning experience.

In the digital age, public road lighting (PJU) monitoring efficiency is becoming essential for effective infrastructure management. The research developed a PJU monitoring system based on LoRa technology and an Arduino microcontroller to monitor the operating conditions of PJU solar panels in real time. The system uses the LoRa 433 MHz module for remote data communication and is equipped with a voltage sensor to monitor batteries and solar panels as well as an ACS712 current sensor to measure current consumption on LED lights. The data is displayed on an I2C 16x2 LCD screen, making monitoring easy. LoRa technology offers the advantages of broad communication range and low power consumption. The development method used is prototyping, including needs analysis, system design, implementation, testing, and maintenance. Test results show that the system works well, with sensors providing adequate accuracy and LoRa communication enabling remote data access. The system improves the efficiency and accuracy of PJU monitoring, as well as reduces time and effort in the monitoring process. Overall, the system is an effective solution for PJU management in the digital age.

Tuberculosis (TB) is one of the infectious diseases caused by Mycobacterium tuberculosis bacteria infection in the human lungs. Tuberculosis is a disease that can be transmitted from people with TB through coughing, sneezing, talking, laughing or singing. Lack of public knowledge about TB and lack of funds for health checks make many people late to be treated. Expert systems are technologies developed based on programs, in accordance with human methods and mindsets. This aims to help people who want to check their health, but are hampered by costs, besides saving time if the examination place is far from the residential environment of the community concerned. Expert systems require a method that can help solve existing problems. In this study, the method used is the Case-Based Reasoning (CBR) method, because the main function of this method is to diagnose the disease. The calculation process of the Case-Based Reasoning (CBR) method which looks for the similarity value or proximity of old cases to new cases of a patient.

This research explores the application of the Problem-Based Learning (PBL) model to improve the quality of digital image processing learning in the Informatics Engineering Study Program. Through in-depth interviews, observations, and document analysis, this research shows that PBL has a positive impact on students' conceptual understanding, technical skills, and involvement in the learning process. Nevertheless, challenges such as planning relevant learning problems and evaluating the learning process were also identified. Support and training are needed for lecturers to overcome these challenges. These findings provide valuable insights for the development of problem-based teaching practices in the context of digital image processing.

Rohingya in Indonesia has become trending conversation on social media. Sentiment analysis can get public responds. Big data makes the problem time efficiency labeling process, therefore the lexicon dictionary is needed for the labeling process. Data is growing and circulating very rapidly so it takes a fast and efficient time. Although it is fast and makes it easier to solve problems, it is still necessary to question the accuracy produced when using the lexicon labeling. A comparison of the labeling process between the InSet lexicon and the VADER lexicon was conducted to determine the accuracy of the labeling. It was done by combining lexicon with machine learning method of support vector machine and TF-IDF weighting and accuracy result calculated using confusion marix. Data from social media X as many as 9117 lines and labeled with InSet lexicon result 5241 negative sentiments, 1369 positive, and 521 neutral. Then the labeling results with VADER produced 2749 positive, 2523 negative, and 1881 neutral. After labeled, processed SVM and calculated accuracy with results of InSet lexicon accuracy having an average of 85.8% while the VADER SVM lexicon has an average of 82.65%.
 

This study aims to apply the K-Nearest Neighbors (KNN) algorithm to predict stunting status in young children based on height and weight data. Stunting is a growth failure condition caused by chronic malnutrition that negatively impacts children's physical and mental development. The dataset includes height, weight, and stunting status of children. The results show that the KNN model with k=3 achieved 100% accuracy on the test data. Evaluation using the confusion matrix and classification report indicates perfect precision, recall, and F1-score for each class. Data normalization with StandardScaler improved the model's performance by ensuring all features are on the same scale. The KNN algorithm proves to be a simple yet effective method for predicting stunting, demonstrating significant potential for early detection and health intervention in children. This study recommends using a larger and more diverse dataset, as well as incorporating additional relevant features to enhance model accuracy. Implementing the model in a web or mobile application is also suggested to assist healthcare professionals in the field.

Education is an effective way to improve people's quality of life. The quality of education depends on the quality of the teachers who teach. Teachers have an important role in the growth and development of students at school. SDN Langenharjo 01 evaluates teacher performance in teaching students by selecting the best teachers as a form of appreciation for their performance and also as a form of teacher motivation to improve the quality of teaching students at school. The school still carries out manual calculation processes and does not yet use the system. The aim of this research is to develop a decision support system for determining the best teachers at SDN Langenharjo 01 using the web-based Simple Additive Weighting (SAW) method. This research uses the Agile System Development method, starting with initial planning, then continuing with the analysis and design stages, and ending with implementation and testing. The SAW method calculation shows that Raharja S.Pd is the best teacher with a preference value of 0.9913. Testing of system functionality shows that all functions in this decision support system work as expected

Abstract: The New Student Admission Information System (SIPMB) at STMIK Kaputama aims to enhance efficiency and transparency in the student admission process. This study conducted an information system audit using the COBIT 5 framework to evaluate system maturity and provide improvement recommendations. The audit results indicate that the system has not met the expected targets in all domains, with an average maturity level of 1.35 or 135%, classified as Fully Achieved (F). However, according to the capability model mapping condition index, the value is at level 1 (Performed Process). STMIK Kaputama needs to increase openness to innovation inputs and clear communication with prospective students to avoid errors in the admission process, as well as improve service quality for prospective students.
 

The Binjai Library has developed a technology-based information service system to improve service quality for the community. This study aims to audit the information service system of the Binjai Library using the COBIT 5 framework. Based on the analysis, it was found that the information system has not reached the expected target levels in all domains. The average maturity level calculation shows a score of 1.43 or 143%, which is classified as Fully Achieved. Key findings include the near alignment of human resources and IT capabilities with institutional needs, suboptimal innovation in services, and the need for improvement in HR management and communication. This study concludes that the Binjai Library needs to implement improvements and innovations to enhance the performance of the information system and service quality.
 

This research aims to analyze the influence of AI Chat GPT in completing practicum reports by students of the Informatics Engineering Study Program at Muhammadiyah University of Ponorogo. The background to this research is based on the increasingly rapid development of AI technology, offering various conveniences in various fields, including education. The aim of the research is to analyze the extent to which Chat GPT influences students' ability to prepare practical reports effectively and efficiently. The research method used is a quantitative method by collecting data through a questionnaire using a Google form which was distributed to active students of the Informatics Engineering study program at Muhammadiyah University of Ponorogo. The results of this research show that Chat GPT significantly increases students' understanding of practical material, helps students solve problems encountered in preparing reports, and reduces the time needed to complete reports. The implication of this research is the importance of integrating AI technology in the learning process to increase student academic effectiveness and efficiency. However, appropriate policies are needed to avoid excessive dependence on this technology.
 

Children under five are more at risk of experiencing nutritional disorders because during this period children will experience very rapid growth and development. Nutritional disorders can result in children's performance in activities not being optimal, hampering the growth and development process, and can even cause disease. Malnutrition sufferers in Indonesia are increasing every year, and the percentage of malnutrition in Indonesia is around 3.4%. An expert system is a computer-based system built based on facts, knowledge and reasoning that can help solve a problem. However, to measure the uncertainty and level of expert confidence in identifying and detecting malnutrition, an expert system can use the Dempster-Shafer method. The results of testing the system model using the Dempster-Shafer method on toddler nutritional diseases obtained quite good results with system accuracy of 84% with a precision value of 81.83% and also a recall value of 80%.

The process of identifying the ripeness level of cayenne peppers is an important step in cultivation and post-harvest handling. Dependence on the quality factors of farmers, such as visual diversity and differences in ripeness perception, results in subjective harvest outcomes. This manual process is also prone to inconsistent results, as humans have time limitations, fatigue, and sometimes lack concentration when sorting for long periods. To minimize these issues, technological intervention is needed to mechanically classify the ripeness level of cayenne peppers. This research aims to develop a classification model for the maturity level of cayenne pepper plants. This research proposes the use of the CNN method for feature extraction and KNN for data classification based on the features extracted by CNN. From the test scenarios carried out, the classification carried out by KNN based on CNN feature extraction got the best accuracy of 99.33%, while the CNN classification model got the best accuracy of 87.33%.

: This study aims to evaluate the sales information system at UMKM CKS using the COBIT 5 framework. Initial observations revealed several issues, including an unresponsive user interface and a complicated check-out process. The audit results showed an average maturity level of 1.56 or 156%, indicating a Fully Achieved (F) status with a systematic approach. However, on the capability model scale, it is at level 1, which is the Performed Process. The conclusion of this research indicates that although the sales information system has been successfully implemented, there is a need for innovation and enhancement of consumer services, as well as better communication between the company and consumers to improve the effectiveness of the information system.

This study aims to apply the C4.5 algorithm in classifying athlete performance based on the 2023 award recipient list. The C4.5 algorithm was chosen for its ability to construct decision trees that can identify patterns and characteristics distinguishing high-performing athletes. The data used in this study includes various attributes such as gender, age, sport, number of medals, and level of competition participation. The results show that the C4.5 algorithm can classify athletes with high accuracy. The resulting decision tree provides valuable insights into the key factors contributing to athlete performance. The implementation of this algorithm is expected to assist sports organizations in more effectively identifying and developing potential talents.