This study aims to design and develop an automatic attendance system using Radio Frequency Identification (RFID) technology and an ESP32 CAM. The system is designed to improve efficiency and accuracy in recording student attendance in academic environments. When a student's RFID card is scanned, the system automatically identifies the student and captures an image through the ESP32 CAM as additional proof of attendance. Attendance data and captured images are stored in a database that is connected in real-time via the Internet of Things (IoT). The results of the testing show that this system is able to perform the attendance process quickly and accurately, providing convenience for administrators in managing and monitoring attendance data digitally.

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.

This study aims to design and develop an automatic solar tracker system based on the Internet of Things (IoT) to enhance the efficiency of energy collection in solar panels. The system utilizes key components such as the ESP32 microcontroller, LDR sensors, servo motors, and the Blynk platform to monitor performance in real-time. The solar tracker is designed to follow the movement of the sun, ensuring that the solar panels are always positioned optimally to receive sunlight throughout the day. Testing indicates that this system improves solar energy absorption efficiency compared to static solar panel systems. Furthermore, the integration of IoT allows for more effective and efficient remote control and monitoring. Thus, this technology not only offers improvements in energy efficiency but also provides a practical solution for better and more sustainable solar energy management.

Birth problems are one of the problems that have not been resolved in various regions, where an average mother gives birth to three to four children. The increase in population due to births will also affect various aspects of development, and pose a big risk to ensuring community welfare. For example, opportunities to obtain educational facilities, job opportunities, health insurance, housing and can increase opportunities for increasing poverty and crime. To find out which factors influence the birth of a baby, an association rule is needed to find out which factors influence the birth of a baby which can be seen from several criteria such as a woman who has a low level of education or a bachelor's degree, a woman who marries at an old age. , or women who marry underage, give birth naturally or surgically. Association rules are a data mining technique for determining the relationship between items in a set of data that has been determined. By determining min support 0.01, confidence 0.1 and 7 itemsets, the results obtained are 25 data items with varying min support and confidence with a maximum support x confidence result of 100%. By using the a priori method, 14 of the best rules were produced by producing the most up-to-date information.

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%.

This research aims to cluster disease data based on patient age using the K-Means method at RSUD Dr. RM. Djoelham. In this case study, the clustering method with the K-Means algorithm is used to group patients based on patient age, address and type of disease. With this method, information can be obtained regarding patient grouping patterns based on age at Dr. RM. Djoelham, who helps identify the closest relationships between patient groups and provides insight into the distribution of disease across age groups, regions and types of disease suffered.This research was conducted at RSUD Dr. RM. Djoelham by loading data from patients treated at the hospital. The data used is 1,100 patient data from 2022-2024 which has been recorded by the hospital. This patient data will be analyzed using 3 variables in the research, namely Patient Age (C1), Address (C2), and Type of Disease (C3). With the results, cluster 1 contains 320 data, cluster 2 contains 326 data, and cluster 3 contains 454 data.

Dementia is a growing global health challenge due to the aging population and lifestyle changes. Early and accurate diagnosis is crucial but often difficult and costly. The Case-Based Reasoning (CBR) method in artificial intelligence offers a solution by mimicking human problem-solving based on past experiences. This study aims to develop and implement an efficient and reliable CBR-based dementia diagnosis system. The system is expected to analyze and compare patient symptoms and medical histories with documented cases to provide faster and more accurate diagnostic recommendations. The implementation of CBR in a web-based expert system using PHP and MySQL has proven effective, significantly contributing to the improvement of patient quality of life and healthcare system effectiveness.

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.