The increasing waste problem requires innovative solutions that are efficient and sustainable. This study aims to design and build an Internet of Things (IoT) based garbage cleaning robot that can be monitored and controlled remotely. This system is designed by utilizing a microcontroller as the main brain, sensors to detect the presence of garbage, and an IoT-based communication module that allows monitoring and control of the robot via mobile devices or the web. Based on the results of the analysis and testing carried out, this study shows that the use of ESP8266 in the RC Trans Robot motor control with the Blynk application has succeeded in significantly increasing system efficiency. The system successfully responds to user input via a mobile application and can control the movement of the robot in real-time. The performance of the robot system controlled via a WiFi network shows good stability in various test scenarios. The robot can operate effectively within a distance that matches the range of the WiFi network, with fast control response and reliable communication.

Noise in libraries can disrupt the concentration and comfort of visitors who are reading or studying. Therefore, a tool is needed to detect noise and automatically issue warnings to maintain silence in the library environment. This research aims to design and develop a noise detection tool in libraries using Internet of Things (IoT) technology. The device consists of a sound sensor that detects noise levels and sends data to a cloud-based server for analysis. When the noise level exceeds a specified threshold, the tool provides warnings through visual indicators and notifications to the library staff. The prototype was tested in a library environment and was able to detect noise accurately and issue real-time alerts. The results showed that the device effectively reduced noise levels and improved the comfort of library visitors. With the implementation of IoT technology, this system can be accessed and monitored remotely, facilitating library management in maintaining a conducive environment.

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.

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.

The lungs as the only pump for the respiratory system are very important organs for the continuation of life. Diagnosing or checking lung symptoms early can help people recognize the possibility that they are suffering from lung disease, so that treatment or care can be done earlier to prevent the severity of the disease. The method used in this study is the Naïve Bayes method. Naive Bayes is a simple probabilistic classifier that calculates a set of probabilities by adding up the frequencies and combinations of values ??from the given dataset. An expert system is a computer application that can help decision making in more specific fields with methods that have been analyzed in advance by experts or specialists. This study used variables, namely types of lung disease including Pulmonary Tuberculosis (TB), Chronic Obstructive Pulmonary Disease (COPD), Bronchial Asthma and Lung Cancer. The results of this study are that lung disease or types of lungs can be diagnosed using the web-based Naïve Bayes method, and make it easier for sufferers to consult without seeing a doctor by selecting symptoms of lung disease.