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.

Members of the KPPS (Voting Organizing Group) are responsible for organizing voting in a polling station (TPS) during general elections in Indonesia. They are the spearhead in carrying out the democratization process by supervising and ensuring the continuity of elections honestly, fairly, and transparently. The duties of KPPS members include preparing TPS before voting begins, receiving and examining voters, supervising the election process to ensure compliance with applicable regulations, counting votes after voting is complete, reporting election results, and maintaining security and order around TPS. Decision support system is a Decision support system or Decision Support System (DSS) is an interactive system that supports decisions in the decision-making process through alternatives obtained from data processing results. The purpose of this study is to facilitate the recruitment of members of the Voting Organizing Group (KPPS). The research method is Weighted Aggregated Sum Product Assessment (WASPAS). WASPAS is to find the most appropriate priority location choices using weighting. The results of this study are that the development of this support system can help the KPU in selecting or selecting KPPS members and this decision support system as a tool in developing KPPS members by viewing or using criteria according to the criteria needed using the WASPAS method.
 
 
 

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.

Vulvodynia is a chronic pain condition affecting the vulva that significantly impacts women’s quality of life. Accurate and early diagnosis poses a challenge due to the often-overlapping symptoms with other conditions and the lack of definitive diagnostic tests. This paper proposes the use of expert system methods as a diagnostic tool for vulvodynia in women. The expert system, integrating medical knowledge with inference algorithms, is designed to analyze symptoms, medical history, and test results to provide accurate diagnoses and treatment recommendations. The study involves the development and evaluation of a computer-based expert system prototype that uses clinical data and medical decision-making to enhance the accuracy of vulvodynia diagnosis. Preliminary results indicate that the expert system can improve diagnostic rates and reduce the time required for identifying this condition, offering a potentially valuable tool for medical professionals in clinical practice.