Informed consent is a crucial component of medical records that ensures the legality and ethical compliance of medical procedures conducted on patients. At RSUP Surakarta, the completeness of informed consent documentation in the surgical ward has consistently failed to meet the national minimum service standard of 100%, with observed monthly completion rates ranging from 86% to 98% throughout 2024. This study aims to analyze the factors contributing to the incomplete documentation of informed consent in the surgical ward. A qualitative descriptive approach was employed using data collection techniques such as direct observation, in-depth interviews, documentation review, and participatory methods including the USG (Urgency, Seriousness, Growth) prioritization technique and brainstorming. The study involved four key informants: a medical services director, a surgeon, a surgical nurse, and a medical records officer. Findings indicate that the main contributing factors are the absence of Standard Operating Procedures (SOP) for consent documentation, lack of training, insufficient internal and external motivation due to absence of rewards or enforcement measures, limited knowledge, and short tenure of some staff members. The USG analysis identified the absence of an SOP as the most critical issue. Consequently, the development and dissemination of an SOP, coupled with regular staff training, were recommended as corrective measures. This study underscores the importance of structural and motivational support in improving the completeness of informed consent documentation, which is vital for patient safety and institutional accountability.

The detection of computer network attacks is becoming increasingly important as the complexity of cyber-attacks threatening information systems and infrastructure continues to rise. To address these threats, artificial intelligence techniques have become a primary focus in the development of more effective attack detection systems. One algorithm that has proven reliable in this context is the C4.5 decision tree. This study aims to apply the C4.5 algorithm in network attack detection using a dataset that includes various types of attacks and network activities. The process includes data preprocessing, decision tree model building, and model performance evaluation. The results show that the C4.5 decision tree algorithm is effective in classifying network activities into attacks and normal activities with a satisfactory level of accuracy. The model successfully recognizes attack-related patterns, and further analysis identifies key factors influencing attack detection. This research provides a significant contribution to the development of reliable and efficient attack detection systems in computer networks. By applying the C4.5 decision tree algorithm, it is expected to help enhance information security and protect network infrastructure from increasingly complex cyber threats