<![CDATA[Regional General Hospital (RSUD) Dr. M. Yunus Bengkulu is a government-owned health care facility with a strategic role in providing health services to the public, making employee discipline an important factor to maintain. The determination of disciplinary sanctions for employees at RSUD Dr. M. Yunus Bengkulu is still carried out manually based on recorded violation data, which requires a relatively long time and has the potential to create subjectivity in decision making. This study aims to apply the data mining method using the K-Nearest Neighbor (K-NN) algorithm to classify employee disciplinary sanctions based on five assessment attributes, namely years of service, number of tardiness incidents, number of unexplained absences, SOP violations, and service ethics violations. The system was developed using the Waterfall development method and implemented as a web-based application using the PHP programming language and MySQL database. Testing was conducted on 20 training data and 20 testing data using a K value of 5, calculated from the square root of the number of training data. The test results show that the K-Nearest Neighbor algorithm is able to classify employee disciplinary violation data into four sanction categories, namely no sanction, verbal warning, written warning, and disciplinary sanction, with an accuracy rate of 90%. Black box testing results also show that all system functions run according to requirements. This research is expected to help the management of RSUD Dr. M. Yunus Bengkulu make decisions on employee disciplinary sanctions in a more objective, consistent, and measurable manner.]]>
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