Simulation of A Decision Support System Using Data Mining Method with C4.5 Algorithm: A Case Study

Every university has a goal to produce quality and highly competitive students. To achieve this goal, Telkom University Information Systems Study Program has three learning methods, namely, class lectures, practicum, and expertise groups. To implement this learning method, stakeholders are needed, commonly referred to as Laboratory Assistants and Expert Group Members. However, in the stakeholder recruitment process there is still subjectivity and difficulty in determining applicants who fit the criteria. In order to improve the quality and excellent quality, every university is required to be able to maximize the processes that can have an impact on the quality and quality. This research was conducted to study the C4.5 Algorithm method which was implemented in the recruitment process for Laboratory Assistants and Expert Group Members. Data were collected through file collection and interviews conducted at the Expert Group Trustees and laboratories. From the simulation, 49 patterns were obtained for the old laboratory assistant recruitment data, 9 patterns for the new laboratory assistant recruitment data and 8 patterns for the Expertise Group recruitment data. From each of these data, the decision-making factors that most influence recruitment graduation are obtained, namely, the Interview attribute for the Laboratory Assistant recruitment data and the Motivation Letter attribute for the recruitment data of Expert Group Members.