A PREDIKSI TINGKAT KELULUSAN MAHASISWA KEPERAWATAN DENGAN PENDEKATAN NAÏVE BAYES - Putra, Arriano; Sugandi, Febri (LP2M Institut Informatika Dan Bisnis Darmajaya, 2024)
One of the benefits of the Academic Information System (SIAKAD) in higher education institutions is its ability to present graduation index results for each student, allowing for an assessment of their academic engagement throughout each semester. Using cloud-based academic applications, decision-makers can access semester-by-semester student performance data to evaluate and predict the completion time of student studies. A machine learning approach is employed to systematically analyze the data, particularly in measuring the Graduation Index (GI). The Naïve Bayes algorithm is selected for its ability to classify and structure data effectively. This need can be further explored through campus research to enhance future decision-making processes. The objective of this study is to predict the graduation rate of nursing students using the Naïve Bayes algorithm, one of the machine learning methods. The research data was sourced from the Dharma Wacana Nursing Academy, including student cohorts from 2019 to 2023 as training data and the 2024 cohort as testing data. The dataset comprises 453 students who graduated between 2019 and 2023 and 64 active students from the 2024 academic year. Testing using the RapidMiner application, with graduation status as the output, demonstrated that the prediction model achieved an accuracy rate of 99.81%, with 100% precision and 99.80% recall. These results indicate that Naïve Bayes is a reliable tool for determining the likelihood of student graduation based on their historical academic data. This study concludes that implementing Naïve Bayes for graduation prediction can support more effective academic decision-making