Implementation Of The Naïve Bayes Method With Approach Holistic In Predicting Student Achievement

Abstract

Within the realm of education, students frequently encounter many obstacles, including defiance, absence of drive, and other intricate factors that can impact the caliber of their learning. This issue can result in many students abstaining from attending class, withdrawing from school, or prematurely leaving before completing their education. This highlights the need for proactive and preventive measures taken by educational institutions to assist children in overcoming learning barriers from a young age. Professional educational and training institutions should promptly identify potential obstacles to students' learning and take proactive measures to assist them in overcoming these difficulties. These endeavors encompass vigilant supervision, effective communication among educators, parents, and students, as well as impactful mentoring and counseling initiatives. By adopting this methodology, educational establishments can establish an all-encompassing and nurturing atmosphere that prioritizes the academic achievement of students. In this study, we employ the Naive Bayes algorithm and the Holistic Approach, two powerful tools in the field of data analysis, to forecast student academic performance at Otista Bandung Vocational School. The Naive Bayes algorithm allows us to make predictions based on the probability of an event occurring given certain conditions, while the Holistic Approach ensures that we consider all relevant factors in our analysis. This research aims to offer early forecasts for students who are susceptible to low academic performance, enabling schools to proactively intervene and avert a fall in student success. By implementing this approach, schools may offer targeted support to individuals requiring more help, while also fostering an inclusive environment that promotes academic success for all students, regardless of their circumstances.