ANALYSIS OF NAÏVE BAYES ALGORITHM METHOD FOR OUTSTANDING STUDENTS AT YAPENDAK AJAMU PRIVATE JUNIOR HIGH SCHOOL

Putri Erwina, NPM 1909100010 (2023) ANALYSIS OF NAÏVE BAYES ALGORITHM METHOD FOR OUTSTANDING STUDENTS AT YAPENDAK AJAMU PRIVATE JUNIOR HIGH SCHOOL. Tugas_ Akhir (Artikel) : Budapest International Research and Critics Institute-Journal (BIRCI-Journal), 6 (3). pp. 1739-1747. ISSN 2615-3076 (Online) / 2615-1715 (Print)

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Abstract

Academic achievement is a change in skills or abilities that can be enhanced through learning situations. However, an issue arises at Yapendak Ajamu Private Junior High School, where student assessments are still manually inputted, resulting in inefficiency when transferring grades to paper, which are later re-entered into the e-Report system. The calculation of student scores is also done manually, and the criteria for determining outstanding students heavily rely on academic grades, while non-academic aspects are only considered as supporting data with unclear weighting. Consequently, the assessment lacks fairness in determining outstanding students. Moreover, the manual nature of the assessment and the fact that it is held solely by the homeroom teachers make it difficult to access. The Naïve Bayes algorithm method applies a classification system that includes academic grades, attitudes, attendance, and extracurricular activities. School is a place where students weigh knowledge for future needs, each school also has its own permissibility, both in terms of the best student creator school and a school that only has a few smart students, but it can be ascertained that every school wants all its students to have high intelligence, but intelligence is also not only created by the school but intelligence is also based on the students, Yapendak Ajamu Private Junior High School is a private school that has smart students Where this make Yapendak Ajamu Private Junior High School known to many people. These factors can be utilized by the school to determine outstanding students. Out of the 34 data training sessions processed in the Orange application, 30 students were predicted to be outstanding, while the remaining students were classified as not outstanding. The precision for predicting outstanding students is 1.000, while for predicting non outstanding students, it is 0.104. Therefore, the conclusion drawn is that the grades of outstanding students are higher compared to those of non-outstanding students.

Item Type:	Article
Uncontrolled Keywords:	Academic; Achievement; Clsssification; Junior High School; Naïve Bayes
Subjects:	L Education > LB Theory and practice of education > LB1603 Secondary Education. High schools T Technology > T Technology (General)
Divisions:	Fakultas Sains Dan Teknologi > Sistem Informasi
Depositing User:	Unnamed user with email repository@ulb.ac.id
Date Deposited:	20 Nov 2023 09:21
Last Modified:	20 Nov 2023 09:21
URI:	http://repository.ulb.ac.id/id/eprint/517

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