Work

Application of Naïve Bayes Method for Student Performance Classification

Year: 2025

Type: article

Abstract: In every school, students exhibit varying levels of performance, influenced by several factors such as parental support and involvement, participation in extracurricular activities, motivation levels, internet access for learning, teacher quality, peer influence, and learning difficulties. This study aims to classify student performance to identify those who may need additional support for improvement. The classification method employed in this research is the Naïve Bayes algorithm. The results indicate that the trained model successfully classified 25 out of 30 tested data points. The evaluation metrics achieved include a precision of 100%, recall of 80%, specificity of 100%, accuracy of 83%, and an F1-score of 89%.

Source: SISTEMASI

DOI: 10.32520/stmsi.v14i2.4852

Authors: Riza Akhsani Setyo Prayoga, Rizky Basatha, Muhammad Sonhaji Akbar, Ersha Aisyah Elfaiz, Cendra Devayana Putra

Institutions: Institut Teknologi Telkom Surabaya, Universitas Negeri Surabaya

Language: English

Topic: Online Learning and Analytics

Subfield: Computer Science Applications

Field: Computer Science

Domain: Physical Sciences

SDG: Quality education

Open Access status: diamond

OpenAlex ID: W4408317992

FWCI: 0.976

Cites:

Cited by: 1

Related to: 10