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All Journal Riau Jurnal Teknik Informatika
Suwarno Herry, Ayni
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Computer Science & IT

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IMPLEMENTASI DATA MINING PREDIKSI KELULUSAN SISWA MENGGUNAKAN METODE DECISION TREE PADA SMK IPTEK TANGSEL Suryaningrat, Suryaningrat; Lina Mulani Sitio, Sartika; Suwarno Herry, Ayni
Riau Jurnal Teknik Informatika Vol. 4 No. 1 (2025): Maret 2025
Publisher : Prodi Teknik Informatika Universitas Pasir Pengaraian

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30606/rjti.v4i1.3267

Abstract

Vocational High School (SMK) is a formal educational institution that prepares its graduates for the world of work. However, SMK Iptek Tangsel faces challenges in optimizing student data and overcoming labor shortages in the field of education administration. To overcome this problem, this study applies a prediction system using a data mining technique with the decision tree method. Aims to improve the accuracy of student graduation predictions. The methodology of this study adopts a quantitative approach with structured steps, including observation, interviews, data collection, and documentation. The results showed that the accuracy of Rapid miners in motorcycle and multimedia business engineering reached 98.49%, while in hospitality and accounting accommodation reached 99.05%. This graduation prediction system helps schools identify students at risk of not graduating and who are at potential to graduate, optimize data management, and enable appropriate interventions. This research makes a significant contribution to improving the decision-making process in the field of education through the use of data mining technology. Thus, the graduation prediction system can be an effective tool in supporting data management and increasing student graduation in vocational schools.
Co-Authors Sartika Lina Mulani Suryaningrat, Suryaningrat