Implementation of Decision Tree Algorithm for Student  Interest Analysis Based on Subjects at MTs Aisyiyah Binjai

Muhammad Haekal; Irvan

doi:10.59934/jaiea.v5i1.1754

Authors

Muhammad Haekal Universitas Muhammadiyah Sumatera Utara
Irvan Universitas Muhammadiyah Sumatera Utara

DOI:

https://doi.org/10.59934/jaiea.v5i1.1754

Keywords:

Learning Interest, MTs Aisyiyah Binjai, Decision Tree, Data Analysis, Web-based System

Abstract

Education plays a vital role in improving the quality of human resources, where students’ interest in learning subjects is one of the key factors influencing academic achievement. However, not all students show the same level of interest in every subject, which affects their motivation and performance. This study aims to analyze students’ subject preferences at MTs Aisyiyah Binjai using the Decision Tree algorithm. The method was chosen for its ability to process complex academic data, discover hidden patterns, and identify influential factors in determining students’ interests. The results are implemented into a web-based system that allows schools to flexibly monitor and evaluate students’ learning interests. This research is expected to assist schools in developing more effective learning strategies, enhancing students’ motivation, and supporting better academic achievement.

Downloads

Download data is not yet available.

References

Y. Hulu and Y. N. Telaumbanua, “Analisis Minat Dan Hasil Belajar Siswa Menggunakan Model Pembelajaran Discovery Learning,” Educ J Pendidik, vol. 1, no. 1, pp. 283–290, 2022, doi: 10.56248/educativo.v1i1.39.

S. Pada, M. Pelajaran, N. Hikmah, M. I. Haliq, and E. Ekasari, “Pengaruh Minat Belajar Dan Teman Sebaya Terhadap Hasil Belajar,” vol. 6, no. 1, pp. 1248–1254, 2022.

Z. Nurizati, A. Hidayat, D. Vernanda, and T. Hendriawan, “Analisis Kelayakan Penurunan UKT Pada Mahasiswa dengan Menggunakan Metode Decision Tree,” J Tekno Kompak, vol. 18, no. 1, p. 90, 2024, doi: 10.33365/jtk.v18i1.3419.

R. Robianto, S. H. Sitorus, and U. Ristian, “Penerapan Metode Decision Tree Untuk Mengklasifikasikan Mutu Buah Jeruk Berdasarkan Fitur Warna Dan Ukuran,” Coding J Komput dan Apl, vol. 9, no. 01, p. 76, 2021, doi: 10.26418/coding.v9i01.45907.

A. I. Rizmayanti, N. Hidayati, F. S. Nugraha, and W. Gata, “Penerapan Data Mining Untuk Memprediksi Kompetensi Siswa Menggunakan Metode Decission Tree ( Studi Kasus Smk Multicomp Depok ),” Swabumi, vol. 9, no. 1, pp. 9–18, 2021, doi: 10.31294/swabumi.v9i1.8363.

R. Rahmadini, Enjel Erika LorencisLubis, Aji Priansyah, Yolanda R.W.N, and Tuti Meutia, “Penerapan Data Mining Untuk Memprediksi Harga Bahan Pangan Di Indonesia Menggunakan Algoritma K-Nearest Neighbor,” J Mhs Akunt Samudra, vol. 4, no. 4, pp. 223–235, 2023, doi: 10.33059/jmas.v4i4.7074.

M. A. Abdillah, A. Setyanto, and S. Sudarmawan, “Implementasi Decision Tree Algoritma C4.5 Untuk Memprediksi Kesuksesan Pendidikan Karakter,” Respati, vol. 15, no. 2, p. 59, 2020, doi: 10.35842/jtir.v15i2.349.

I. Y. Yudo Bismo Utomo, Iin Kurniasari, “Penerapan Knowledge Discovery in Database,” J Tek Inform Kaputama, vol. 7, no. 1, 2023.

M. A. R. Siregar, Implementasi machine learning dengan metode algoritma decision tree c4. 5 untuk pemilihan bandwidth internet wifi rumahan. 2022. [Online]. Available: https://repository.uinjkt.ac.id/dspace/handle/123456789/65304%0Ahttps://repository.uinjkt.ac.id/dspace/bitstream/123456789/65304/1/MUHAMMAD AMMARIDHO R. S.-FST.pdf

M. M. Ali, T. Hariyati, M. Y. Pratiwi, and S. Afifah, “Metodologi Penelitian Kuantitatif Dan Penerapan Nya Dalam Penelitian,” vol. 2, no. 2, 2022.