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All Journal Jurnal Informatika dan Teknik Elektro Terapan Brahmana : Jurnal Penerapan Kecerdasan Buatan
Naibaho, Agusti Frananda Alfonsus
Unknown Affiliation
Computer Science & IT Decision Sciences, Operations Research & Management Education Engineering

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Prediksi Kelulusan Siswa Sekolah Menengah Pertama Menggunakan Machine Learning Naibaho, Agusti Frananda Alfonsus; Zahra, Amalia
Brahmana : Jurnal Penerapan Kecerdasan Buatan Vol 4, No 2 (2023): Edisi Juni
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/brahmana.v4i2.192

Abstract

In recent years, there have been students who have not graduated on time at Lubuk Alung 1 Public Junior High School. This statement is supported by graduation data from SMP Negeri 1 Lubuk Alung. Therefore it is necessary to predict student graduation status to identify factors that influence student graduation, which can also be used to help schools solve problems more easily. To overcome this problem, researchers predict student graduation based on student graduation information. The attributes used are personal data related to students, student academic data, and data related to the work of students' parents. Researchers obtained data on student graduation from schools that had been recapitulated. The classification algorithms used are decision tree, random forest, and extreme gradient boosting with grid searchCV and k-fold=5. Predictive accuracy using the random forest algorithm outperforms other methods with a value of 99.5%.
PREDIKSI KELULUSAN SISWA SEKOLAH MENENGAH PERTAMA MENGGUNAKAN MACHINE LEARNING Naibaho, Agusti Frananda Alfonsus; Zahra, Amalia
Jurnal Informatika dan Teknik Elektro Terapan Vol. 11 No. 3 (2023)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v11i3.3056

Abstract

In recent years, there has been a number of students who graduated late at Lubuk Alung 1st State Junior Highschool. This statement is supported by graduation data from Lubuk Alung 1st Satet Junior Highschool. Therefore, it is necessary to predict students’ graduation status to identify which factors influence the student’s graduation, which will also consequently help the school to solve problem more easily. To solve this problem, the researchers predict student graduation based on student graduation information. The attributes used are personal data related to students, student academic data, and data related to the work of the student’s parents. This research retrieved data on student graduation from schools that have been recapitulated. The classification algorithms used to predict students’ graduation are decision tree, random forest, and extreme gradient boosting with grid searchCV and k-fold=5. The prediction accuracy using the random forest algorithm outperforms the others with a value of 99.5%.
Co-Authors Zahra, Amalia