Seleksi Fitur dan Perbandingan Algoritma Klasifikasi untuk Prediksi Kelulusan Mahasiswa

Zeniarja, Junta; Salam, Abu; Ma'ruf, Farda Alan

doi:10.17529/jre.v18i2.24047

Cited by 1 publication

(1 citation statement)

References 20 publications

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“…Algoritma random forest, sebagai metode klasifikasi yang digunakan untuk mengidentifikasi siswa yang berisiko tinggi tidak lulus tepat waktu, membawa inovasi signifikan dalam analisis prediktif di bidang pendidikan [10]. Prosesnya melibatkan pembuatan pohon keputusan secara acak menggunakan subset acak dari data pelatihan, dan prediksi dari masing-masing pohon diintegrasikan sebagai suara mayoritas [11].…”

Section: Pendahuluanunclassified

Prediksi Kelulusan Tepat Waktu Siswa SMK Teknik Komputer Menggunakan Algoritma Random Forest

Fatunnisa,

Marcos

2024

JAMIKA

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School performance can be measured through student completion rates, which is a key indicator. Low graduation rates indicate problems in the education and learning system, which require timely intervention to prevent students from not completing their education. Therefore, predicting graduation rates is crucial for schools in order to determine students who are likely to not complete their education, so as to provide early assistance to improve their academic performance. This research is urgent because by understanding and predicting student completion, schools can allocate resources more effectively to support at-risk students, with the ultimate goal of improving graduation rates and overall school performance. This research uses random forest algorithm with graduation dataset. The distribution of training and test data selection uses stratified random sampling method to ensure a balanced representation of each class generated. The Random Forest model was successfully obtained through training and model evaluation using test data, showing an accuracy of 1.0 or equivalent to 100%. The use of the random forest algorithm on student graduation datasets can be an effective approach in supporting timely graduation prediction due to its high accuracy and the model's ability to recognize both graduating and non-passing students.

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Section: Pendahuluanunclassified

Prediksi Kelulusan Tepat Waktu Siswa SMK Teknik Komputer Menggunakan Algoritma Random Forest

Fatunnisa,

Marcos

2024

JAMIKA

View full text Add to dashboard Cite

show abstract

Seleksi Fitur dan Perbandingan Algoritma Klasifikasi untuk Prediksi Kelulusan Mahasiswa

Cited by 1 publication

References 20 publications

Prediksi Kelulusan Tepat Waktu Siswa SMK Teknik Komputer Menggunakan Algoritma Random Forest

Prediksi Kelulusan Tepat Waktu Siswa SMK Teknik Komputer Menggunakan Algoritma Random Forest

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