CD Skripsi

Penerapan Data Mining Terhadap Prediksi Mahasiswa Drop Out Menggunakan Metode Decision Tree

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The high dropout rates in universities, particularly in the Faculty of Mathematics and Natural Sciences (FMIPA), raise concerns about the effectiveness of education and academic success of students. This phenomenon is often influenced by various academic and non-academic factors that require further identification. This study aims to identify the most influential variables on student dropout risks using the Decision Tree method. Data from 1,518 students were analyzed based on variables such as Grade Point Average (GPA) and the number of credits taken. The results showed that GPA and the number of credits were the most significant factors affecting dropout risks. The predictive model was tested using the split validation technique, yielding an accuracy of 95.39%, precision of 97.38%, recall of 97.61%, specificity of 70.27%, and an F1 score of 97.49%, demonstrating the model's effectiveness in predicting potential student dropouts.

Keywords: Decision Tree, Prediction, Dropout, Split Validation, Confusion Matrix

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Ketersediaan

Informasi Detail

Judul Seri

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No. Panggil

1903113221

Penerbit

Pekanbaru : Universitas Riau FMIPA Sistem Informasi., 2024

Deskripsi Fisik

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Bahasa

Indonesia

ISBN/ISSN

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Klasifikasi

1903113221

Tipe Isi

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Tipe Media

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Tipe Pembawa

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Edisi

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Subjek

SISTEM INFORMASI

Info Detail Spesifik

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Pernyataan Tanggungjawab

Mutia

Versi lain/terkait

Lampiran Berkas

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