CD Skripsi

Implementasi Data Mining Menggunakan Algoritma Xgboost Dalam Menentukan Kelompok Uang Kuliah Tunggal Pada Mahasiswa Ilmu Komputer Universitas Riau

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One of the most crucial tasks for universities is to make accurate predictions regarding single tuition fees (UKT). This is particularly relevant for Riau University, where the objective is to determine UKT for Computer Science students. The aim of this research is to develop a prediction model that is effective in determining a student's UKT based on a number of related factors. The data set used in this study consisted of 772 student samples, which underwent a pre-processing process to ensure readiness for analysis, including the removal of irrelevant variables. The Cross-Industry Standard Process for Data Mining (CRISP-DM) method was employed as the framework for this study. The XGBoost model was trained and evaluated using cross-validation techniques and accuracy, precision, recall, f1-score. The results demonstrated that the XGBoost model was capable of accurately predicting UKT with an accuracy of 72.59%, precision of 72.53%, recall of 73.30%, and F1-score of 72.77%. This finding suggests that the utilisation of the XGBoost algorithm can be an effective tool to assist universities in setting more equitable and data-driven UKT. Furthermore, this research provides a foundation for the development of additional UKT prediction models that utilise a more diverse data set.
Keywords: Confusion Matrix, CRISP-DM, Single Tuition Fee, XGBoost.

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Ketersediaan

Informasi Detail

Judul Seri

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No. Panggil

1903113745

Penerbit

Pekanbaru : Universitas Riau FMIPA Sistem Informasi., 2024

Deskripsi Fisik

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Bahasa

Indonesia

ISBN/ISSN

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Klasifikasi

1903113745

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

mardiah

Versi lain/terkait

Lampiran Berkas

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