CD Skripsi

Studi Komparasi Mahasiswa Berpeluang Drop Out Menggunakan Algoritma K-Means Dan Agglomerative Hierarchical Clustering (Ahc)

---

The quality of a higher education institution results from students who are competent in completing their studies. A problem in the campus world that often occurs today is the length of time students take to complete their education. In the Faculty of Mathematics and Natural Sciences, Riau University, every year students have the opportunity to drop out. This research aims to compare the results of the grouping of students who are likely to drop out at the Faculty of Mathematics and Natural Sciences, Riau University and determine the level of accuracy of the method used. Grouping was carried out using the K-Means Clustering method and Agglomerative Hierarchical Clustering by grouping into 3 clusters, namely students with a low, medium and high chance of dropping out. In this research, calculations were carried out using Rapidminer software. The final results of this research show that the K-Means method produces a Davies-Bouldin Index value that is smaller than Agglomerative Hierarchical Clustering, namely 0.1981 and 0.23166 respectively, so the K-Means method can provide more accurate grouping results.


Keywords: Data mining, K-Means, Agglomerative Hierarchical Clustering, Drop Out.

---

Ketersediaan

Informasi Detail

Judul Seri

-

No. Panggil

1703110881

Penerbit

Pekanbaru : Universitas Riau FMIPA Sistem Informasi., 2024

Deskripsi Fisik

-

Bahasa

Indonesia

ISBN/ISSN

-

Klasifikasi

1703110881

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

SISTEM INFORMASI

Info Detail Spesifik

NIM yang benar di bagian pengarang

Pernyataan Tanggungjawab

Mutia

Versi lain/terkait

Lampiran Berkas

Komentar

---

Anda harus masuk sebelum memberikan komentar