Penerapan Algoritma C5.0 Untuk Prediksi Kelulusan Pembelajaran Mahasiswa Pada Matakuliah Arsitektur Sistem Komputer

Authors

  • Muchamad Sobri Sungkar Politeknik Harapan Bersama, Jawa Tengah
  • M Taufik Qurohman Politeknik Harapan Bersama, Jawa Tengah

DOI:

https://doi.org/10.30865/mib.v5i3.3116

Keywords:

Data Mining, Prediction, Graduation, Computer System Architecture, C5.0 Algorithm

Abstract

Computer system architecture is one of the subjects that must be taken in the informatics engineering study program. In the study program the graduation of each student in the course is one of the important aspects that must be evaluated every semester. Graduation for each student / I in the course is an illustration that the learning process delivered is going well and also the material presented by the lecturer in charge of the course can be digested by students. Graduation of each student in the course can be predicted based on the habit pattern of the students. Data mining is an alternative process that can be done to find out habit patterns based on the data that has been collected. Data mining itself is an extraction process on a collection of data that produces valuable information for companies, agencies or organizations that can be used in the decision-making process. Prediction of graduation with data mining can be solved by classifying the data set. The C5.0 algorithm is an improvement algorithm from the C4.5 algorithm where the process is almost the same, only the C5.0 algorithm has advantages over the previous algorithm. The results of the C5.0 algorithm are in the form of a decision tree or a rule that is formed based on the entropy or gain value. The prediction process is carried out based on the classification of the C5.0 algorithm by using the attributes of Attendance Value, Assignment Value, UTS Value and UAS Value. The final result of the C5.0 algorithm classification process is a decision tree with rules in it. The performance of the C5.0 algorithm gets a high accuracy rate of 93.33%

References

E. Buulolo, Data Mining Untuk Perguruan Tinggi, 1st ed. Yogyakarta: Deepublish, 2020.

E. Prasetyo, Data Mining, Konsep Dan Aplikasi Menggunakan Matlab. Yogyakarta: Andi, 2012.

D. Nofriansyah and G. W. Nurcahyo, Algoritma Data Mining Dan Pengujiannya. Yogyakarta: Deepublish, 2017.

A. Novianti and E. Elisa, “Penentuan Aturan Asosiasi Pola Pembelian Pada Minimarket Dengan Algoritma Apriori,†Build. Informatics, Technol. sicience, vol. 2, no. 1, pp. 64–70, 2020.

V. Miralda, M. Zarlis, and E. Irawan, “Penerapan Metode K-Means Clustering Untuk Daging Ayam Buras,†Build. Informatics, Technol. Sci., vol. 2, no. 2, pp. 91–98, 2020.

C. Hutabarat, “Penerapan Data Mining Untuk Memprediksi Permintaan Produk Kartu Perdana Internet Menggunakan Algoritma C5.0 (Studi Kasus: Vidha Ponsel),†Pelita Inform., vol. 6, no. April, pp. 419–424, 2018.

R. Pratiwi, M. N. Hayati, and S. Prangga, “Perbandingan Klasifikasi Algoritma C5.0 Dengan Classification and Regression Tree (Studi Kasus : Data Sosial Kepala Keluarga Masyarakat Desa Teluk Baru Kecamatan Muara Ancalong Tahun 2019),†BAREKENG J. Ilmu Mat. dan Terap., vol. 14, no. 2, pp. 273–284, 2020.

D. P. Utomo, P. Sirait, and R. Yunis, “Reduksi Atribut Pada Dataset Penyakit Jantung dan Klasifikasi Menggunakan Algoritma C5. 0,†Media Inform. Budidarma, vol. 4, no. 4, pp. 994–1006, 2020.

D. P. Utomo and M. Mesran, “Analisis Komparasi Metode Klasifikasi Data Mining dan Reduksi Atribut Pada Data Set Penyakit Jantung,†J. Media Inform. Budidarma, vol. 4, no. 2, p. 437, 2020.

T. Permana, A. M. Siregar, A. F. N. Masruriyah, and A. R. Juwita, “Perbandingan Hasil Prediksi Kredit Macet Pada Koperasi,†Conf. Innov. Appl. Sci. Technol., vol. 3, no. 1, pp. 737–746, 2020.

R. P. S. Putri and I. Waspada, “Penerapan Algoritma C4.5 pada Aplikasi Prediksi Kelulusan Mahasiswa Prodi Informatika,†Khazanah Inform. J. Ilmu Komput. dan Inform., vol. 4, no. 1, p. 1, 2018.

F. Hadi, “Penerapan Data Mining Dalam Menganalisa Pemberian Pinjamana Dengan Menggunakan Metode Algoritma C5 . 0 ( Studi Kasus : Koperasi Jasa Keuangan Syariah Kelurahan Lambung Bukik ),†J. KomTekInfo, vol. 4, no. 2, pp. 214–223, 2017.

I. P. Sari and R. Harman, “Decission Tree Technique Dalam Menentukan Penjurusan Siswa Menengah Kejuruan,†J. Inf. Syst. Res., vol. 1, no. 4, pp. 296–304, 2020.

N. Mayasari, “Comparison of Support Vector Machine and Decision Tree in Predicting On-Time Graduation (Case Study : Universitas Pembangunan Panca Budi),†Int. J. Recent Trends Eng. Res., vol. 2, no. 12, pp. 140–151, 2016.

D. Dalbergio, M. N. Hayati, and Y. N. Nasution, “Klasifikasi Lama Studi Mahasiswa Menggunakan Metode C5.0 pada Studi Kasus Data Kelulusan Mahasiswa Fakultas Matematika Dan Ilmu Pengetahuan Alam Universitas Mulawarman Tahun 2017,†Pros. Semin. Nas. Mat. Stat. dan Apl. 2019, vol. 1, no. 1, pp. 36–42, 2019.

A. C. Wijaya, N. A. Hasibuan, and P. Ramadhani, “Implementasi Algoritma C5 . 0 Dalam Klasifikasi Pendapatan Masyarakat ( Studi Kasus : Kelurahan Mesjid Kecamatan Medan Kota ),†Inf. dan Teknol. Ilm., vol. 13, pp. 192–198, 2018.

M. Pardede, E. Buulolo, and E. Ndruru, “Implementasi Algoritma C5.0 Pada Kelulusan Peserta Ujian Kemahiran Berbahasa Indonesia (Ukbi) Pada Balai Bahasa Sumatera Utara,†KOMIK (Konferensi Nas. Teknol. Inf. dan Komputer), vol. 3, no. 1, pp. 64–72, 2019.

Downloads

Published

2021-07-31

Issue

Vol. 5 No. 3 (2021): Juli 2021

Section

Articles

License

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License

Authors who publish with this journal agree to the following terms:

  1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
  2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
  3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).