Implementasi Teknik Data Mining untuk Evaluasi Kinerja Mahasiswa Berdasarkan Data Akademik

Authors

  • Gita Indah Marthasari Universitas Muhammadiyah Malang

DOI:

https://doi.org/10.21111/fij.v2i2.1216

Keywords:

masa studi, algoritma apriori, algoritma simple expectation maximization, data mining untuk pendidikan, WEKA

Abstract

One indicator of college efficiency is the study period of the students. It is important for university managers to improve the ratio of students who graduate on time. This research aims to analyze the characteristics that affect the study period of students from academic data. The methods used are association rule mining (ARM) and clustering. We propose a framework to analyze academic data using ARM dan clustering method. ARM method is a method to find association rules that meet minimum support and minimum confidence. The algorithm used is Apriori. While clustering using Simple Expectation-Maximization (EM-clustering) algorithm. Simple EM is a model-based algorithm that searches for maximum likelihood estimation in the probability model. The variables analyzed were student achievement index, province of the students, and type of high school. The analysis is done using WEKA. Research begins with the collection of data from the primary source of the Biro Administrasi Akademik (BAA) Universitas Muhammadiyah Malang (UMM). Then, we do the data cleaning and transformation. Analyzing process is done in two step. First, do a rule search using Apriori algorithm. The regulated parameters are the minimum support and minimum confidence value. Second, we use Simple EM algorithm for the clustering process. The experiments were conducted to find the clustering result with the largest log likelihood value. Based on the experiment, the method used successfully describes the characteristics based on the study period.

References

B. A. N. Indonesia, “Peraturan Badan Akreditasi Nasional PT No 4/2017,” 2017.

A. El-Halees, “Mining Students Data To Analyze Learning Behavior : a Case Study Educational Systems,” Work, no. February, 2008.

S. Ahmed, R. Paul, A. Sayed, and L. Hoque, “Knowledge Discovery from Academic Data using Association Rule Mining,” no. December, pp. 22–23, 2014.

M. I. Al-Twijri and A. Y. Noaman, “A New Data Mining Model Adopted for Higher Institutions,” in Procedia Computer Science, 2015, vol. 65, pp. 836–844.

S. Baher and L. L.M.R.J., “Data Preparation Strategy in E-Learning System using Association Rule Algorithm,” Int. J. Comput. Appl., vol. 41, no. 3, pp. 35–40, 2012.

K. Kularbphettong and C. Tongsiri, “Mining Educational Data to Analyze the Student Motivation Behavior,” World Acad. Sci. Eng. Technol., vol. 6, no. 8, pp. 1036–1040, 2012.

L. Talavera and E. Gaudioso, “Mining student data to characterize similar behavior groups in unstructured collaboration spaces,” Work. Artif. Intell. CSCL. 16th Eur. Conf. Artif. Intell., pp. 17–23, 2004.

M. M. A. Tair and A. M. El-Halees, “Mining Educational Data to Improve Students’ Performance: A Case Study,” Int. J. Inf. Commun. Technol. Res., vol. 2, no. 2, pp. 140–146, 2012.

G. I. Marthasari, “Identifikasi Faktor Ketidak-aktifan Mahasiswa menggunakan Teknik Data Mining,” 2016.

M. H. Meinanda, M. Annisa, N. Muhandri, and K. Suryadi, “Prediksi Masa Studi Sarjana dengan Artificial Neural Network,” Internetworking Indones. J., vol. 1, no. 2, pp. 31–35, 2009.

A. Jananto, “Algoritma Naive Bayes untuk Mencari Perkiraan Waktu Studi Mahasiswa,” J. Teknol. Inf. Din., vol. 18, no. 1, pp. 9–16, 2013.

S. Haryati, A. Sudarsono, and E. Suryana, “IMPLEMENTASI DATA MINING UNTUK MEMPREDIKSI MASA STUDI MAHASISWA MENGGUNAKAN ALGORITMA C4 . 5,” Media Infotama, vol. 11, no. 2, pp. 130–138, 2015.

S. H. Hasan, N. Aziz, and M. Adam, “PENGARUH LINGKUNGAN KERJA TERHADAP KINERJA AKTIVIS PADA LEMBAGA SWADAYA MASYARAKAT DI KOTA BANDA ACEH,” Ilmu Manaj., vol. 1, no. 1, pp. 2–22, 2012.

W. E. Pratiwi, “PENGARUH BUDAYA JAWA TERHADAP ASERTIVITAS PADA REMAJA SISWA KELAS XDI SMA NEGERI 3,” Psikologi, vol. 3, no. 1, pp. 348–357, 2015.

N. Yuliana, “Pengaruh Pendekatan Differentiated Instruction (DI) Terhadap Kecemasan Matematika (Match Anxiety), Peningkatan Kemampuan Pamahaman Dan Penalaran Matematis Siswa SMK,” Universitas Pendidikan Indonesia, 2013.

N. Azizah, “Perilaku Moral dan Religiusitas Siswa Berlatar Belakang Pendidikan Umum dan Agama,” Psikologi, vol. 33, no. 2, pp. 1–8, 2003.

M. Manvar and M. Rao, “Predicting students performance in higher education: A Data Mining Approach,” Ijser.Org, vol. 5, no. 2, pp. 1024–1027, 2014.

S. Kotsiantis and D. Kanellopoulos, “Association Rules Mining: A Recent Overview,” GESTS Int. Trans. Comput. Sci. Eng., vol. 32, no. 1, pp. 71–82, 2006.

V. Kumar and A. Chadha, “Mining association rules in student’s assessment data,” Int. J. Comput. Sci. Issues, vol. 9, no. 5, pp. 211–216, 2012.

M. Meila and D. Heckerman, “An Experimental Comparison of Model-Based Clustering Method,” Mach. Learn., vol. 1225, no. January 2001, pp. 41–42, 2014.

Downloads

Submitted

2017-10-19

Accepted

2017-11-01

Published

2017-11-10

Issue

Section

Articles