Pemodelan Prediksi Predikat Kelulusan Mahasiswa Menggunakan Fuzzy C-Means Berbasis Particle Swarm Optimization

Hardi Jamhur

Abstract

Analysis and excavation of information on educational data is an inseparable part of organizing student academic systems in universities. The excavation of information is known as Educational Data mining (EDM), which is a discipline that focuses on the application of techniques and data mining devices specifically in the field of education. One of the EDM processes is related to the need for prediction especially to gain knowledge regarding the progress of student studies. Practically the activity of getting information / knowledge about the progress of student studies is difficult to do conventionally considering the size of the data volume is quite large. The approach to data mining clustering on student study progress data for prediction of predicate graduation tends to be not optimal. Fuzzy C-means (FCM) modeling optimized with Particle Swarm Optimization (PSO) can produce a more optimal prediction performance. In the case of prediction of student graduation prediction, the application of PSO-based FCM algorithm model produces more optimal results, with predictive accuracy of 86% while the FCM algorithm modeling is 79%.

Keywords

clustering; Fuzzy c-means; Particle Swarm Optimization; prediction

References

A. Abraham, C. Grosan, and V. Ramos, Swarm Intelligence in Data Mining. Verlag Berlin Heidelberg: Springer, 2006.

A. Naba, Belajar Cepat Fuzzy Logic Menggunakan MATLAB. Yogyakarta: CV. ANDI OFFSET, 2009.

B. Santosa and P. Willy, Metoda dan Metaheuristik: Konsep dan Implementasi. Surabaya: Guna Widya, 2011.

E. Mehdzadeh, S. Sadi_nezhad, and R. Tavakkoli-moghaddam, “Optimization of Fuzzy Clustering Criteria By a Hybrid PSO and Fuzzy c-means Clustering Algorithm,” vol. 5, no. 3, pp. 1–14, 2008.

F. Akbar, “Clustering Data Nilai Siswa SMA Untuk Penentuan Jurusan Menggunakan Algoritma Fuzzy C-Means,” vol. XII, no. 1, 2015.

F. Gorunescu, Data Mining: Concepts, models and techniques. Springer-Verlag Berlin Heidelberg, 2011.

H. Izakian, A. Abraham, and V. Snasel, “Fuzzy Clustering Using Hybrid Fuzzy C-Means and Fuzzy Particle Swarm Optimization,” in Proceeding of World Congress on Nature and Biologically Inspired Computing (NaBIC 2009), 2009.

J. Han and M. Kamber, Data Mining: Concepts and Techniques, vol. 54, no. Second Edition. 2006.

J. P. Campbell, P. B. DeBlois, and D. G. Oblinger, “Academic Analytics: A New Tool for a New Era,” Educ. Rev., vol. 42, no. October, pp. 40–57, 2007.

J. Yan, Michael, and J. Power, Using Fuzzy Logic (Toward Intelligent Systems). New York: Prentice-Hall, 1994.

L. Rusdiana, “APLIKASI BERBASIS FUZZY C-MEANS DALAM PENENTUAN,” vol. 2, no. 2, pp. 1–9, 2016.

M. Clerc, From Theory to Practice in Particle Swarm Optimization in B.K. Handbook of Swarm Intelligence: Concepts, Principles and Applications. Berlin: Springer, 2011.

M. Clerc, Particle Swarm Optimization. London: ISTE Ltd, 2006.

Q. Bai, “Analysis of Particle Swarm Optimization Algorithm,” vol. 3, no. 1, pp. 180–184, 1998.

Sumanto and R. S. Wahono, “PENERAPAN FUZZY C-MEANS DALAM PEMILIHAN,” vol. 1, no. 1, 2011.

R. A. Huebner, “A survey of educational data-mining research,” pp. 1–13, 2012.

R. Saxena, “Educational Data Mining : Performance Evaluation of Decision Tree and,” vol. 14, no. April, pp. 1–10, 2015.

R. S. J. D. Baker and K. Yacef, “The State of Educational Data Mining in 2009: A Review and Future Visions,” J. Educ. Data Min., vol. 1, no. 1, pp. 3–16, 2009.

R. Xu and D. C. Wunsch, Clustering. 2008.

S. Kusumadewi and H. Purnomo, Aplikasi Logika Fuzzy untuk Pendukung Keputusan, 2nd ed. Yogyakarta: Graha Ilmu, 2013.

S. R. Rao, Engineering Optimization: Theory and Practice 4th Edition. Hoboken - New Jersey: John Wiley & Sons Inc, 2009.

W. M. Rand, “Objective Criteria for the Evaluation of Clustering Methods,” J. Am. Stat. Assoc., vol. 66, no. 336, pp. 846–850, Dec. 1971.

W. Pang, K. Wang, C. Zhou, and L. Dong, “Fuzzy Discrete Paricle Swarm Optimization For Solving Traveling Salesman Problem,” in Proceeding of the fourth international conference on computer and information technology, 2004, pp. 796–800.

Y. Kusnadi, “PREDIKSI TINGKAT KELULUSAN SISWA ELEARNING BERBASIS ALGORITMA FUZZY C-MEANS,” vol. 15, no. 1, pp. 1–6, 2018.

DOI: https://doi.org/10.36350/jbs.v10i1.79

Article metrics

Abstract views : 634 | views : 571

Refbacks

  • There are currently no refbacks.