Classification Modelling of Random Forest to Identify the Important Factors in Improving the Quality of Education
How to cite (IJASEIT) :
Indonesian government, “National Education Standards (SNP),” 2005.
Indonesian government, Permendikbud No.004/H/AK/2017, “Criteria and Instrument Accreditations for SMA/MA,” 2017.
Indonesian government, Permendikbud No. 3 of 2017, “Educational Assessment by The Government and Schools,” 2017.
D. Vita, B. Susetyo, and B. Indriyanto, “Generalized Structured Component Analysis (GSCA) for National Education Standards (NES) of Secondary School In Indonesia,” Global Journal of Pure and Applied Mathematics, vol. 11, pp. 2441-2449, Apr 2015.
M. Hijrah, B. Susetyo, and B. Sartono, “Structural Equation Modeling of National Standard Education of Vocational High School Using Partial Least Square Path Modeling,” IJSRSET, vol. 4, pp. 1418-1422, Apr. 2018.
I. A. Setiawan, B. Susetyo, and A. Fitrianto, “Application of Generalized Structural Component Analysis to Identify Relation between Accreditation and National Assessment,” IJSRSET, vol. 4, pp 93-97, Oct 2018.
L. Breiman, “Random Forest,” Machine Learning, vol. 45, pp. 5-32, Apr 2001.
Q. Yanjun. (2017) The CMU website. [Online]. Available: www.cs.cmu.edu/~qyj/papersA08/11-rfbook.pdf.
C. Bunkhumpornpat, K. Sinapiromsaran, and C. Lursinsap, ”DBSMOTE: Density-based Synthetic Minority Over-Sampling Technique,” Application Intelligence, vol. 36, pp. 664-684. Mar. 2012.
J. Brownlee. (2015) Machine Learning Process homepage on machinelearningmastery. [Online]. Available: https://machinelearningmastery.com/tactics-to-combat-imbalanced-classes-in-your-machine-learning-dataset/.
N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer, “{SMOTE}: Synthetic Minority Over-Sampling Technique,” Journal of Atrificial Intelligence Research, vol. 9, pp. 321-357, Jun 2002.
S. Cost and S. Salzberg S, “A Weighted Neighbour Algorithm for Learning with Symbolic Features,” Machine Learning, vol. 10, pp. 57-58, Jan. 1993.
M. N. Adnan and M. Z. Islam, “One-vs-all binarization technique in the context of random forest,” Computational. Intelligence and Machine Learning, vol. 5, pp. 385-390, Apr 2015.
L. Zhou, Q. Wang, and H. Fujita, “One versus one multi-class classification fusion using optimizing decision directed acyclic graph for predicting listing status of companies,” Information Fusion, vol. 36, pp 80-89, Nov. 2016.
E. Hullermeier and S. Vanderlooy S, “Combining predictions in pairwise classification : An optimal adaptive voting strategy and its relation to weighted voting,” Pattern Recognit, vol. 43 pp. 128-142, Jan. 2010.
A. Sen, M. M. Islam, K. Murase, and X. Yao. (2015) IEEEtran homepage on CS.BHAM. [Online]. Available: http://www.cs.bham.ac.uk/~xin/papers/Binarization.pdf.
M. Sandri and P. Zuccolotto, Data Analysis, Classification and the Forward Search, Zani S., cccc A., M. Riani, and M. Vichi., Ed. Berlin, Germany: Springer, 2006.
P. Probst, M. Wright, and A-L. Boulesteix. (2018) The ARXIV website. [Online]. Available: https://arxiv.org/pdf/1804.03515.pdf.
T. M. Oshiro, P. S. Perez, and J. A. Baranauskas, “How many trees in a random forest?” in Machine Learning and Data Mining in Pattern Recognition: 8th International Conference, 2012, paper Proceedings, vol. 7376, p. 154.
P. Probst and A-L. Boulesteix. (2017) The ARXIV website. [Online]. Available: https://arxiv.org/pdf/1609.06146.pdf.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- 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 acknowledgement of its initial publication in this journal.
- 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 (See The Effect of Open Access).