Combining fuzzy signature and rough sets approach for predicting the minimum passing level of competency achievement

Yuhana, UL and Fanani, Nurul Zainal and Yuniarno, Eko Mulyanto and Rochimah, Siti and Koczy, Laszlo T and Purnomo, Mauridhi Hery (2020) Combining fuzzy signature and rough sets approach for predicting the minimum passing level of competency achievement. International Journal of Artificial Intelligence (IJAI), 18 (1). pp. 237-249. ISSN 0974-0635

	Text (Full Text) 4. Combining Fuzzy Signature and Rough Sets Approach for Predicting the Minimum Passing Level of Competency Achievement.pdf - Published Version Download (682kB)
	Text (Hasil Cek Similarity) Combining Fuzzy Signature and Rough Sets Approach for Predicting the Minimum Passing Level of Competency Achievement_2.pdf - Supplemental Material Download (2MB)
	Text (Hasil Peer Review) 8. Combining fuzzy signature...pdf - Supplemental Material Restricted to Repository staff only Download (637kB)

Abstract

This paper aims to investigate the important factors that affect the value of the minimum passing level (MPL) of competency achievement and find the best method to predict it. The MPL of competency achievement is the value that represents the minimum passing score of examination related to the competency. Different schools may have a different value of the MPL because the MPL is defined based expert opinion on several uncertainty aspects and conditions at each school. This paper proposes the combination of rough sets and fuzzy signature method to predict the category of the MPL. The rough sets method is applied to reduce unnecessary features for classification and find the important factors to predict the MPL. The fuzzy signature is employed to predict the category of MPL based on the selected features. The method proposed in this paper consists of several stages, namely data collection and pre-processing, features selection, predict the category of the MPL using the combination of rough sets and fuzzy signatures method, and performance evaluation. Fifteen headmasters and sixty teachers of elementary schools participated in the data collection process. Based on the experiment with 203 objects data we achieved 97% accuracy in the prediction of MPL. The proposed method succeeded to identify the important factors on predicting the MPL on the complexity of competency and resource capacity of the school aspect. We obtained the improvement for accuracy of the complexity of competency prediction of 8.5% from the best method in the previous research.

Actions (login required)

View Item