Blackbox Testing Using Fuzzy Clustering Based on Boundary Value Analysis on The Text Opinion Mining Application in Traditional Culture Arts Presentation

Anis Zubair, Elta Sonalitha, Salnan Ratih, Bambang Nurdewanto, Kukuh Yudhistiro, Irfan Mujahidin

Abstract


The success of organizing a traditional work of art cannot be separated from the important role of data and information obtained from the public in general, and viewers or art connoisseurs in particular. This information is an indicator that can be used to measure the amount of public attention to traditional arts, which is an effort to promote traditional cultural arts. Data and information related to traditional artworks were obtained from filling out the instruments that were distributed to the public online to produce an opinion form that contained a complete description with a discussion containing the aesthetic of the artwork. Opinion data is needed as a measure of progress and preservation of a work of art. The linguistic measurement of opinion can be solved using fuzzy methods in a cryptic form that can be weighted. In this study, the authors tested the audience opinion text mining application on the presentation of traditional cultural artworks using fuzzy clustering using the functional testing method (Black box testing). Through this test will be discussed related to the menu or module to produce information.


Keywords


text mining, fuzzy clustering, art, black-box testing

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References


Bambang, N. et al. (2019) ‘Market matching online to recommend MSME export products destination by using fuzzy control’, Pertanika Journal of Science and Technology, 27(1), pp. 69–79.

Baradaran, A. A. and Navi, K. (2020) ‘HQCA-WSN: High-quality clustering algorithm and optimal cluster head selection using fuzzy logic in wireless sensor networks’, Fuzzy Sets and Systems. doi: 10.1016/j.fss.2019.11.015.

Bharill, N. et al. (2020) ‘Fuzzy knowledge based performance analysis on big data’, Neurocomputing. doi: 10.1016/j.neucom.2018.10.088.

Cao, J. et al. (2020) ‘Integrating Multisourced Texts in Online Business Intelligence Systems’, IEEE Transactions on Systems, Man, and Cybernetics: Systems. doi: 10.1109/TSMC.2017.2710161.

Charwand, M. et al. (2020) ‘Clustering of electrical load patterns and time periods using uncertainty-based multi-level amplitude thresholding’, International Journal of Electrical Power and Energy Systems. doi: 10.1016/j.ijepes.2019.105624.

Gan, H. (2019) ‘Safe Semi-Supervised Fuzzy C -Means Clustering’, IEEE Access. doi: 10.1109/ACCESS.2019.2929307.

He, H. and Tan, Y. (2020) ‘Unsupervised Classification of Multivariate Time Series Using VPCA and Fuzzy Clustering with Spatial Weighted Matrix Distance’, IEEE Transactions on Cybernetics. doi: 10.1109/TCYB.2018.2883388.

Kim, E. H. et al. (2020) ‘Reinforced fuzzy clustering-based ensemble neural networks’, IEEE Transactions on Fuzzy Systems. doi: 10.1109/TFUZZ.2019.2911492.

Al Kindhi, B. et al. (2019) ‘Hybrid K-means, fuzzy C-means, and hierarchical clustering for DNA hepatitis C virus trend mutation analysis’, Expert Systems with Applications. doi: 10.1016/j.eswa.2018.12.019.

De La Rosa, E. and Yu, W. (2020) ‘Data-Driven Fuzzy Modeling Using Restricted Boltzmann Machines and Probability Theory’, IEEE Transactions on Systems, Man, and Cybernetics: Systems. doi: 10.1109/TSMC.2018.2812156.

Li, Y. et al. (2019) ‘Fuzzy identity-based data integrity auditing for reliable cloud storage systems’, IEEE Transactions on Dependable and Secure Computing. doi: 10.1109/TDSC.2017.2662216.

Lu, Z. et al. (2008) ‘Index of cluster validity based on modal logic’, Jisuanji Yanjiu yu Fazhan/Computer Research and Development.

Maji, P. and Mahapatra, S. (2020) ‘Circular Clustering in Fuzzy Approximation Spaces for Color Normalization of Histological Images’, IEEE Transactions on Medical Imaging. doi: 10.1109/TMI.2019.2956944.

Meng, X. et al. (2020) ‘Fuzzy min-max neural network with fuzzy lattice inclusion measure for agricultural circular economy region division in heilongjiang province in china’, IEEE Access. doi: 10.1109/ACCESS.2020.2975561.

Nurdewanto, B. et al. (2020) ‘Taxonomy of Artist and Art Works Using Hybrid TF-IDF Fuzzy C-Means Clustering’, 29(03), pp. 12066–12075.

Seyedzadeh, A. et al. (2020) ‘Artificial intelligence approach to estimate discharge of drip tape irrigation based on temperature and pressure’, Agricultural Water Management. doi: 10.1016/j.agwat.2019.105905.

Sonalitha, E. et al. (2020) ‘Combined Text Mining : Fuzzy Clustering for Opinion Mining on the Traditional Culture Arts Work’, 11(8), pp. 294–299.

Xu, G. et al. (2019) ‘Sentiment analysis of comment texts based on BiLSTM’, IEEE Access. doi: 10.1109/ACCESS.2019.2909919.

Zhao, Z. X. and Cheng, L. Z. (2011) ‘Fuzzy piecewise smooth image segmentation model and a fast algorithm’, Guangdianzi Jiguang/Journal of Optoelectronics Laser.




DOI: https://doi.org/10.31098/ic-smart.v1i1.21

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