Klasterisasi Penjawab Berdasar Kualitas Jawaban pada Platform Brainly Menggunakan K-Means

Authors

DOI:

https://doi.org/10.32736/sisfokom.v11i2.1314

Keywords:

clustering, brainly, k-means, question answer

Abstract

Brainly is a Community Question Answering (CQA) educational platform that makes it easy for users to find answers based on questions posed by students. Questions from students are often answered quickly by many answerers interested in the field being asked. The number of available answers is the choice of students to be able to receive answers and give a good rating to the answerer. Based on the number of good ratings, an answerer can be said to be an expert in certain subjects. Therefore, this research focuses on finding expert answering groups who have quality answers. K-means clustering is possible to group the answering data into two different clusters. The first cluster is expert users with ten respondents, and The second cluster is a non-expert cluster with 474 respondents. The expert cluster data is expected to help the questioner to be able to ask questions directly to the experts and obtain quality answers. Meanwhile, the number of clusters is determined based on the test results using a silhouette score that obtains a value of 0.971, with the optimal number of clusters being two clusters.

Author Biographies

Puji Winar Cahyo, Universitas Jenderal Achmad Yani Yogyakarta

Informatika

Landung Sudarmana, Universitas Jenderal Achmad Yani Yogyakarta

Informatika

References

Bramastia and E. K. Purnama, “Pengaruh Penggunaan Brainly Terhadap Hasil Belajar Siswa,” J. EPISTEMA, vol. 2, no. 1, 2021.

M. Neshati, Z. Fallahnejad, and H. Beigy, “On dynamicity of expert finding in community question answering,” Inf. Process. Manag., vol. 53, no. 5, pp. 1026–1042, 2017.

A. Diyanati, B. S. Sheykhahmadloo, S. M. Fakhrahmad, M. H. Sadredini, and M. H. Diyanati, “A proposed approach to determining expertise level of StackOverflow programmers based on mining of user comments,” J. Comput. Lang., vol. 61, no. August, p. 101000, 2020.

R. Gharibi and M. Malekzadeh, “Gamified Incentives: A Badge Recommendation Model to Improve User Engagement in Social Networking Websites,” Int. J. Adv. Comput. Sci. Appl., vol. 8, no. 5, pp. 272–278, 2017.

S. Yanovsky, N. Hoernle, O. Lev, and K. Gal, “One size does not fit all: A study of badge behavior in stack overflow,” J. Assoc. Inf. Sci. Technol., vol. 72, no. 3, pp. 331–345, 2021.

L. T. Le, C. Shah, and E. Choi, “Assessing the quality of answers autonomously in community question–answering,” Int. J. Digit. Libr., vol. 20, no. 4, pp. 351–367, 2019.

P. W. Cahyo and M. Habibi, “Entity Profiling to Identify Actor Involvement in Topics of Social Media Content,” IJCCS (Indonesian J. Comput. Cybern. Syst., vol. 14, no. 4, p. 417, 2020.

P. W. Cahyo and L. Sudarmana, “A Comparison of K-Means and Agglomerative Clustering for Users Segmentation based on Question Answerer Reputation in Brainly Platform,” Elinvo (Electronics, Informatics, Vocat. Educ., vol. 6, no. 2, pp. 166–173, 2021.

S. Lyu, Y. Wang, W. Ouyang, H. Shen, and X. Cheng, “What we vote for? Answer selection from user expertise view in community question answering,” Web Conf. 2019 - Proc. World Wide Web Conf. WWW 2019, pp. 1198–1209, 2019.

X. Zhou, B. Hu, Q. Chen, and X. Wang, “Recurrent convolutional neural network for answer selection in community question answering,” Neurocomputing, vol. 274, no. 2017, pp. 8–18, 2018.

Y. Xiang, Q. Chen, X. Wang, and Y. Qin, “Answer Selection in Community Question Answering via Attentive Neural Networks,” IEEE Signal Process. Lett., vol. 24, no. 4, pp. 505–509, 2017.

P. W. Cahyo, K. Kusumaningtyas, and U. S. Aesyi, “A User Recommendation Model for Answering Questions on Brainly Platform,” J. Infotel, vol. 13, no. 1, pp. 7–12, 2021.

N. L. Sowah, Q. Wu, and F. Meng, “A classification and clustering method for tracking multiple objects,” 2018 IEEE 8th Annu. Comput. Commun. Work. Conf. CCWC 2018, vol. 2018-Janua, pp. 537–544, 2018.

P. W. Cahyo and M. Habibi, “Clustering followers of influencers accounts based on likes and comments on Instagram Platform,” IJCCS (Indonesian J. Comput. Cybern. Syst., vol. 14, no. 2, pp. 199–208, 2020.

K. Kafle, M. Yousefhussien, and C. Kanan, “Data augmentation for visual question answering,” INLG 2017 - 10th Int. Nat. Lang. Gener. Conf. Proc. Conf., pp. 198–202, 2017.

N. Duan, D. Tang, P. Chen, and M. Zhou, “Question generation for question answering,” EMNLP 2017 - Conf. Empir. Methods Nat. Lang. Process. Proc., pp. 866–874, 2017.

A. Abujabal, R. S. Roy, M. Yahya, and G. Weikum, “COMQA: A community-sourced dataset for complex factoid question answering with paraphrase clusters,” NAACL HLT 2019 - 2019 Conf. North Am. Chapter Assoc. Comput. Linguist. Hum. Lang. Technol. - Proc. Conf., vol. 1, pp. 307–317, 2019.

P. W. Cahyo, M. Habibi, A. Priadana, and A. B. Saputra, “Analysis of Popular Hashtags on Instagram Account The Ministry of Health,” vol. 34, no. Ahms 2020, pp. 270–273, 2021.

E. Ogasawara, L. C. Martinez, D. De Oliveira, G. Zimbrão, G. L. Pappa, and M. Mattoso, “Adaptive Normalization: A novel data normalization approach for non-stationary time series,” Proc. Int. Jt. Conf. Neural Networks, 2010.

M. D. J. Bora and D. A. K. Gupta, “Effect of Different Distance Measures on the Performance of K-Means Algorithm: An Experimental Study in Matlab,” vol. 5, no. 2, pp. 2501–2506, 2014.

R. I. Fajriah, H. Sutisna, and B. K. Simpony, “Perbandingan Distance Space Manhattan Dengan Euclidean Pada K-Means Clustering Dalam Menentukan Promosi,” Inform. Bina Sarana Bsi, Univ., vol. 4, no. 1, pp. 36–49, 2019.

P. W. Cahyo, “Klasterisasi Tipe Pembelajar Sebagai Parameter Evaluasi Kualitas Pendidikan Di Perguruan Tinggi,” Teknomatika, vol. 11, no. 1, pp. 49–55, 2018.

Downloads

Published

2022-08-02

Issue

Section

Articles