A Comparison of K-Means and Agglomerative Clustering for Users Segmentation based on Question Answerer Reputation in Brainly Platform

Authors

Puji Winar Cahyo, Universitas Jenderal Achmad Yani Yogyakarta
Landung Sudarmana, Universitas Jenderal Achmad Yani Yogyakarta, Indonesia

Keywords

k-means, agglomerative clustering, question answerer, brainly, clustering

Document Type

Article

Abstract

Brainly is a question and answer (Q&A) site that students can use as a media for questions and answers. Students can also use Brainly to find and share educational information that helps students solve their homework problems. In Brainly, users can answer questions according to their interests. However, it could be that the interest is not necessarily following the competencies possessed. It causes many answers to the questions given not to have a high rating because the answers given are of low quality to be prioritized as the main answer. This study aims to apply the K-Means and Agglomerative Clustering methods to segment users based on the reputation of the answerers by conducting clustering based on track records in answering questions on mathematics subjects. This study used the number of the brightest scores and the number of answers that did not get a rating as the basic features for clustering. The comparison between the two methods used is based on the Silhouette Score, representing the quality of the clustering results, calculated by applying the Silhouette Coefficient method. This study result indicates that the K-Means method gives better results than the Agglomerative Clustering. The Silhouette Score generated by the K-Means method is higher at 0.9081 than the Agglomerative Clustering method, which is 0.8990, which produces two clusters or two segments.

First Page

166

Last Page

173

Page Range

166-173

Issue

2

Volume

6

Digital Object Identifier (DOI)

10.21831/elinvo.v6i2.44486

Source

https://journal.uny.ac.id/index.php/elinvo/article/view/44486

Recommended Citation

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,", vol. 6, no. 2, pp. 166 - 173, Dec 2021.

The definitive version is available at https://doi.org/10.21831/elinvo.v6i2.44486

References

H. Fu and S. Oh, "Quality assessment of answers with user-identified criteria and data-driven features in social Q&A," Inf. Process. Manag., vol. 56, no. 1, pp. 14-28, Jan. 2019.

Z. Liu and B. J. Jansen, "Identifying and predicting the desire to help in social question and answering," Inf. Process. Manag., vol. 53, no. 2, pp. 490-504, Mar. 2017.

I. Adaji and J. Vassileva, "Susceptibility of users to social influence strategies and the influence of culture in a Q&A collaborative learning environment," in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2017, vol. 10391 LNCS, pp. 49-64.

K. Sagan, J. Colby, S. Y. Rieh, and E. Choi, "Beyond questioning and answering: Teens' learning experiences and benefits of social Q&A services," in CSCW 2017 - Companion of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing, 2017, pp. 295-298.

A. Ayuningtyas, A. S. Honggowibowo, S. Mulyani, and A. Priadana, "A Web-Based Aircraft Maintenance Learning Media to Support Learning Process in Aerospace Engineering Education during the COVID-19 Pandemic," in Proceeding - 2020 Sixth International Conference on e-Learning (econf), 2020, pp. 55-60.

M. Li, Y. Li, Y. Lu, and Y. Zhang, "Evaluating Indicators of Answer Quality in Social Q&A Websites," PACIS 2019 Proc., Jun. 2019.

E. Choi et al., "Utilizing content moderators to investigate critical factors for assessing the quality of answers on brainly, social learning Q&A platform for students: A pilot study," Proc. Assoc. Inf. Sci. Technol., vol. 52, no. 1, pp. 1-4, Jan. 2015.

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, 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.

Y. Christian and J. Jimmy, "Perancangan Model Predıksı Performa Akademık Mahasıswa Menggunakan Algorıtma K-Means Clusterıng (Studı Kasus: Unıversıtas Xyz)," Mar. 2021.

Y. H. Chrisnanto and G. Abdullah, "The uses of educational data mining in academic performance analysis at higher education institutions (case study at UNJANI)," Matrix J. Manaj. Teknol. dan Inform., vol. 11, no. 1, pp. 26-35, Mar. 2021.

S. Dewi, S. Defit, and Y. Yunus, "Akurasi Pemetaan Kelompok Belajar Siswa Menuju Prestasi Menggunakan Metode K-Means (Studi Kasus SMP Pembangunan Laboratorium UNP)," J. Sistim Inf. dan Teknol., pp. 28-33, Sep. 2020.

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

D. Exasanti and A. Jananto, "Analisa Hasil Pengelompokan Wilayah Kejadian Non-Kebakaran Menggunakan Agglomerative Hierachical Clustering di Semarang," J. Tekno Kompak, vol. 15, no. 2, pp. 63-75, Aug. 2021.

L. Zahrotun, "ANALISIS PENGELOMPOKAN JUMLAH PENUMPANG BUS TRANS JOGJA MENGGUNAKAN METODE CLUSTERING K-MEANS DAN AGGLOMERATIVE HIERARCHICAL CLUSTERING (AHC)," J. Inform., vol. 9, no. 1, Jan. 2015.

R. O. Pratikto and N. Damastuti, "Klasterisasi Menggunakan Agglomerative Hierarchical Clustering Untuk Memodelkan Wilayah Banjir," JOINTECS (Journal Inf. Technol. Comput. Sci., vol. 6, no. 1, pp. 13-20, Jan. 2021.

Z. Arifin, S. Santosa, and M. A. Soeleman, "KLASTERISASI GENRE CERPEN KOMPAS MENGGUNAKAN AGGLOMERATIVE HIERARCHICAL CLUSTERING- SINGLE LINKAGE," J. Cyberku, vol. 13, no. 2, pp. 2-2, Dec. 2017.

R. C. Pereira and T. Vanitha, "Web Scraping of Social Networks," Int. J. Innov. Res. Comput. Commun. Eng., vol. 3, no. 7, pp. 237-240, 2015.

Fatmasari, Y. N. Kunang, and S. D. Purnamasari, "Web Scraping Techniques to Collect Weather Data in South Sumatera," in Proceedings of 2018 International Conference on Electrical Engineering and Computer Science, ICECOS 2018, 2019.

M. R. E. Waluyo, P. Y. Saputra, and H. E. Dien, "KLASTERISASI WILAYAH TANAH LONGSOR BERDASARKAN DAMPAK WILAYAH DAN GEOGRAFIS MENGGUNAKAN METODE K-MEANS (Studi Kasus : Kabupaten dan Kota di Jawa Timur)," Semin. Inform. Apl. Polinema, Oct. 2020.

U. A. Nasron and M. Habibi, "Analysis of Marketplace Conversation Trends on Twitter Platform Using K-Means," Compiler, vol. 9, no. 1, pp. 51-62, May 2020.

A. I. Abdullah, E. Winarko, and A. Musdholifah, "Metode Boost-K-means untuk Clustering Puskesmas berdasarkan Persentase Bayi yang Diimunisasi," JRST (Jurnal Ris. Sains dan Teknol., vol. 4, no. 2, p. 89, Nov. 2020.

Suyanto, Data Mining Untuk Klasifikasi dan Klasterisasi Data. Bandung: Informatika, 2017.

I. H. Witten, E. Frank, M. A. Hall, and C. J. Pal, Data Mining: Practical Machine Learning Tools and Techniques, Fourth Edi. Cambridge, MA: Morgan Kaufmann Publishers, 2016.

W. K. Härdle and L. Simar, Applied Multivariate Statistical Analysis, Fifth Edit. Springer, 2019.

A. I. Abdullah, A. Priadana, M. Muhajir, and S. N. Nur, "Data Mining for Determining The Best Cluster Of Student Instagram Account As New Student Admission Influencer," Telemat. J. Inform. dan Teknol. Inf., vol. 18, no. 2, pp. 255-266, Oct. 2021.

H. L. Sun, K. P. Liang, H. Liao, and D. B. Chen, "Evaluating user reputation of online rating systems by rating statistical patterns," Knowledge-Based Syst., vol. 219, p. 106895, 2021.