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Penerapan Text Mining untuk Melakukan Clustering Data Tweet Akun Blibli Pada Media Sosial Twitter Menggunakan K-Means Clustering

PENERAPAN TEXT MINING UNTUK MELAKUKAN CLUSTERING DATA TWEET AKUN BLIBLI PADA MEDIA SOSIAL TWITTER MENGGUNAKAN K-MEANS CLUSTERING

*Syiva Multi Fani  -  Departemen Statistika, Fakultas Sains dan Matematika, Universitas Siponegoro, Indonesia
Rukun Santoso  -  Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro, Indonesia
Suparti Suparti  -  Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro, Indonesia
Open Access Copyright 2021 Jurnal Gaussian under http://creativecommons.org/licenses/by-nc-sa/4.0.

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Abstract

Social media is computer-based technology that facilitates the sharing of ideas, thoughts, and information through the building of virtual networks and communities. Twitter is one of the most popular social media in Indonesia which has 78 million users. Businesses rely heavily on Twitter for advertising. Businesses can use these types of tweet content as a means of advertising to Twitter users by Knowing the types of tweet content that are mostly retweeted by their followers . In this study, the application of Text Mining to perform clustering using the K-means clustering method with the best number of clusters obtained from the Silhouette Coefficient method on the @bliblidotcom Twitter tweet data to determine the types of tweet content that are mostly retweeted by @bliblidotcom followers. Tweets with the most retweets and favorites are discount offers and flash sales, so Blibli Indonesia could use this kind of tweet to conduct advertising on social media Twitter because the prize quiz tweets are liked by the @bliblidotcom Twitter account followers.

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Keywords: Advertising, Blibli Indonesia, Clustering, K-means, Silhouette Coefficient, Text Mining, Twitter.

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  1. Afuan, L. 2013. Stemming Dokumen Teks Bahasa Indonesia Menggunakan Algoritma Porter. Jurnal Telematika, Vol. 6 No. 2
  2. Castella, Q. & Sutton, C., 2014. Word Storms: Multiples of Word Clouds for Visual Comparison of Documents. Seoul, International Conference on World Wide Web, Vol. 1
  3. Feldman, R dan Sanger, J. 2007. The Text Mining Handbook. New York: Cambridge University Press
  4. Go, A., Bhayani, R., dan Huang, L. 2009. Twitter Sentiment Classification using Distant Supervision. Stanford: Stanford University
  5. Gupta, V dan Lehal, G. S. 2009. A Survey of Text Mining Techniques and Applications. Jurnal Emerging Technologies in Web Intelligence Vol.1, No.1: Hal 60-7
  6. Handoyo, R., Mangkudjaja, R., & Nasution, S. M. 2014. Perbandingan Metode Clustering menggunakan Metode Single Linkage dan K-means pada Pengelompokan Dokumen. Jurnal Sifo Mikroskil, Vol. 15, No.2, Hal: 73-82
  7. Harjanta, A. J. T. 2015. Preprocessing Text untuk Meminimalisir Kata yang Tidak Berarti dalam Proses Text Mining. Jurnal Informatika UPGRIS Vol. 1
  8. Hootsuite. 2020. Local Insights. https://datareportal.com/reports/digital-2020-indonesia. Diakses: 13 April 2020
  9. Indraloka, D. S. dan Santosa, B. 2017. Penerapan Text Mining untuk Melakukan Clustering Data Tweet Shopee Indonesia. Jurnal Sains dan Seni ITS, Vol. 6, No.2: 2337-3520
  10. Laeli, S. 2014. Analisis Cluster dengan Average Linkage Method and Ward's Method untuk Data Responden Nasabah Asuransi Jiwa Unit Link. Yogyakarta: Universitas Negeri Yogyakarta
  11. Nurhuda, F., Sihwi, S. W., dan Doewes, A. 2013. Analisis Sentimen Masyarakat terhadap Calon Presiden Indonesia 2014 berdasarkan Opini dari Twitter Menggunakan Metode Naive Bayes Classifier. Jurnal IT SMART, Vol. 2, No. 2, Hal: 35-42
  12. Salton, G. dan Buckley, C. 1988. Term-Weighting Approaches in Automatic Text Retrieval. Jurnal Information Processing and Management Vol.24, No. 5, Hal: 512-523
  13. Supranto, 2004. Analisis Multivariat Arti dan Interpretasi. Jakarta: PT. Rineka Cipta
  14. Tessem, B., Bjornestad, S., Chen, W. & Nyre, L., 2015. Word Cloud Visualization of Locative Information. Journal of Location Based Services, Hal. 254-272
  15. Twitter. 2020. Tentang Twitter. www.twitter.com. Diakses: 14 April 2020
  16. Utomo, M. S. 2015. Stopword Dinamis dengan Pendekatan Statistik. Jurnal Informatika Upgris, Vol. 1, No. 2
  17. Wahid, D. H. dan Azhari. 2016. Peringkasan Sentimen Esktraktif di Twitter Menggunakan Hybrid TF-IDF dan Cosine Similarity. Indonesian Journal of Computing and Cybernetics Systems (IJCCS), Vol. 10 , No. 2, Hal: 207-218
  18. Wu, X. dan Kumar, V. 2009. The Top Ten Algorithms in Data Mining. USA: Chapman and Hall/CRC

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