ANALYSIS OF MARKETPLACE CONVERSATION TRENDS ON TWITTER PLATFORM USING K-MEANS

Ulil Amri Nasron, Muhammad Habibi

Abstract

Businesses began to shift from the marketing process that used to use conventional media to switch to using the internet and social media. This is because the cost of marketing using the internet and social media is cheaper than using conventional media. The problem that is often faced by businesspeople when marketing on social media is that they rarely see a marketplace that is becoming a trend and is being discussed by consumers on social media, so the marketing process is carried out less than the maximum. This study aims to analyze conversation trends related to the marketplace on the Twitter platform. The method used in this study is the K-Means Clustering method. Based on the results of the study found that the application of the K-Means Clustering method can produce sufficient information as a basis for consideration of businesspeople in choosing a marketplace. Marketplace trend analysis results show that Shopee, Lazada, and Tokopedia are highly discussed marketplaces on Twitter.

Keywords

Marketplace; Text Mining; K-Means; Twitter; Clustering

References

Sunyoto, D. (2011). Analisis Regresi dan Uji Hipotesis. Yogyakarta: Caps Publishing.

Databoks. (2019). Berapa Pengguna Media Sosial Indonesia? Retrieved November 30, 2019, from https://databoks.katadata.co.id/datapublish/2019/02/08/berapa-pengguna-media-sosial-indonesia

Clinten, B. (2019). Pengguna Aktif Harian Twitter Indonesia Diklaim Terbanyak. Retrieved November 30, 2019, from https://tekno.kompas.com/read/2019/10/30/16062477/pengguna-aktif-harian-twitter-indonesia-diklaim-terbanyak

Habibi, M. (2018). Analisis Konten Jejaring Sosial Twitter dalam Kasus Pemilihan Gubernur DKI 2017. Teknomatika, 11(1), 31–40.

Cahyo, P. W. (2017). Model Monitoring Sebaran Penyakit Demam Berdarah di Indonesia Berdasarkan Analisis Pesan Twitter. Universitas Gadjah Mada Yogyakarta.

Habibi, M., & Cahyo, P. W. (2019). Clustering User Characteristics Based on the influence of Hashtags on the Instagram Platform. IJCCS (Indonesian Journal of Computing and Cybernetics Systems), 13(4), 399–408. https://doi.org/10.22146/ijccs.50574

Cahyo, P. W. (2018). Klasterisasi Tipe Pembelajar Sebagai Parameter Evaluasi Kualitas Pendidikan di Perguruan Tinggi. Teknomatika, 11(1), 49–55.

Maulida, L. (2018). Kunjungan Wisatawan Ke Objek Wisata Unggulan Di Prov . Dki Jakarta Dengan K-Means. JISKa, 2(3), 167–174.

Kelik Nugroho, A. (2018). Image Quantization in Psoriasis Using K-Mean Clustering. In Prosiding Seminar Nasional Teknologi Informasi dan Kedirgantaraan (SENATIK) (Vol. IV, pp. 183–189). https://doi.org/10.1017/CBO9781107415324.004

Habibi, M., & Sumarsono. (2018). Implementation of Cosine Similarity in an automatic classifier for comments Program. JISKa (Jurnal Informatika Sunan Kalijaga), 3(2), 38–46. https://doi.org/http://dx.doi.org/10.14421/jiska.2018.32-05

Sebastiani, F. (2002). Machine Learning in Automated Text Categorization. ACM Computing Surveys (CSUR), 34(1), 1–47.

Habibi, M. (2017). Analisis Sentimen dan Klasifikasi Komentar Mahasiswa pada Sistem Evaluasi Pembelajaran Menggunakan Kombinasi KNN Berbasis Cosine Similarity dan Supervised Model. Departemen Ilmu Komputer dan Elektronika, Fakultas Matematika dan Ilmu Pengetahuan Alam. Universitas Gadjah Mada.

Kalra, V., & Aggarwal, R. (2018). Importance of Text Data Preprocessing & Implementation in RapidMiner. Proceedings of the First International Conference on Information Technology and Knowledge Management, 14, 71–75. https://doi.org/10.15439/2017km46

Siqueira, H., & Barros, F. (2010). A Feature Extraction Process for Sentiment Analysis of Opinions on Services. Proceedings of the III International Workshop on Web and Text Intelligence (WTI).

Manning, C. D., Raghavan, P., & Schutze, H. (2009). An Introduction to Information Retrieval. Cambridge, England: Cambridge University Press. https://doi.org/10.1109/LPT.2009.2020494

Nisha, N., & Jai Kaur, P. (2015). A Survey of Clustering Techniques and Algorithms. 2nd International Conference on Computing for Sustainable Global Development (INDIACom), 3014–3017.

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