Sentimen Analisis pada Data Tweet Pengguna Twitter Terhadap Produk Penjualan Toko Online Menggunakan Metode K-Means
DOI:
https://doi.org/10.30812/matrik.v19i2.640Keywords:
SENTIMEN ANALISIS, Tweet, Toko online, Komentar konsumen, K-MeansAbstract
In this big data era, the use of social media often makes posts in his social media accounts in the form of opinions on events and things around him. One of them is making a post that gives an opinion on the events and items around it. One of them is making a post that gives an opinion on an item that has just been purchased, so that the effect is on other users who are connected to it. The more people who know it, then indirectly people will get to know the item. For that from the description of the problem above, this study raises an idea to make an analysis of social media sentiment which aims to provide a decision of consumer opinion on social media on sales products. As for the several stages of the method for the research, namely from the collection of data carried out by collecting existing data in tweets from social media Twitter using the R programming language. The data produces raw or raw data associated with sales items. With the K-means method as inputting, after each group is known from the K-Means output
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References
[1] C. Gallagher, E. Furey, and K. Curran, “The application of sentiment analysis and text analytics to customer experience reviews to understand what customers are really saying,†Int. J. Data Warehous. Min., vol. 15, no. 4, pp. 21–47, 2019, doi: 10.4018/IJDWM.2019100102.
[2] S. A. F. Alvi Pranandha Syah, Adiwijaya, “Analisis Sentimen Pada Data Ulasan Produk Toko Online Dengan Metode Maximum Entropy Sentiment Analysis on Online Store Product Reviews With Maximum,†e-Proceeding Eng., vol. 4, no. 3, pp. 4632–4640, 2017.
[3] Y. W. Syaifudin and R. A. Irawan, “Implementasi Analisis Clustering Dan Sentimen Data Twitter Pada Opini Wisata Pantai Menggunakan Metode K-Means,†J. Inform. Polinema, vol. 4, no. 3, p. 189, 2018, doi: 10.33795/jip.v4i3.205.
[4] M. H. Siregar, “Data Mining Klasterisasi Penjualan Alat-Alat Bangunan Menggunakan Metode K-Means (Studi Kasus Di Toko Adi Bangunan),†J. Teknol. Dan Open Source, vol. 1, no. 2, pp. 83–91, 2018, doi: 10.36378/jtos.v1i2.24.
[5] G. M. J. E. A. F. T. H. R. N. D. Delen, Practical Text Mining and Statistical Analysis for Non-structured Text Data Applications, 1st ed. American: Academic Press, 2012.
[6] J. W. Creswell, Education Research. Planning, coduction and evaluating quantitative and qualitative research, 4th ed. Boston: Pearson, 2012.
[2] S. A. F. Alvi Pranandha Syah, Adiwijaya, “Analisis Sentimen Pada Data Ulasan Produk Toko Online Dengan Metode Maximum Entropy Sentiment Analysis on Online Store Product Reviews With Maximum,†e-Proceeding Eng., vol. 4, no. 3, pp. 4632–4640, 2017.
[3] Y. W. Syaifudin and R. A. Irawan, “Implementasi Analisis Clustering Dan Sentimen Data Twitter Pada Opini Wisata Pantai Menggunakan Metode K-Means,†J. Inform. Polinema, vol. 4, no. 3, p. 189, 2018, doi: 10.33795/jip.v4i3.205.
[4] M. H. Siregar, “Data Mining Klasterisasi Penjualan Alat-Alat Bangunan Menggunakan Metode K-Means (Studi Kasus Di Toko Adi Bangunan),†J. Teknol. Dan Open Source, vol. 1, no. 2, pp. 83–91, 2018, doi: 10.36378/jtos.v1i2.24.
[5] G. M. J. E. A. F. T. H. R. N. D. Delen, Practical Text Mining and Statistical Analysis for Non-structured Text Data Applications, 1st ed. American: Academic Press, 2012.
[6] J. W. Creswell, Education Research. Planning, coduction and evaluating quantitative and qualitative research, 4th ed. Boston: Pearson, 2012.
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Published
2020-05-30
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Articles
How to Cite
[1]
A. Faesal, A. Muslim, A. H. Ruger, and K. Kusrini, “Sentimen Analisis pada Data Tweet Pengguna Twitter Terhadap Produk Penjualan Toko Online Menggunakan Metode K-Means”, MATRIK, vol. 19, no. 2, pp. 207–213, May 2020, doi: 10.30812/matrik.v19i2.640.
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