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Articles
Published: 31-03-2022

Sentiment analysis of the Bukalapak application before IPO and after IPO using the Naive Bayes algorithm

Universitas Amikom Yogyakarta
analisis sentimen bukalapak naive bayes tf-idf confussion matrix

Abstract

Bukalapak is one of the earliest eCommerce startups established in Indonesia. Bukalapak has been bridging between sellers (Pelapak) and buyers since 2010. In 2021 Bukalapak ventured to conduct Initial Public Offers on the IDX. There are many kinds of responses from Bukalapak users to Bukalapak's steps, both positive and negative. These negative or positive sentiments can be used as input and evaluation for Bukalapak itself to maintain the loyalty of its users. The research process starts from collecting data obtained from scrapping data on Bukalapak product reviews on Google Playstore before and after the IPO. Then preprocessing the data starting from casefolding, removing stop words, tokenization, steming to TF-IDF. The results of the preprocessing are then used as data for classification using Naive Bayes. The classification was then tested and obtained an accuracy value for the data before the IPO of 77% and the data after the IPO of 76%.

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References

  1. “Rekor bukalapak di bursa saham,” 2021, diakses 29 Oktober 2021. [Online]. Available: https://katadata.co.id/ariayudhistira/infografik/610d4b596bf63/ rekor-bukalapak-di-bursa-saham
  2. B. Brahimi, M. Touahria, and A. Tari, “Improving sentiment analysis in arabic: A combined approach,” Journal of King Saud University-Computer and Information Sciences, vol. 33, no. 10, pp. 1242–1250, 2021.
  3. B. Liu, “Sentiment analysis and opinion mining,” Synthesis lectures on human language technologies, vol. 5, no. 1, pp. 1–167, 2012.
  4. Z. Zhang, Q. Ye, Z. Zhang, and Y. Li, “Sentiment classification of internet restaurant reviews written in cantonese,” Expert Systems with Applications, vol. 38, no. 6, pp. 7674– 7682, 2011.
  5. A. B. P. Negara, H. Muhardi, and I. M. Putri, “Analisis sentimen maskapai penerbangan menggunakan metode naive bayes dan seleksi fitur information gain,” J. Teknol. Inf. dan Ilmu Komput, vol. 7, no. 3, 2020.
  6. S. A. Azzahra and A. Wibowo, “Analisis sentimen multi-aspek berbasis konversi ikon emosi dengan algoritme naïve bayes untuk ulasan wisata kuliner pada web tripadvisor,” Jurnal Teknologi Informasi dan Ilmu Komputer, vol. 7, no. 4, pp. 737–744, 2020.
  7. P. Arsi and R. Waluyo, “Analisis sentimen wacana pemindahan ibu kota indonesia meng- gunakan algoritma support vector machine (svm),” Jurnal Teknologi Informasi dan Ilmu Komputer, vol. 8, no. 1, pp. 147–156, 2021.
  8. A. Rahmatulloh, N. I. Kurniati, I. Darmawan, A. Z. Asyikin, and D. Witarsyah, “Com- parison between the stemmer porter effect and nazief-adriani on the performance of winnowing algorithms for measuring plagiarism,” International Journal on Advanced Science, Engineering and Information Technology, vol. 9, no. 4, pp. 1124–1128, 2019.

How to Cite

Yanuargi, Bayu. 2022. “Sentiment Analysis of the Bukalapak Application before IPO and After IPO Using the Naive Bayes Algorithm”. JNANALOKA 3 (1):17-25. https://doi.org/10.36802/jnanaloka.2022.v3-no1-17-25.