Identification of Karleen Hijab Fashion SME Competitors Based on Sentiment Analysis Using Naïve Bayes Classifier Algorithm
Abstract
Hijab Fashion Small and Medium-Sized Enterprises (SMEs) need to develop competitive advantages brands as a source of SME competitiveness. However, most Hijab Fashion SMEs experience limitations in developing the competitive advantages of their brands. This research was conducted to find out and understand the competitive advantages of Karleen Hijab Fashion SME competitors as the object of study. The method used is sentiment analysis using the Naïve Bayes algorithm. Sentiment analysis was carried out using online review data of Shopee e-commerce. Sentiment analysis data processing was done using orange data mining software. Sentiment analysis using the Naïve Bayes algorithm produced an average value of AUC, CA, F1, Precision and adequate recall for the entire Hijab Fashion SME brand, which is 0.72, 0.887, 0.856, 0.833, and 0.887. Based on the percentage of the largest positive sentiment on each fashion quality attribute, it is known that competitive advantages of Lozy are in the Fabric Quality Attribute (30.77%), and Good Fit (15.38%), and Halwa's competitive advantage is in the Design attribute (34.19%). Competitive advantages of Hijup are on the Serviceability Attribute (21.74%) and Packaging (15.38%), and Competitive advantages of Lafiye are on the Price Attribute (6.17%). Competitive advantages of Deenay brand are on the Reliability Attribute (20.89%), while Karleen does not have a relative advantage on any fashion quality attribute because the percentage of positive sentiment for each attribute is still below competitors.

Keywords
References
J. Khoirunnissa, “Riset: Shopee Jadi e-Commerce Ideal buat Seller, Ini Alasannya,” 2021. [Online]. Available: https://inet.detik.com/business/d-5567778/riset-shopee-jadi-e-commerce-ideal-buat-seller-ini-alasannya.
M. N. Taj and G. Girisha, “Insights of strength and weakness of evolving methodologies of sentiment analysis,” Global Transitions Proceedings, vol. 2, no. 2, pp. 157–162, 2021.
Q. Ariel, V. Chang and C. Jayne, “A systematic review of social media-based sentiment analysis : Emerging trends and challenges,” Decision Analytics Journal, vol. 3, 2022.
A. Lawani, M. R. Reed, T. Mark and Y. Zheng, “Reviews and price on online platforms: Evidence from sentiment analysis of Airbnb reviews in Boston,” Regional Science and Urban Economics (2018), 2018.
R. Ireland and A. Liu, “Application of data analytics for product design: Sentiment analysis of online product reviews,” CIRP Journal of Manufacturing Science and Technology, vol. 23, pp. 128–144, 2018.
A. Chakrapani, “Consumer Behavior and Preferences of Indian Consumers Towards Apparel Purchase in Retail Markets of India,” Innovative Journal of Business and Management, vol. 4, no. 2277–4947, pp. 94–100, 2015.
J. Su and A. Chang, “Factors affecting college students’ brand loyalty toward fast fashion: A consumer-based brand equity approach,” International Journal of Retail and Distribution Management, vol. 46, no. 1, pp. 90–107, 2018.
K. Chaykowsky, “Examining the effects of apparel attributes on perceived copyright infringement and the relationship between perceived risks and purchase intention of knockoff fashion. University of North Texas,” 2012.
K. Jegethsen, J. N. Sneddon and G. N. Soutar, “Young Australian consumers’ preferences for fashion apparel attributes,” Journal of Fashion Marketing and Management: An International Journal, vol. 16, no. 3, pp. 275–289, 2012.
D. A. Garvin, “Competing on the Eight Dimensions of Quality,” Harvard Business Review, 1987.
E. N. Hopfer and C. Istook, “The importance of apparel attributes among young Mexican-American female consumers,” Journal of Textile and Apparel, Technology and Management, vol. 10, no. 1, 2016.
L. O. Sihombing, H. Hannie, and B. A. Dermawan, “Sentimen Analisis Customer Review Produk Shopee Indonesia Menggunakan Algortima Naïve Bayes Classifier,” Edumatic: Jurnal Pendidikan Informatika, vol. 5, no. 2, pp. 233–242, 2021.
H. Hozairi, A. Anwari, and S. Alim, “Implementasi Orange Data Mining Untuk Klasifikasi Kelulusan Mahasiswa Dengan Model K-Nearest Neighbor, Decision Tree Serta Naive Bayes,” Network Engineering Research Operation, vol. 6, no. 2, pp. 133, 2021.
S. Shedriko, “Perbandingan Algoritma SVM dan KNN dalam Mengklasifikasi Kelulusan Mahasiswa pada Suatu Mata Kuliah,” STRING (Satuan Tulisan Riset Dan Inovasi Teknologi), vol. 6, no. 2, pp. 115, 2021.
W. Irmayani, “Visualisasi Data Pada Data Mining Menggunakan Metode Klasifikasi,” Jurnal Khatulistiwa Informatika, vol. IX, no. I, pp. 68–72, 2021.
DOI: http://dx.doi.org/10.31544/jtera.v7.i2.2022.323-330


Refbacks
- There are currently no refbacks.
Copyright (c) 2022 JTERA (Jurnal Teknologi Rekayasa)

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Copyright @2016-2023 JTERA (Jurnal Teknologi Rekayasa) p-ISSN 2548-737X e-ISSN 2548-8678.
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
JTERA Editorial Office:
Politeknik Sukabumi
Jl. Babakan Sirna 25, Sukabumi 43132, West Java, Indonesia
Phone/Fax: +62 266215417
Whatsapp: +62 81809214709
Website: https://jtera.polteksmi.ac.id
E-mail: [email protected]