Analysis of Customer Sentiment towards Wardah Beauty Products on Instagram Using K-Nearest Neighbors (KNN) and Naïve Bayes Algorithms
DOI:
https://doi.org/10.70609/gtech.v9i2.6804Keywords:
K-Nearest Neighbors (KNN), Naïve Bayes, Sentiment analysis, Social Media, BeautyAbstract
The beauty industry in Indonesia continues to grow rapidly, with Wardah emerging as a leading local brand that garners numerous customer reviews on Instagram. Understanding these customer opinions is crucial, as they significantly influence brand perception and purchasing decisions. This study aims to compare the performance of K-Nearest Neighbors (KNN) and Naïve Bayes algorithms in classifying customer sentiment toward Wardah’s products on Instagram. The research follows the CRISP-DM methodology, involving data collection through web scraping, data preprocessing, and classification using both algorithms. The results show that KNN achieves higher accuracy (78.23%) than Naïve Bayes (74.56%), making it more effective in sentiment classification. KNN is advantageous in identifying distance-based patterns, while Naïve Bayes offers faster processing and easier implementation. These findings suggest that KNN is more suitable for accurate sentiment prediction. Wardah can implement a KNN-based sentiment monitoring system to track public feedback on product launches and campaigns, thereby enhancing marketing strategies and customer engagement
References
Adhi Putra, A. D. (2021). Sentiment Analysis on User Reviews of the Bibit and Bareksa Application with the KNN Algorithm. JATISI (Jurnal Teknik Informatika Dan Sistem Informasi), 8(2), 636–646.
Anam, M. K., Pikir, B. N., & Firdaus, M. B. (2021). Penerapan Na ̈ıve Bayes Classifier, K-Nearest Neighbor (KNN) dan Decision Tree untuk Menganalisis Sentimen pada Interaksi Netizen danPemeritah. MATRIK : Jurnal Manajemen, Teknik Informatika Dan Rekayasa Komputer, 21(1), 139–150. https://doi.org/10.30812/matrik.v21i1.1092
Anggraini, A. D., Baiti, A. N., Maulana, R., & Anjaya, V. F. (2024). ANALISIS SWOT DALAM MENGHADAPI PERSAINGAN INDUSTRI KOSMETIK : ( STUDY PADA STRATEGI BISNIS WARDAH BEAUTY STORE DI BANDAR LAMPUNG ). 1, 237–246.
Article, O., Influencer, P., Beli, M., Makeup, K., Shop, T., Tri, A., Purba, L., Saragih, A. A., Area, U. M., Author, C., Pendahuluan, T. S., Indonesia, D., & Kosmetik, P. (2025). https://jim.usk.ac.i d/sejarah. 78–84.
Arvy, N. A., & Wahyuni, I. I. (2023). Strategi Electronic Word of Mouth Wardah Melalui Hashtag #Wardahfeelthecolor Pada Media Sosial Instagram. Jurnal Riset Mahasiswa Dakwah Dan Komunikasi, 5(2), 145–164.
Azhari, M., & Sazali, H. (2023). Eksistensi Produk Wardah Dalam Pemasaran Di Media Sosial. Jurnal Ilmu Komunikasi UHO : Jurnal Penelitian Kajian Ilmu Komunikasi Dan Informasi, 8(2), 244–258. https://doi.org/10.52423/jikuho.v8i2.37
Buhori, K., Nasional, I. U., & Selatan, K. J. (2024). Comparative Analysis of KNN and Neavy Bayes Algorithms in Socio-Economic Data Classification in Indonesia. x(1), 222–232.
Ghandi, S., & Ramadhan, Y. R. (2024). PENERAPAN METODE CONVOLUTIONAL NEURAL NETWORK ( CNN ) DALAM APLIKASI PENDETEKSI PENYAKIT DAUN TANAMAN KENTANG BERBASIS ANDROID. 8(5), 8701–8708.
Hermawan, M. A., Faqih, A., Dwilestari, G., Informatika, T., & Informasi, S. (2025). IMPLEMENTASI AKURASI MODEL NAIVE BAYES MENGGUNAKAN SMOTE DALAM ANALISIS SENTIMEN PENGGUNA APLIKASI BRIMO. 13(1).
Industri, P., & Tiktok, A. (2025). Jurnal JTIK ( Jurnal Teknologi Informasi dan Komunikasi ) Pemanfaatan Media Sosial TikTok sebagai Media Promosi untuk. 9(June), 639–645.
Isnain, A. R., Supriyanto, J., & Kharisma, M. P. (2021). Implementation of K-Nearest Neighbor (K-NN) Algorithm For Public Sentiment Analysis of Online Learning. IJCCS (Indonesian Journal of Computing and Cybernetics Systems), 15(2), 121. https://doi.org/10.22146/ijccs.65176
Köster, J., Mölder, F., Jablonski, K. P., Letcher, B., Hall, M. B., Tomkins-Tinch, C. H., Sochat, V., Forster, J., Lee, S., Twardziok, S. O., Kanitz, A., Wilm, A., Holtgrewe, M., Rahmann, S., & Nahnsen, S. (2021). Sustainable data analysis with Snakemake. F1000Research, 10(May). https://doi.org/10.12688/f1000research.29032.2
Kurniawan, D., & Yasir, M. (2022). Optimization Sentimen Analysis using CRISP-DM and Naive Bayes Methods Implemented on Social Media. Cyberspace: Jurnal Pendidikan Teknologi Informasi, 6(2), 74. https://doi.org/10.22373/cj.v6i2.12793
Lesmana, T. (2023). Analisis Perilaku Konsumen Generasi Milenial Dalam Berbelanja Online. Jurnal EBI, 5(2), 46–56. https://doi.org/10.52061/ebi.v5i2.175
Muttaqin, Wahyu Wijaya Widiyanto, M. M., Green Ferry Mandias, Stenly Richard Pungus, A. W., Wiranti Kusuma Hapsari, S. A. H., Aslam Fatkhudin, Pasnur, E. F. B., & Mochammad Anshori, Suryani, N. S. (2023). Pengenalan Data Mining (Issue July).
Pranasa, R., Lazuardi, A., & Putrajip, M. (2023). Visual Content Strategy Analysis: Enhancing Consumer Engagement in Wardah’s Marketing Approach. Jurnal Ilmiah Manajemen Kesatuan, 11(3), 1015–1024. https://doi.org/10.37641/jimkes.v11i3.2230
Pratmanto, D., & Imaniawan, F. F. D. (2023). Analisis Sentimen Terhadap Aplikasi Canva Menggunakan Algoritma Naive Bayes Dan K-Nearest Neighbors. Computer Science (CO-SCIENCE), 3(2), 110–117. https://doi.org/10.31294/coscience.v3i2.1917
Sari, S. S., Ulfa, U. K., Pradita, P. E. P. U., & Tri, T. S. (2021). Analisis Sentimen Terhadap Komentar Beauty Shaming Di Media Sosial Twitter Menggunakan Algoritma SentiStrength. Indonesian Journal of Informatic Research and Software Engineering (IJIRSE), 1(1), 71–78. https://doi.org/10.57152/ijirse.v1i1.55
Sarker, I. H. (2021). Data Science and Analytics: An Overview from Data-Driven Smart Computing, Decision-Making and Applications Perspective. SN Computer Science, 2(5), 1–22. https://doi.org/10.1007/s42979-021-00765-8
Tazlia, I., Nurfadilah, D., & Pratama, S. (2023). Purchase Intention of Halal Local Beauty Brand During Covid-19: the Role of Influencers’ Credibility and Halal Awareness. Journal of Islamic Monetary Economics and Finance, 9(3), 397–418. https://doi.org/10.21098/jimf.v9i3.1623
Wankhade, M., Rao, A. C. S., & Kulkarni, C. (2022). A survey on sentiment analysis methods, applications, and challenges. In Artificial Intelligence Review (Vol. 55, Issue 7). Springer Netherlands. https://doi.org/10.1007/s10462-022-10144-1
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Aghniya Aulia Nisa, Nur Hafidzah Safitri, Lilis Stianingsih, Fiqih Hana Saputri
This work is licensed under a Creative Commons Attribution 4.0 International License.
