ANALISIS SENTIMEN PRODUK KECANTIKAN JENIS MOISTURIZER DI TWITTER MENGGUNAKAN ALGORITMA SUPER VECTOR MACHINE

  • Ria Cantika Larasati Universitas Kristen Satya Wacana
  • Christine Dewi Universitas Kristen Satya Wacana
  • Henoch Juli Christanto Universitas Kristen Satya Wacana

Abstract

The increasing public awareness in using beauty products as part of their lifestyle has gone hand in hand with the increasing public views on these products which are often expressed through reviews on social media platforms. This research reviews the use of the Support Vector Machine algorithm to classify sentiment towards the use of moisturizer on Application X. This research was conducted to obtain information on people’s responses to various moisturizer products that are spreading in the community. The data used in this study were 50,868 opinion tweets, 35,565 tweets were used as training data and 15,303 tweets were used as test data. In sentiment labeling using the TextBlob library. The classification result using Support Vector Machine get accuracy value of 98%, the highest confusion matrix of 98%, the highest recall value of 100%, and an f1-score value of 99%.

References

[1] D. W. R. Wati, N. Fatmawatie, and N. Fauza, “Pengaruh Customer Satisfaction Dan Customer Trust Terhadap Customer Loyalty Produk Krim Pelembab Wajah Fair & Lovely,” Istithmar J. Stud. Ekon. Syariah, vol. 4, no. 1, pp. 50–71, 2020, doi: 10.30762/istithmar.v4i1.4.
[2] M. E. T. Butarbutar and A. Y. Chaerunisaa, “Peran Pelembab dalam Mengatasi Kondisi Kulit Kering,” Maj. Farmasetika, vol. 6, no. 1, pp. 56–69, 2020, doi: 10.24198/mfarmasetika.v6i1.28740.
[3] I. L. Situmorang, “Pengaruh Kualitas Produk dan Iklan terhadap Citra Merek dan Keputusan Pembelian Produk Kecantikan Merek Pond’s,” Suparyanto dan Rosad (2015, vol. 5, no. 3, pp. 248–253, 2020, [Online]. Available: https://jom.unri.ac.id/index.php/JOMFEKON/article/view/12298
[4] S. Fatimah, “Pengaruh Kesadaran Merek, Persepsi Kualitas, Asosiasi Merek, Dan Loyalitas Merek Terhadap Keputusan Pembelian Pelembab Wardah Pada Konsumen Al Yasini Mart Wonorejo,” Sketsa Bisnis, vol. 1, no. 2, 2014, doi: 10.35891/jsb.v1i2.75.
[5] E. Putra, “Pengaruh Promosi Melalui Sosial Media Dan Review Produk Pada Marketplace Shopee Terhadap Keputusan Pembelian ( Studi Pada Mahasiswa Stie Pasaman ) the Influence of Promotion Through Social Media and Product Review on the Marketplace Shopee on Purchase Dec,” J. Apresiasi Ekon., vol. 8, no. 3, pp. 467–474, 2020.
[6] I. Zukhrufillah, “Gejala Media Sosial Twitter Sebagai Media Sosial Alternatif,” Al-I’lam J. Komun. dan Penyiaran Islam, vol. 1, no. 2, p. 102, 2018, doi: 10.31764/jail.v1i2.235.
[7] R. Slamet, W. Gata, A. Novtariany, K. Hilyati, and F. A. Jariyah, “Analisis Sentimen Twitter Terhadap Penggunaan Artis Korea Selatan Sebagai Brand Ambassador Produk Kecantikan Lokal,” INTECOMS J. Inf. Technol. Comput. Sci., vol. 5, no. 1, pp. 145–153, 2022, doi: 10.31539/intecoms.v5i1.3933.
[8] C. Dewi and R. C. Chen, “Complement Naive Bayes Classifier for Sentiment Analysis of Internet Movie Database,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 13757 LNAI, pp. 81–93, 2022, doi: 10.1007/978-3-031-21743-2_7.
[9] C. Dewi, B.-J. Tsai, and R.-C. Chen, “Shapley Additive Explanations for Text Classification and Sentiment Analysis of Internet Movie Database BT - Recent Challenges in Intelligent Information and Database Systems,” 2022, pp. 69–80.
[10] C. Dewi, R.-C. Chen, H. J. Christanto, and F. Cauteruccio, “Multinomial Naïve Bayes Classifier for Sentiment Analysis of Internet Movie Database,” Vietnam J. Comput. Sci., vol. 10, no. 04, pp. 485–498, 2023, doi: 10.1142/s2196888823500100.
[11] Irbah salsabila and Yuliant Sibaroni, “Multi Aspect Sentiment of Beauty Product Reviews using SVM and Semantic Similarity,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 5, no. 3, pp. 520–526, 2021, doi: 10.29207/resti.v5i3.3078.
[12] A. P. Giovani, A. Ardiansyah, T. Haryanti, L. Kurniawati, and W. Gata, “Analisis Sentimen Aplikasi Ruang Guru Di Twitter Menggunakan Algoritma Klasifikasi,” J. Teknoinfo, vol. 14, no. 2, p. 115, 2020, doi: 10.33365/jti.v14i2.679.
[13] C. Dewi, J. Zendrato, and H. J. Christanto, “Improvement of support vector machine for predicting diabetes mellitus with machine learning approach,” J. Auton. Intell., vol. 7, no. 2, pp. 1–12, 2023, doi: 10.32629/jai.v7i2.888.
[14] C. Dewi and R. C. Chen, “Random forest and support vector machine on features selection for regression analysis,” Int. J. Innov. Comput. Inf. Control, vol. 15, no. 6, pp. 2027–2037, 2019, doi: 10.24507/ijicic.15.06.2027.
[15] N. W. S. Agustini, D. Priadi, and R. V. Atika, “Profil Kimia dan Aktivitas Antibakteri Fraksi Aktif Nannochloropsis sp. sebagai Senyawa Penghambat Bakteri Penyebab Gangguan Kesehatan Mulut,” J. Pascapanen dan Bioteknol. Kelaut. dan Perikan., vol. 17, no. 1, p. 19, 2022, doi: 10.15578/jpbkp.v17i1.781.
[16] P. Arsi and R. Waluyo, “Analisis Sentimen Wacana Pemindahan Ibu Kota Indonesia Menggunakan Algoritma Support Vector Machine (SVM),” J. Teknol. Inf. dan Ilmu Komput., vol. 8, no. 1, p. 147, 2021, doi: 10.25126/jtiik.0813944.
[17] H. Nurrun Muchammad Shiddieqy, S. Paulus Insap, and W. Wing Wahyu, “Studi Literatur Tentang Perbandingan Metode Untuk Proses Analisis Sentimen Di Twitter,” Semin. Nas. Teknol. Inf. dan Komun., vol. 7, no. 2, pp. 57–64, 2016.
[18] A. Deviyanto and M. D. R. Wahyudi, “Penerapan Analisis Sentimen Pada Pengguna Twitter Menggunakan Metode K-Nearest Neighbor,” JISKA (Jurnal Inform. Sunan Kalijaga), vol. 3, no. 1, p. 1, 2018, doi: 10.14421/jiska.2018.31-01.
[19] J. W. Iskandar and Y. Nataliani, “Perbandingan Naïve Bayes, SVM, dan k-NN untuk Analisis Sentimen Gadget Berbasis Aspek,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 5, no. 6, pp. 1120–1126, 2021, doi: 10.29207/resti.v5i6.3588.
[20] R. Azhar, A. Surahman, and C. Juliane, “Analisis Sentimen Terhadap Cryptocurrency Berbasis Python TextBlob Menggunakan Algoritma Naïve Bayes,” J. Sains Komput. Inform. (J-SAKTI, vol. 6, no. 1, pp. 267–281, 2022.
[21] B. M. Iqbal, K. M. Lhaksmana, and E. B. Setiawan, “2024 Presidential Election Sentiment Analysis in News Media Using Support Vector Machine,” J. Comput. Syst. Informatics, vol. 4, no. 2, pp. 397–404, 2023, doi: 10.47065/josyc.v4i2.3051.
[22] M. Hamka, N. Alfatari, and D. Ratna Sari, “Analisis Sentimen Produk Kecantikan Jenis Serum Menggunakan Algoritma Naïve Bayes Classifier,” J. Sist. Komput. dan Inform., vol. 4, no. 1, p. 64, 2022, doi: 10.30865/json.v4i1.4740.
[23] I. P. Monika and M. T. Furqon, “Penerapan Metode Support Vector Machine (SVM) Pada Klasifikasi Penyimpangan Tumbuh Kembang Anak,” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 2, no. 10, pp. 3165–3166, 2018, [Online]. Available: http://j-ptiik.ub.ac.id
[24] A. R. Isnain, A. I. Sakti, D. Alita, and N. S. Marga, “Sentimen Analisis Publik Terhadap Kebijakan Lockdown Pemerintah Jakarta Menggunakan Algoritma Svm,” J. Data Min. dan Sist. Inf., vol. 2, no. 1, p. 31, 2021, doi: 10.33365/jdmsi.v2i1.1021.
[25] M. R. Adrian, M. P. Putra, M. H. Rafialdy, and N. A. Rakhmawati, “Perbandingan Metode Klasifikasi Random Forest dan SVM Pada Analisis Sentimen PSBB,” J. Inform. Upgris, vol. 7, no. 1, pp. 36–40, 2021, doi: 10.26877/jiu.v7i1.7099.
[26] R. Tineges, A. Triayudi, and I. D. Sholihati, “Analisis Sentimen Terhadap Layanan Indihome Berdasarkan Twitter Dengan Metode Klasifikasi Support Vector Machine (SVM),” J. Media Inform. Budidarma, vol. 4, no. 3, p. 650, 2020, doi: 10.30865/mib.v4i3.2181.
[27] A. M. Pravina, I. Cholissodin, and P. P. Adikara, “Analisis Sentimen Tentang Opini Maskapai Penerbangan pada Dokumen Twitter Menggunakan Algoritme Support Vector Machine (SVM),” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 3, no. 3, pp. 2789–2797, 2019, [Online]. Available: http://j-ptiik.ub.ac.id
[28] S. Styawati, N. Hendrastuty, and A. R. Isnain, “Analisis Sentimen Masyarakat Terhadap Program Kartu Prakerja Pada Twitter Dengan Metode Support Vector Machine,” J. Inform. J. Pengemb. IT, vol. 6, no. 3, pp. 150–155, 2021, doi: 10.30591/jpit.v6i3.2870.
[29] O. Arifin and T. B. Sasongko, “Analisa perbandingan tingkat performansi metode support vector machine dan naïve bayes classifier,” Semin. Nas. Teknol. Inf. dan Multimed. 2018, vol. 6, no. 1, pp. 67–72, 2018.
[30] Trivusi, “Metriks Evaluasi Sistem Menggunakan Confusion Matrix,” Trivusi.web.id, 2022. https://www.trivusi.web.id/2022/04/evaluasi-sistem-dengan-confusion-matrix.html (accessed Feb. 26, 2024).
[31] S. Budi, “Text Mining Untuk Analisis Sentimen Review Film Menggunakan Algoritma K-Means,” Techno.Com, vol. 16, no. 1, pp. 1–8, 2017, doi: 10.33633/tc.v16i1.1263.
[32] Anggreany Maria Susan, “Confusion Matrix,” Bina Nusantara University, 2022. https://socs.binus.ac.id/2020/11/01/confusion-matrix/ (accessed Feb. 26, 2024).
[33] Rina, “Memahami Confusion Matrix: Accuracy, Precision, Recall, Specificity, dan F1-Score untuk Evaluasi Model Klasifikasi,” Medium.com, 2023. https://esairina.medium.com/memahami-confusion-matrix-accuracy-precision-recall-specificity-dan-f1-score-610d4f0db7cf (accessed Feb. 26, 2024).
[34] N. Fitriyah, B. Warsito, and D. A. I. Maruddani, “Analisis Sentimen Gojek Pada Media Sosial Twitter Dengan Klasifikasi Support Vector Machine (Svm,” J. Gaussian, vol. 9, no. 3, pp. 376–390, 2020, doi: 10.14710/j.gauss.v9i3.28932.
[35] R. Novaneliza, F. Handayani, R. J. Suhandar, H. Surono, N. S. Azzahra, and D. Nadilla, “Perbandingan Algoritma Untuk Analisis Sentimen Pada Twitter Transportasi Umum Commuterline,” J. Sains Komput. Inform. (J-SAKTI, vol. 7, no. 1, pp. 13–21, 2023.
Published
2024-06-30
How to Cite
LARASATI, Ria Cantika; DEWI, Christine; CHRISTANTO, Henoch Juli. ANALISIS SENTIMEN PRODUK KECANTIKAN JENIS MOISTURIZER DI TWITTER MENGGUNAKAN ALGORITMA SUPER VECTOR MACHINE. Jurnal Tekinkom (Teknik Informasi dan Komputer), [S.l.], v. 7, n. 1, p. 124-134, june 2024. ISSN 2621-3079. Available at: <https://jurnal.murnisadar.ac.id/index.php/Tekinkom/article/view/1243>. Date accessed: 21 july 2024. doi: https://doi.org/10.37600/tekinkom.v7i1.1243.
Section
Articles