IMPLEMENTASI SUPPORT VECTOR MACHINE (SVM) DENGAN QUERY EXPANSION RANKING PADA REVIEW PENGGUNAAN JAMU MADURA

Rika Yunitarini, Hambali Fitrianto, Fifin Ayu Mufarroha

Abstract


Madura traditional herbal medicine is a traditional herbal medicine made from natural ingredients and is well-known for its efficacy. The popularity of Madura traditional herbal medicine is not only based on the diversity of traditional herbal medicine products and their health benefits, but also on traditional values that have been passed down from generation to generation. One of the most popular Madura traditional herbal medicine is Peluntur traditional herbal medicine. Peluntur traditional herbal medicine is a series of medicinal or herbal products specifically designed as a solution to overcome late menstruation or irregular menstruation, which is often a source of concern for mothers and young women. With the background of the increasing demand for Madura traditional herbal medicine products, a sentiment analysis was conducted on Madura traditional herbal medicine product reviews on the Shopee, Lazada, and Tokopedia applications. This study applies Support Vector Machine and Query Expansion Ranking to achieve the highest accuracy in reviewing the use of Madura traditional herbal medicine. The results obtained for the use of the Support Vector Machine algorithm have an accuracy of 93%, while for the use of the Support Vector Machine and Query Expansion Ranking algorithms at feature selection ratios of 50% and 100% the accuracy increases to 94%.

Keywords: Madura traditional herbal medicine, Peluntur traditional herbal medicine, Query Expansion Ranking, Sentiment Analysis, Support Vector Machine


Full Text:

PDF

References


R. Yunitarini, E. Widiaswanti, P. Adi, and P. Nugroho, “Menggunakan Metode Waterfall Information System of Madura Herb Stock Using Waterfall Method,” J. SimanteC, vol. 11, no. 1, pp. 65–72, 2022.

S. Kristianto, J. Batoro, S. Widyarti, and S. B. Sumitro, “Exploration and economic value of medicinal plants as traditional herbal ingredients in bangselok, madura, indonesia,” Proc. Int. Conf. Ind. Eng. Oper. Manag., no. August, 2020.

M. R. Adiyasa and M. Meiyanti, “Pemanfaatan obat tradisional di Indonesia: distribusi dan faktor demografis yang berpengaruh,” J. Biomedika dan Kesehat., vol. 4, no. 3, pp. 130–138, 2021, doi: 10.18051/jbiomedkes.2021.v4.130-138.

Eva, “Jamu Pluntur /Tepat Bulan (herbs to avoid late menstrual period),” Maduherbal, 2021. https://maduraherbal.com/jamu-pluntur-tepat-bulan-herbs-to-avoid-late-menstrual-period-herbal-code-jt-011/ (Diakses 3 September 2024).

I. F. Pramasari and N. Q. Wijaya, “Strategi Pengembangan Jamu Ramuan Madura Di Kabupaten Sumenep,” J. Pertan. Cemara, vol. 18, no. 1, pp. 50–63, 2021, doi: 10.24929/fp.v18i1.1365.

Prasetyo, Vincentius Riandaru, Ihza Akbar Ryanda, and Delta Ardy Prima. “Analisis Sentimen Dan Kategorisasi Review Pelanggan Pada Cafe Kopi Paste Dengan Metode Naive Bayes Dan K-Nearest Neighbor.” NERO (Networking Engineering Research Operation) 8.1 (2023): 1-8.

E. H. Muktafin, K. Kusrini, and E. T. Luthfi, “Analisis Sentimen pada Ulasan Pembelian Produk di Marketplace Shopee Menggunakan Pendekatan Natural Language Processing,” J. Eksplora Inform., vol. 10, no. 1, pp. 32–42, 2020, doi: 10.30864/eksplora.v10i1.390.

R. Ireland and A. Liu, “Application of data analytics for product design: Sentiment analysis of online product reviews,” CIRP J. Manuf. Sci. Technol., vol. 23, pp. 128–144, 2018, doi: 10.1016/j.cirpj.2018.06.003.

A. P. Natasuwarna, “Seleksi Fitur Support Vector Machine pada Analisis Sentimen Keberlanjutan Pembelajaran Daring,” Techno.Com, vol. 19, no. 4, pp. 437–448, 2020, doi: 10.33633/tc.v19i4.4044.

S. S. Istia and H. D. Purnomo, “Sentiment analysis of law enforcement performance using support vector machine and K-nearest neighbor,” Proc. - 2018 3rd Int. Conf. Inf. Technol. Inf. Syst. Electr. Eng. ICITISEE 2018, pp. 84–89, 2018, doi: 10.1109/ICITISEE.2018.8720969.

A. A. Munandar, F. Farikhin, and C. E. Widodo, “Sentimen Analisis Aplikasi Belajar Online Menggunakan Klasifikasi SVM,” JOINTECS (Journal Inf. Technol. Comput. Sci., vol. 8, no. 2, p. 77, 2023, doi: 10.31328/jointecs.v8i2.4747.

B. Sifa Amalia, Y. Umaidah, R. Mayasari, S. Karawang Jl HSRonggo Waluyo, K. Telukjambe Timur, and K. Karawang, “Analisis Sentimen Review Pelanggan Restoran Menggunakan Algoritma Support Vector Machine Dan K-Nearest Neighbor,” SITEKIN J. Sains, Teknol. dan Ind., vol. 19, no. 1, pp. 28–34, 2021.

H. Atsqalani, N. Hayatin, and C. S. K. Aditya, “Sentiment Analysis from Indonesian Twitter Data Using Support Vector Machine And Query Expansion Ranking,” J. Online Inform., vol. 7, no. 1, p. 116, 2022, doi: 10.15575/join.v7i1.669.

M. R. A. Nasution and M. Hayaty, “Perbandingan Akurasi dan Waktu Proses Algoritma K-NN dan SVM dalam Analisis Sentimen Twitter,” J. Inform., vol. 6, no. 2, pp. 226–235, 2019, doi: 10.31311/ji.v6i2.5129.

S. Rizkia, E. Budi Setiawan, and D. Puspandari, “Analisis Sentimen Kepuasan Pelanggan Terhadap Internet Provider Indihome di Twitter Menggunakan Metode Decision Tree dan Pembobotan TF-IDF,” e-Proceeding Eng., vol. 6, no. Agustus, pp. 9683–9693, 2019.

S. Fanissa, M. A. Fauzi, and S. Adinugroho, “Analisis Sentimen Pariwisata di Kota Malang Menggunakan Metode Naive Bayes dan Seleksi Fitur Query Expansion Ranking,” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 2, no. 8, pp. 2766–2770, 2018, [Online]. Available: http://j-ptiik.ub.ac.id

Y. A. V. Gunawan, N. A. S. ER, I. B. M. Mahendra, I. M. Widiartha, I. G. N. A. C. Putra, and I. G. A. G. A. Kadyanan, “Analisis Sentimen Ulasan Aplikasi Transportasi Online Menggunakan Multinomial Naïve Bayes dan Query Expansion Ranking,” JELIKU (Jurnal Elektron. Ilmu Komput. Udayana), vol. 11, no. 1, p. 121, 2022, doi: 10.24843/jlk.2022.v11.i01.p13.

F. Rahutomo, P. Y. Saputra, and M. A. Fidyawan, “Implementasi Twitter Sentiment Analysis Untuk Review Film Menggunakan Algoritma Support Vector Machine,” J. Inform. Polinema, vol. 4, no. 2, p. 93, 2018, doi: 10.33795/jip.v4i2.152.

S. Huang, C. A. I. Nianguang, P. Penzuti Pacheco, S. Narandes, Y. Wang, and X. U. Wayne, “Applications of support vector machine (SVM) learning in cancer genomics,” Cancer Genomics and Proteomics, vol. 15, no. 1, pp. 41–51, 2018, doi: 10.21873/cgp.20063.

F. Prasetiawan1, S. Widiyanesti2, and T. Widarmanti3, “Analisis Sentimen Mengenai Kualitas Layanan Jasa Ekspedisi Barang Sicepat Di Media Sosial Twitter Sentiment Analysis Regarding Quality of Sicepat Expedition Services On Twitter Social Media,” e-Proceeding Manag., vol. 9, no. 2, pp. 147–160, 2022.

F. R. Irawan, A. Jazuli, and T. Khotimah, “Analisis Sentimen Terhadap Pengguna Gojek Menggunakan Metode K-Nearset Neighbors Sentiment Analysis of Gojek Users Using K-Nearest Neighbor,” JIKO (Jurnal Inform. dan Komputer), vol. 5, no. 1, pp. 62–68, 2022, doi: 10.33387/jiko.

M. Birjali, M. Kasri, and A. Beni-Hssane, “A comprehensive survey on sentiment analysis: Approaches, challenges and trends,” Knowledge-Based Syst., vol. 226, p. 107134, 2021, doi: 10.1016/j.knosys.2021.107134.




DOI: https://doi.org/10.21107/nero.v9i2.27785

Refbacks

  • There are currently no refbacks.


Copyright (c) 2024 Rika Yunitarini