Analisis Sentimen Ulasan Pelanggan Tomoro Coffee Menggunakan Algoritma Support Vector Machine (SVM)
DOI:
https://doi.org/10.29407/savqmk91Keywords:
Analisis sentimen, Support Vector Machine, Tomoro CoffeeAbstract
Tomoro Coffee memiliki banyak ulasan pelanggan di Google Maps sehingga sulit dianalisis secara manual. Penelitian ini bertujuan menganalisis sentimen ulasan pelanggan Tomoro Coffee menggunakan algoritma Support Vector Machine (SVM). Data diperoleh dari Google Maps melalui SerpAPI sebanyak 1.829 ulasan. Tahapan penelitian meliputi preprocessing teks, ekstraksi fitur menggunakan TF-IDF, serta klasifikasi sentimen dengan SVM kernel linear dan penanganan ketidakseimbangan data menggunakan SMOTE. Evaluasi dilakukan menggunakan confusion matrix dengan metrik akurasi, precision, recall, dan F1-score. Hasil penelitian menunjukkan model SVM mencapai akurasi 96,17% dengan performa sangat baik pada kelas sentimen positif, meskipun kinerja pada kelas netral masih rendah. Hasil ini menunjukkan bahwa SVM efektif untuk analisis sentimen ulasan pelanggan dan dapat digunakan sebagai dasar evaluasi peningkatan kualitas layanan.
Downloads
References
[1] R. F. Siahaan and R. Muliono, “Coffee Quality Classification Based on Customer Reviews Using C4.5 Algorithm,” JITE ( Journal of Informatics and Telecommunication Engineering ) Algorithm, vol. 8, no. 3, pp. 101–109, 2025, doi: 10.31289/jite.v8i3Spc.14427.
[2] M. P. Ir Muhammad Rizwan, Budidaya Kopi. Cv. Azka Pustaka, 2022.
[3] N. Azhar et al., “SENTIMENT ANALYSIS FOR COFFEE SHOP REVIEWS USING NAÏVE BAYES,” vol. 8, no. 3, 2021, doi: 10.25126/jtiik.202184436.
[4] M. A. Palimbani, R. P. Hasuti, R. A. Rajagede, D. Teknik, S. Vokasi, and U. G. Mada, “Analisis Sentimen Berbasis Aspek pada Ulasan Pengguna Aplikasi Starbucks Menggunakan Algoritma Support Vector Machine,” vol. 5, no. 1, pp. 43–49, 2024.
[5] A. M. Putri, W. K. Nofa, and D. A. P. Hapsari, “PENERAPAN METODE BERT UNTUK ANALISIS SENTIMEN ULASAN PENGGUNA APLIKASI SEGARI DI GOOGLE PLAY STORE,” Jurnal Ilmiah Teknik, vol. 4, no. 1, pp. 89–104, 2025.
[6] L. A. Fitriana, M. F. Julianto, R. Dahlia, M. R. Firdaus, and A. Fazriansyah, “Analisis Ulasan Konsumen sebagai Data Non-Keuangan dalam Sistem Informasi Akuntansi,” Profitabilitas, vol. 5, no. 1, pp. 64–74, 2025.
[7] A. F. Setyawan, Ariyanto, Fari Katul Fikriah, and Rozaq Isnaini Nugraha, “Analisis Sentimen Ulasan iPhone di Amazon Menggunakan Model Deep Learning BERT Berbasis Transformer,” Elkom: Jurnal Elektronika dan Komputer, vol. 17, no. 2, pp. 447–452, Dec. 2024, doi: 10.51903/elkom.v17i2.2150.
[8] R. Rahmadani, A. Rahim, and R. Rudiman, “Analisis Sentimen Ulasan ‘Ojol the Game’ Di Google Play Store Menggunakan Algoritma Naive Bayes Dan Model Ekstraksi Fitur Tf-Idf Untuk Meningkatkan Kualitas Game,” Jurnal Informatika dan Teknik Elektro Terapan, vol. 12, no. 3, 2024.
[9] T. I. Alfawas, A. Rahim, and R. Rudiman, “Penerapan Fitur Ekstraksi TF-IDF untuk Analisis Sentimen Ulasan Game Bus Simulator Indonesia dengan Algoritma Naive Bayes,” Innovative: Journal Of Social Science Research, vol. 4, no. 5, pp. 3177–3193, 2024.
[10] B. Salungweni et al., “ANALISIS PENGARUH FILM ‘ICE COLD’ KASUS KOPI SIANIDA TERHADAP SENTIMEN PENGGUNA YOUTUBE DENGAN SVM DAN RANDOM FOREST.” [Online]. Available: http://ejournal.stmik-time.ac.id
[11] H. Sakdiyah, “Analisis sentimen customer review brand Kopi Kenangan menggunakan metode Naive Bayes,” Fakultas Sains dan Teknologi, 2023.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Yustitio Caesar, Dwijanda Galih Prameswara
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Copyright on any article is retained by the author(s).
- The author grants the journal, right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work’s authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal’s published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
- The article and any associated published material is distributed under the Creative Commons Attribution-ShareAlike 4.0 International License
