Analisis Sentimen Pengguna Terhadap Layanan Aplikasi Seabank Indonesia di Instagram Menggunakan Metode Support Vector Machine
-
Muhammad Sunni Arrafiq
*
Universitas Islam Negeri Sumatera Utara, Deli Serdang,
Indonesia
- Rakhmat Kurniawan Universitas Islam Negeri Sumatera Utara, Deli Serdang, Indonesia (*) Corresponding Author
Abstract
Various aspects of life have been significantly changed by rapid technological advances; this includes the banking industry, which has developed digital banking services. SeaBank is a digital banking application that allows us to do many things with our money, from saving to making online transactions with our mobile phones anytime and anywhere. By using the Support Vector Machine (SVM) method to classify user comments on the SeaBank Indonesia Instagram account into positive and negative comments, this research aims to find analytical ways to improve service quality and customer satisfaction through this sentiment. Data is processed in several stages, such as cleaning, normalization, tokenization, stopword removal, and stemming. Then the SVM algorithm is used to classify sentiment. These performance algorithms are measured by metrics such as accuracy, precision, recall, and F1 score. The results of the analysis of 1201 comment data show that 536 data are positive and 665 data are negative. The Support Vector Machine method shows an accuracy of 89%, precision of 93%, recall of 83%, and fl-score of 88%.
Downloads
References
Alun Sujjadaa, Somantri, Juwita Nurfazri Novianti, and Indra Griha Tofik Isa, “Analisis Sentimen Terhadap Review Bank Digital Pada Google Play Store Menggunakan Metode Support Vector Machine (Svm),” J. Rekayasa Teknol. Nusa Putra, vol. 9, no. 2, pp. 122–135, 2023, doi: 10.52005/rekayasa.v9i2.345.
I. P. D. W. Darmawan, G. A. Pradnyana, and I. B. N. Pascima, “Optimasi Parameter Support Vector Machine Dengan Algoritma Genetika Untuk Analisis Sentimen Pada Media Sosial Instagram,” SINTECH (Science Inf. Technol. J., vol. 6, no. 1, pp. 58–67, 2023, doi: 10.31598/sintechjournal.v6i1.1245.
C. S. Feroza and D. Misnawati, “Penggunaan Media Sosial Instagram Pada Akun @Yhoophii_Official Sebagai Media Komunikasi Dengan Pelanggan,” J. Inov., vol. 15, no. 1, pp. 54–61, 2021, doi: 10.33557/ji.v15i1.2204.
R. Mahendrajaya, G. A. Buntoro, and M. B. Setyawan, “Analisis Sentimen Pengguna Gopay Menggunakan Metode Lexicon Based Dan Support Vector Machine,” Komputek, vol. 3, no. 2, p. 52, 2019, doi: 10.24269/jkt.v3i2.270.
N. Nandal, R. Tanwar, and A. S. K. Pathan, “Sentiment Analysis based Emotion Extraction for COVID-19 Using Crawled Tweets and Global Statistics for Mental Health,” Procedia Comput. Sci., vol. 218, no. 2022, pp. 949–958, 2022, doi: 10.1016/j.procs.2023.01.075.
P. Mario Saskara and H. Moch Rizal, “Pengembangan Business Model Canvas untuk Menciptakan Competitive Advantage Seabank Indonesia,” J. Impresi Indones., vol. 2, no. 1, pp. 40–57, 2023, doi: 10.58344/jii.v2i1.1921.
V. K. S. Que, A. Iriani, and H. D. Purnomo, “Analisis Sentimen Transportasi Online Menggunakan Support Vector Machine Berbasis Particle Swarm Optimization,” J. Nas. Tek. Elektro dan Teknol. Inf., vol. 9, no. 2, pp. 162–170, 2020, doi: 10.22146/jnteti.v9i2.102.
F. Romadoni, Y. Umaidah, and B. N. Sari, “Text Mining Untuk Analisis Sentimen Pelanggan Terhadap Layanan Uang Elektronik Menggunakan Algoritma Support Vector Machine,” J. Sisfokom (Sistem Inf. dan Komputer), vol. 9, no. 2, pp. 247–253, 2020, doi: 10.32736/sisfokom.v9i2.903.
H. Syah and A. Witanti, “Analisis Sentimen Masyarakat Terhadap Vaksinasi Covid-19 Pada Media Sosial Twitter Menggunakan Algoritma Support Vector Machine (Svm),” J. Sist. Inf. dan Inform., vol. 5, no. 1, pp. 59–67, 2022, doi: 10.47080/simika.v5i1.1411.
M. Afdal and L. R. Elita, “Penerapan Text Mining Pada Aplikasi Tokopedia Menggunakan Algoritma K-Nearest Neighbor,” J. Ilm. Rekayasa dan Manaj. Sist. Inf., vol. 8, no. 1, p. 78, 2022, doi: 10.24014/rmsi.v8i1.16595.
N. Veraniazzahra and R. Listiawati, “Pengaruh Perceived Usefulness, Perceived Credibility, dan Features Terhadap Minat Menggunakan Bank Digital SeaBank di Jabodetabek,” Semin. Nas. Akunt. dan Manaj., vol. 3, pp. 1–9, 2023.
S. M. Kom, “Sentimen Pengguna Aplikasi BRImo: Kinerja Algoritma Support Vector Machine, Naive Bayes, dan Adaboost,” SATIN - Sains dan Teknol. Inf., vol. 9, no. 2, pp. 195–204, 2023, doi: 10.33372/stn.v9i2.1057.
V. Lisa and F. Selamat, “Faktor-Faktor yang Memengaruhi Loyalitas Nasabah dengan Mediasi Kepuasan Nasabah pada PT Bank Seabank Indonesia,” J. Manajerial Dan Kewirausahaan, vol. 5, no. 4, pp. 1091–1097, 2023, doi: 10.24912/jmk.v5i4.26987.
D. Safryda Putri and T. Ridwan, “Analisis Sentimen Ulasan Aplikasi Pospay Dengan Algoritma Support Vector Machine,” J. Ilm. Inform., vol. 11, no. 01, pp. 32–40, 2023, doi: 10.33884/jif.v11i01.6611.
R. Oktafiani, A. Hermawan, and D. Avianto, “Pengaruh Komposisi Split data Terhadap Performa Klasifikasi Penyakit Kanker Payudara Menggunakan Algoritma Machine Learning,” J. Sains dan Inform., vol. 9, no. April, pp. 19–28, 2023, doi: 10.34128/jsi.v9i1.622.
S. Mandasari, B. H. Hayadi, and R. Gunawan, “Analisis Sentimen Pengguna Transportasi Online Terhadap Layanan Grab Indonesia Menggunakan Multinomial Naive Bayes Classifier,” J-SISKO TECH (Jurnal Teknol. Sist. Inf. dan Sist. Komput. TGD), vol. 5, no. 2, p. 118, 2022, doi: 10.53513/jsk.v5i2.5635.
M. Furqan, S. Sriani, and M. N. Shidqi, “Chatbot Telegram Menggunakan Natural Language Processing,” Walisongo J. Inf. Technol., vol. 5, no. 1, pp. 15–26, 2023, doi: 10.21580/wjit.2023.5.1.14793.
M. Ikhsan and R. R. Kurniawan, “Penerapan Text Mining pada Sistem Rekomendasi Pembimbing Skripsi Mahasiswa Menggunakan Algoritma Naïve Bayes Classifier,” Indones. J. Comput. Sci. Attrib., vol. 12, no. 6, pp. 2023–4196, 2023.
A. Halim Hasugian, R. Amanda Putri, and M. Alfan Simatupang, “Penerapan Algoritma Klasifikasi Naïve Bayes Untuk Analisis Sentimen Tentang Pemindahan Ibu Kota Negara,” J. Sci. Soc. Res., vol. 4307, no. 2, pp. 635–644, 2024, [Online]. Available: http://jurnal.goretanpena.com/index.php/JSSR
R. Abdul Rosid, M. Martanto, and I. Ali, “Analisis Internet Network Performance Menggunakan Parameter Quality of Service,” JATI (Jurnal Mhs. Tek. Inform., vol. 7, no. 1, pp. 203–210, 2023, doi: 10.36040/jati.v7i1.6252.
Bila bermanfaat silahkan share artikel ini
Berikan Komentar Anda terhadap artikel Analisis Sentimen Pengguna Terhadap Layanan Aplikasi Seabank Indonesia di Instagram Menggunakan Metode Support Vector Machine
Pages: 1280-1291
Copyright (c) 2024 Muhammad Sunni Arrafiq, Rakhmat Kurniawan
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International 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 (Refer to The Effect of Open Access).
