Sentiment Analysis of SPayLater and SPinjam Features in the Shopee Application Using the Support Vector Machine (SVM) Algorithm

Rahmad Rahmad Nawi Pane; Wilda Wilda Rina Hasibuan

doi:10.59934/jaiea.v5i3.2322

Authors

Rahmad Rahmad Nawi Pane Universitas Muhammadiyah Sumatera Utara
Wilda Rina Hasibuan Universitas Muhammadiyah Sumatera Utara

DOI:

https://doi.org/10.59934/jaiea.v5i3.2322

Keywords:

Sentiment Analysis, Support Vector Machine, SPayLater, SPinjam

Abstract

The rapid development of information technology and the increasing use of e-commerce applications have generated a large number of user reviews that can be used to measure user satisfaction. SPayLater and SPinjam, as features in the Shopee application, receive various responses in the form of positive, negative, and neutral sentiments, making automatic sentiment analysis necessary. This study aims to analyze user sentiment and implement the Support Vector Machine (SVM) algorithm to classify reviews. The data used consist of 500 user reviews obtained from the Google Play Store. The method includes preprocessing, labeling, and classification using SVM. The results show that there are 231 positive, 230 negative, and 39 neutral sentiments. Model evaluation yields an accuracy of 74%, precision of 0.78, and recall of 0.84, indicating that the model performs fairly well. The developed system is also capable of processing data automatically and displaying classification results effectively. Therefore, the SVM algorithm is effective for sentiment analysis of SPayLater and SPinjam services in the Shopee application.

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References

Nuruddin, M. S. T. S., & Himmati, R. (2024). Minat Konsumen Dalam Berbelanja pada Aplikasi Shopee Ditinjau Berdasarkan Fitur Paylater, Spinjam dan Affiliate. Al-Kharaj: Jurnal Ekonomi, Keuangan & Bisnis Syariah, 6(1), 693-711. https://doi.org/10.47467/alkharaj.v6i1.3800

Prastiwi, I. E., & Fitria, T. N. (2021). Konsep Paylater Online Shopping dalam Pandangan Ekonomi Islam. Jurnal Ilmiah Ekonomi Islam, 7(1), 425–432. https://doi.org/10.29040/jiei.v7i1.1458

Wahidna, F. J., & Nerisafitra, P. (2023). Analisis Sentimen Pengguna Sistem Pay Later Menggunakan Support Vector Machine Metode Pembobotan Lexicon. Journal of Informatics and Computer Science (JINACS), 334-343. https://doi.org/10.26740/jinacs.v4n03.p334-343

Nurhalizah, R. S., Ardianto, R., & Purwono, P. (2024). Analisis Supervised dan Unsupervised Learning pada Machine Learning: Systematic Literature Review. Jurnal Ilmu Komputer Dan Informatika, 4(1), 61–72. https://doi.org/10.54082/jiki.168

Manik, G., Ernawati, I., & Nurlaili, I. (2021). Analisis Sentimen Pada Review Pengguna E-Commerce Bidang Pangan Menggunakan Metode Support Vector Machine (Studi Kasus : Review Sayurbox dan Tanihub pada Google Play ). Seminar Nasional Mahasiswa Ilmu Komputer Dan Aplikasinya (SENAMIKA), (September), 64–74

Handayani, A., & Zufria, I. (2023). Analisis Sentimen Terhadap Bakal Capres RI 2024 di Twitter Menggunakan Algoritma SVM. Journal of Information System Research (JOSH), 5(1), 53-63. https://doi.org/10.47065/josh.v5i1.4379

Isnain, A. R., Sakti, A. I., Alita, D., & Marga, N. S. (2021). Sentimen Analisis Publik Terhadap Kebijakan Lockdown Pemerintah Jakarta Menggunakan Algoritma Svm. Jurnal Data Mining Dan Sistem Informasi, 2(1), 31-37.

Pamungkas, F. S., & Kharisudin, I. (2021). Analisis Sentimen dengan SVM, NAIVE BAYES dan KNN untuk Studi Tanggapan Masyarakat Indonesia terhadap Pandemi Covid-19 pada Media Sosial Twitter. In PRISMA, Prosiding Seminar Nasional Matematika (Vol. 4, pp. 628- 634).

Faiq, M., Putro, A., & Setiawan, E. B. (2022). Analisis Sentimen Terhadap Kebijakan Pemerintah dengan Feature Expansion Metode GloVe pada Media Sosial Twitter. E-Proceeding of Engineering , 9(1), 54–66.

Idris, I. S. K., Mustofa, Y. A., & Salihi, I. A. (2023). Analisis Sentimen Terhadap Penggunaan Aplikasi Shopee Mengunakan Algoritma Support Vector Machine (SVM). Jambura Journal of Electrical and Electronics Engineering, 5(1), 32-35.

Raehanun, M. (2021). Analisis Support Vector Machine (SVM) Dalam Prediksi Permintaan Emas Perhiasan (Studi Kasus: Permintaan Emas Perhiasan dari Beberapa Negara Tertentu Periode Tahun 2000-2021).

Astuti, Y., Ruldeviyani, Y., Salbari, F., & Prayogi, A. (2023). Sentiment Analysis of Electricity Company Service Quality Using Naïve Bayes. Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), 7(2), 389-396. https://doi.org/10.29207/resti.v7i2.4627.

Kristiawan, K., & Widjaja, A. (2021). Perbandingan algoritma machine learning dalam menilai sebuah lokasi toko ritel, Jurnal Teknik Informatika dan Sistem Informasi, 7(1). https://doi.org/10.28932/jutisi.v7i1.3182