COMPARISON OF ACCURACY LEVELS OF SVM, DECISION TREE AND RANDOM FOREST ALGORITHMS IN SENTIMENT ANALYSIS OF USER RESPONSES OF THE GOPAY APPLICATION

Indriani Indriani; Ade Davy Wiranata

doi:10.52436/1.jutif.2024.5.3.1885

Indriani Informatics Engineering, Faculty of industrial technology and informatics, Universitas Muhammadiyah Prof. Dr. Hamka, Indonesia
Ade Davy Wiranata Informatics Engineering, Faculty of industrial technology and informatics, Universitas Muhammadiyah Prof. Dr. Hamka, Indonesia

DOI: https://doi.org/10.52436/1.jutif.2024.5.3.1885

Keywords: Decision Tree, Gopay, Random Forest, Sentiment Analysis, SVM

Abstract

The development of technology from time to time makes all work or activities easier, one of which is online money transactions which are called e-wallets or digital wallets. One of the digital wallet applications that is often used is GoPay, which is a platform and tool created for making digital payments. Not long ago, GoPay was separated into one application, which previously existed in the Gojek application. However, every application certainly has a negative side, such as GoPay, where to use the application you have to be connected to the internet, which creates dependence on smartphones. Based on this problem, the company needs to know the response of users of the GoPay application which has been launched using the SVM, Decision Tree and Random algorithms. Forest. Therefore, the aim of this research is to carry out sentiment analysis on the responses of GoPay application users after being separated from Gojek and to find out the comparison of evaluation results or accuracy produced by the three algorithms. The results of this research show that of the three algorithms used, Positive sentiment is more than Negative sentiment, where in SVM Positive 89% and Negative 85%, Decision Tree class Positive 89% and Negative 76% while in Random Forest class positive 93% and Negative 86 %. Apart from that, the Random Forest algorithm has a high level of accuracy, namely 90%, then the SVM algorithm 88% and the Decision Tree algorithm 84%.

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References

M. Ermanja, “Apa itu E-Wallet? Ini Pengertian dan Keuntungan Menggunakan Dompet Digital,” bayarind, 2023. https://www.bayarind.id/news/apa-itu-e-wallet-semua-yang-perlu-diketahui-tentang-e-wallet (accessed Feb. 07, 2024).

C. E. Sedik, “4 Fitur GoPay Terbaru, Gratis Transfer hingga Keamanan Berlapis,” Bisnis.com, 2023. https://m-bisnis-com.cdn.ampproject.org/v/s/m.bisnis.com/amp/read/20230727/101/1678960/4-fitur-gopay-terbaru-gratis-transfer-hingga-keamanan-berlapis?amp_gsa=1&amp_js_v=a9&usqp=mq331AQIUAKwASCAAgM%3D#amp_tf=Dari %25251%2524s&aoh=17073173293303&referrer=https%25 (accessed Dec. 07, 2024).

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.

D. Hariyanti, “Sehari Diluncurkan, GoPay Peringkat Satu Kategori Finance di App Store,” Katadata, 2023. https://katadata-co-id.cdn.ampproject.org/v/s/katadata.co.id/amp/dinihariyanti/digital/64c26f70069a5/sehari-diluncurkan-gopay-peringkat-satu-kategori-finance-di-app-store?amp_gsa=1&amp_js_v=a9&usqp=mq331AQIUAKwASCAAgM%3D#amp_tf=Dari %251%24s&aoh=170732098 (accessed Feb. 07, 2024).

A. R. R. Riskawati, Fatihanursari, Iin, “PENERAPAN METODE NAÏVE BAYES CLASSIFIER PADA ANALISIS SENTIMEN APLIKASI GOPAY,” JATI (Jurnal Mhs. Tek. Inform., vol. 8, no. 1, pp. 346–353, 2024, doi: https://doi.org/10.36040/jati.v8i1.8699.

N. Ambika Hapsari and A. Dwi Indriyanti, “Analisis Sentimen pada Aplikasi Dompet Digital Menggunakan Algoritma Random Forest,” J. Emerg. Inf. Syst. Bus. Intell., vol. 04, no. 03, pp. 186–192, 2023.

H. Rizqiani, “Dampak Negatif dan Positif dari Penggunaan Dompet Digital,” Ngiup, 2021. https://ngiup.com/2021/11/10/dampak-negatif-dan-positif-dari-penggunaan-dompet-digital/ (accessed Mar. 08, 2024).

F. A. Larasati, D. E. Ratnawati, and B. T. Hanggara, “Analisis Sentimen Ulasan Aplikasi Dana dengan Metode Random Forest,” … Teknol. Inf. dan …, vol. 6, no. 9, pp. 4305–4313, 2022, [Online]. Available: http://j-ptiik.ub.ac.id

D. R. Alghifari, M. Edi, and L. Firmansyah, “Implementasi Bidirectional LSTM untuk Analisis Sentimen Terhadap Layanan Grab Indonesia,” J. Manaj. Inform., vol. 12, no. 2, pp. 89–99, 2022, doi: 10.34010/jamika.v12i2.7764.

Aviliani, “Web Scraping: Alternatif Cari Data dengan Cepat,” Pacmann, 2022. https://pacmann.io/blog/cari-data-dengan-murah-dan-cepat-menggunakan-web-scraping (accessed Feb. 08, 2024).

V. Vamilina and R. Novita, “Analisis Sentimen E-Wallet Menggunakan Support Vector Machine Berbasis Particle Swarm Optimization,” Build. Informatics, Technol. Sci., vol. 5, no. 1, pp. 40–48, 2023, doi: 10.47065/bits.v5i1.3526.

B. Filemon, V. C. Mawardi, and N. J. Perdana, “Penggunaan Metode Support Vector Machine Untuk Klasifikasi Sentimen E-Wallet,” J. Ilmu Komput. dan Sist. Inf., vol. 10, no. 1, 2022, doi: 10.24912/jiksi.v10i1.17824.

R. Rinandyaswara, Y. A. Sari, and M. T. Furqon, “Pembentukan Daftar Stopword Menggunakan Term Based Random Sampling Pada Analisis Sentimen Dengan Metode Naïve Bayes (Studi Kasus: Kuliah Daring Di Masa Pandemi),” J. Teknol. Inf. dan Ilmu Komput., vol. 9, no. 4, p. 717, 2022, doi: 10.25126/jtiik.2022934707.

D. Darwis, E. S. Pratiwi, and A. F. O. Pasaribu, “Penerapan Algoritma Svm Untuk Analisis Sentimen Pada Data Twitter Komisi Pemberantasan Korupsi Republik Indonesia,” Edutic - Sci. J. Informatics Educ., vol. 7, no. 1, pp. 1–11, 2020, doi: 10.21107/edutic.v7i1.8779.

Oktavianus and M. Hondro, “Analisis Sentimen Pengguna Aplikasi E-Wallet Dana Melalui Postingan di Media Sosial Twitter Menggunakan Naïve Bayes,” J. Inform., vol. 01, no. 01, pp. 27–31, 2023.

D. Sierra, “Algoritma TF — IDF,” Medium, 2019. https://dltsierra.medium.com/algoritma-tf-idf-633e17d10a80 (accessed Nov. 01, 2023).

M. I. Fikri, T. S. Sabrila, Y. Azhar, and U. M. Malang, “Comparison of the Naïve Bayes Method and Support Vector Machine on Twitter Sentiment Analysis,” SMATIKA J. STIKI Inform. J., vol. 10, no. 2, pp. 71–76, 2020.

Y. Refo, S. Rostianingsih, and L. Liliana, “Penerapan SVM untuk Klasifikasi Sentimen pada Review Comment Berbahasa Indonesia di Online Shop,” J. Infra, 2022, [Online]. Available: https://publication.petra.ac.id/index.php/teknik-informatika/article/view/12813%0Ahttps://publication.petra.ac.id/index.php/teknik-informatika/article/download/12813/11113

C. Cahyaningtyas, Y. Nataliani, and I. R. Widiasari, “Analisis Sentimen Pada Rating Aplikasi Shopee Menggunakan Metode Decision Tree Berbasis SMOTE,” Aiti, vol. 18, no. 2, pp. 173–184, 2021, doi: 10.24246/aiti.v18i2.173-184.

I. A. D. Aji Susanto, “Analisis Sentimen Data Twitter Topik Ekonomi Dan Industri Dengan Metode Naive Bayes Dan Random Forest,” J. Ilm. Wahana Pendidik., vol. 9, no. 20, pp. 59–65, 2023, doi: https://doi.org/10.5281/zenodo.8398895.

K. A. Nugraha, “Analisis Sentimen Berbasis Emoticon pada Komentar Instagram Bahasa Indonesia Menggunakan Naïve Bayes,” J. Tek. Inform. dan Sist. Inf., vol. 7, no. 3, pp. 715–721, 2021, doi: 10.28932/jutisi.v7i3.4094.

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.

Y. Khoiruddin, A. Fauzi, and A. M. Siregar, “Analisis Sentimen Gojek Indonesia Pada Twitter Menggunakan Algoritme Naïve Bayes Dan Support Vector Machine,” J. Ilm. Komput., vol. 19, pp. 391–400, 2023.

M. K. Rifa, M. H. Totohendarto, and M. R. Muttaqin, “Analisis Sentimen Penguna E-Wallet Dana Dan Gopay Pada Twitter Menggunakan Metode Support Vector Machine (SVM),” J. Tek., vol. 17, no. 2, pp. 323–332, 2023.