Analisa Sentimen Pengguna Aplikasi DANA Pada Ulasan Google Play Store Menggunakan Algoritma Naive Bayes Classifier dan K-Nearest Neighbors
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Dian Ayu Sabillah
*
Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru,
Indonesia
- M Afdal Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia
- Inggih Permana Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia
- Fitriani Muttakin Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia (*) Corresponding Author
Abstract
The use of digital wallets such as DANA in Indonesia continues to increase along with the need for fast and practical non-cash transactions. User reviews on the Google Play Store are an important source of information to assess satisfaction and service problems. This study aims to classify user sentiment towards the DANA application using the Naïve Bayes Classifier (NBC) and K-Nearest Neighbor (KNN) algorithms. A total of 1,000 reviews were collected and processed through text cleaning, tokenization, stopword removal, and stemming. Sentiments were classified into positive, neutral, and negative using the lexicon method and expert validation. The results showed that NBC was superior to KNN, with the highest accuracy of 71.83%, while KNN only reached 56.44%. NBC was also more effective in detecting negative sentiment, although both were less than optimal for neutral sentiment. Word cloud visualization displays the dominant words in each sentiment category. The conclusion of this study states that Naïve Bayes is more effective in analyzing sentiment reviews of digital wallet applications such as DANA.
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References
B. Filemon, V. C. Mawardi, dan N. J. Perdana, “Penggunaan Metode Support Vector Machine untuk Klasifikasi Sentimen E-Wallet,” Jurnal Ilmu Komputer dan Sistem Informasi, vol. 10, no. 1, Mar 2022, doi: 10.24912/jiksi.v10i1.17824.
Z. H. Pradana dan S. Aminah, “The Influence of Perceived Ease of Use and Promotion on Interest in Using OVO E-Wallet in Surabaya City,” Indonesian Journal of Business Analytics, vol. 3, no. 5, hlm. 1827–1836, Okt 2023, doi: 10.55927/ijba.v3i5.5657.
Nurian A dan Sari B.N, “Analisis Sentimen Ulasan Pengguna Aplikasi Google Play Menggunakan Naïve Bayes,” Jurnal Informatika dan Teknik Elektro Terapan, vol. 11, no. 3, hlm. 2830–7062, 2023, doi: 10.23960/jitet.v11i3%20s1.3348.
V. F. Calista dan H. Wandebori, “Ekombis Review-Jurnal Ilmiah Ekonomi dan Bisnis Proposed Marketing Strategy For E-Wallet To Increase The Growth Of Active Users (Case Study: Sap Cash) ARTICLE HISTORY,” Ekombis Review: Jurnal Ilmiah Ekonomi dan Bisnis, vol. 13, no. 1, hlm. 129–144, 2024, doi: 10.37676/ekombis.v13i1.
F. Farahdinna, P. Hari Wira Atmaja, J. Sawo Manila, dan P. Ps Minggu Jakarta, “Pemanfaatan Machine Learning dalam Analisis Sentimen Pengguna E-Wallet Menggunakan SVM,” Jurnal Mahasiswa Teknik Informatika, vol. 9, no. 2, hlm. 3522–3526, Apr 2025, Diakses: 16 April 2025. [Daring]. Tersedia pada: https://ejournal.itn.ac.id/index.php/jati/article/download/13526/7436
W. Bian Lin William Cong Yang Ji dkk., “The Rise of E-Wallets and Buy Now Pay-Later: Payment Competition, Credit Expansion, and Consumer Behavior,” National Bureau Of Economic Research, Mei 2023, [Daring]. Tersedia pada: https://www.juniperresearch.com/press/digital-wallet-users-exceed-5bn-globally-2026.
A. Rahman, E. Utami, dan S. Sudarmawan, “Sentimen Analisis Terhadap Aplikasi pada Google Playstore Menggunakan Algoritma Naïve Bayes dan Algoritma Genetika,” Jurnal Komtika (Komputasi dan Informatika), vol. 5, no. 1, hlm. 60–71, Jul 2021, doi: 10.31603/komtika.v5i1.5188.
J. Yuan Mambu, L. Mea, E. Sumanto, dan E. Lompoliu, “Quality Analysis of DANA Application on the Customer Satisfaction using MS-Qual,” Ministry of Education and Culture Accreditation, vol. 24, no. 2, hlm. 144–151, Sep 2022, doi: 10.31294/p.v24i2.1383.
N. A. Ningtyas dan A. Meiriza, “Penerapan Metode System Usability Scale Dalam Mengevaluasi User Experience Aplikasi DANA,” JURIKOM (Jurnal Riset Komputer), vol. 10, no. 2, hlm. 667, Apr 2023, doi: 10.30865/jurikom.v10i2.6083.
M. P. Cristescu, R. A. Nerişanu, dan D. A. Mara, “Using Data Mining in the Sentiment Analysis Process on the Financial Market,” Journal of Social and Economic Statistics, vol. 11, no. 1–2, hlm. 36–58, Des 2022, doi: 10.2478/jses-2022-0003.
Siti Masturoh dan Achmad Baroqah Pohan, “Sentiment Analysis Against the DANA E-Wallet on Google Play Reviews Using the K-Nearest Neighbor Algorithm,” PILAR Nusa Mandiri, vol. 17, no. 1, hlm. 53–57, Mar 2021, doi: 10.33480/pilar.v17i1.2182.
F. A. Larasati, D. E. Ratnawati, dan B. T. Hanggara, “Analisis Sentimen Ulasan Aplikasi Dana dengan Metode Random Forest,” Pengembangan Teknologi Informasi dan Ilmu Komputer, vol. 6, no. 9, hlm. 4305–4313, 2022, [Daring]. Tersedia pada: http://j-ptiik.ub.ac.id
S. Nada Apsariny, N. Chamidah, E. Ana, dan A. Kurniawan, “Sentiment Analysis of User Reviews Based on Naive Bayes Classifier Algorithm With Hyperparameter Optimization: A Case Study on Application ‘KREDIT PINTAR,’” Jurnal Ilmiah Indonesia, vol. 7, no. 1, hlm. 1139–1150, Jan 2022.
Y. Mao, Q. Liu, dan Y. Zhang, “Sentiment analysis methods, applications, and challenges: A systematic literature review,” Journal of King Saud University - Computer and Information Sciences, vol. 36, no. 4, Apr 2024, doi: 10.1016/j.jksuci.2024.102048.
P. Warakmulty, D. Yeffry, dan H. Putra, “Optimalisasi Manajemen Sentimen di Media Sosial Universitas melalui Machine Learning dan AI: Studi Kasus pada Komentar Instagram,” Jurnal Tata Kelola dan Kerangka Kerja Teknologi informasi, vol. 11, no. 1, hlm. 31–38, 2025, Diakses: 19 Juli 2025. [Daring]. Tersedia pada: https://ojs.unikom.ac.id/index.php/JTK3TI
F. M. Pagi dan N. I. Widiastuti, “Analisis Sentimen Berbasis Aspek pada Ulasan Aplikasi TINDER dengan Metode Support Vector Machine,” Jurnal Ilmiah Komputer dan Informatika, vol. 13, no. 2, hlm. 114–122, 2024, Diakses: 19 Juli 2025. [Daring]. Tersedia pada: https://ojs.unikom.ac.id/index.php/komputa/article/download/14078/4727
Agus Suwarno, Andriani, dan Syaiful Rohman, “Analisis Sentimen Pada Media Sosial Twitter Mengenai Tanggapan Vaksinasi COVID-19 Menggunakan Metode Naive Bayes,” Jurnal Teknik Industri, vol. 2, no. 1, hlm. 22–29, 2021, [Daring]. Tersedia pada: https://t.co/xGaLHsjizn
A. Saepulrohman, S. Saepudin, dan D. Gustian, “Analisis Sentimen Kepuasan Pengguna Aplikasi Whatsapp Menggunakan Algoritma Naive Bayes Dan Support Vector Machine,” Accounting Information Systems and Information Technology Business Enterprise , vol. 6, no. 2, hlm. 91–105, Des 2021, doi: 10.34010/aisthebest.v6i2.4919.
A. R. Isnain, J. Supriyanto, dan M. P. Kharisma, “Implementation of K-Nearest Neighbor (K-NN) Algorithm For Public Sentiment Analysis of Online Learning,” IJCCS (Indonesian Journal of Computing and Cybernetics Systems), vol. 15, no. 2, hlm. 121, Apr 2021, doi: 10.22146/ijccs.65176.
S. S. Hasibuan, A. Angraini, E. Saputra, dan M. Megawati, “Sentimen Analisis Terhadap Fitur Tiktok Shop Menggunakan Naive Bayes dan K-Nearest Neighbor,” Jurnal Media informatika Budidarma, vol. 8, no. 1, hlm. 303–311, Jan 2024, doi: 10.30865/mib.v8i1.7238.
A. Mustofa dan R. Novita, “Klasifikasi Sentimen Masyarakat Terhadap Pemberlakuan Pembatasan Kegiatan Masyarakat Menggunakan Text Mining Pada Twitter,” Building of Informatics, Technology and Science (BITS), vol. 4, no. 1, Jun 2022, doi: 10.47065/bits.v4i1.1628.
S. N. S. Muslim, F. Nurdiyansyah, dan A. Y. Rahman, “Perbandingan Algoritma Naive Bayes dan KNN dalam Analisis Sentimen Ulasan Pengguna Aplikasi CapCut,” Jurnal Informatika dan Teknik Elektro Terapan, vol. 12, no. 3S1, Okt 2024, doi: 10.23960/jitet.v12i3s1.5156.
S. F. Amrillah, D. Krisbiantoro, dan A. Prasetyo, “Penerapan Metode K-Nearest Neighbors dan Naïve Bayes pada Analisis Sentimen Pengguna Aplikasi Bstation melalui Platform Playstore,” Building of Informatics, Technology and Science (BITS), vol. 6, no. 3, hlm. 1281–1292, Des 2024, doi: 10.47065/bits.v6i3.5863.
A. Oktian Permana dan Sudin Saepudin, “Perbandingan algoritma k-nearst neighbor dan naïve bayes pada aplikasi shopee,” Jurnal CoSciTech (Computer Science and Information Technology), vol. 4, no. 1, hlm. 25–32, Apr 2023, doi: 10.37859/coscitech.v4i1.4474.
S. Syafrizal, M. Afdal, dan R. Novita, “Analisis Sentimen Ulasan Aplikasi PLN Mobile Menggunakan Algoritma Naïve Bayes Classifier dan K-Nearest Neighbor,” MALCOM: Indonesian Journal of Machine Learning and Computer Science, vol. 4, no. 1, hlm. 10–19, Des 2023, doi: 10.57152/malcom.v4i1.983.
Muhammad Rayhan Elfansyah, Rudiman, dan Fendy Yulianto, “Perbandingan Metode K-Nearest Neighbor (KNN) DAN Naive Bayes Terhadap Analisis Sentimen Pada Pengguna E-Wallet Aplikasi DANA Menggunakan Fitur Ekstraksi TF-IDF,” JURNAL TEKNOLOGI INFORMASI, vol. 18, no. 2, hlm. 139–159, 2024, doi: https://doi.org/10.47111/JTI.
K. Mustaqim, F. A. Amaresti, dan I. N. Dewi, “Analisis Sentimen Ulasan Aplikasi PosPay untuk Meningkatkan Kepuasan Pengguna dengan Metode K-Nearest Neighbor (KNN),” Edumatic: Jurnal Pendidikan Informatika, vol. 8, no. 1, hlm. 11–20, Jun 2024, doi: 10.29408/edumatic.v8i1.24779.
N. Nur, Asmawati, dan N. Syahra, “Perbandingan Metode k-NN dan Naïve Bayes dalam Klasifikasi Penentuan Calon Pendonor Darah,” Journal of Computer and Information System ( J-CIS ), vol. 1, no. 1, hlm. 21–28, Agu 2021, doi: 10.31605/jcis.v1i1.875.
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