Studi Komparatif Algoritma Random Forest dan Logistic Regression dalam Analisis Sentimen Ulasan Aplikasi E-Wallet Dana
DOI:
https://doi.org/10.30865/jurikom.v13i1.9517Keywords:
Sentiment Analysis, Dana, Google Play Store, Logistic Regression, Random ForestAbstract
The increasing use of digital wallets in Indonesia has led to a growing number of user opinions expressed, including on the DANA platform in the Play Store. These reviews reflect users' experiences and satisfaction levels, necessitating sentiment analysis to comprehend public opinions about the application’s service quality. The study conducts an analytical comparison between Random Forest and Logistic Regression methods in classification sentiments for DANA application. Data was obtained through scraping techniques, resulting in 2,068 reviews after the cleaning process. The analysis stages include text preprocessing, labeling based on review scores, weighting using TF-IDF, and modeling with both algorithms. The evaluation results demonstrate that Random Forest obtains an accuracy of 86.23%, while Logistic Regression obtains an accuracy of 84.54%. Both models are capable of classifying positive sentiments well but are less optimal in detecting negative sentiments. Random Forest shows higher performance compared to Logistic Regression within the task in sentiment analysis for DANA app reviews. Thus, we can conclude that using the random forest algorithm is able to produce accurate sentiment analysis and can act as a basis for making decisions in further research
References
[1] F. M. Delta Maharani, A. Lia Hananto, S. Shofia Hilabi, F. Nur Apriani, A. Hananto, and B. Huda, “Perbandingan Metode Klasifikasi Sentimen Analisis Penggunaan E-Wallet Menggunakan Algoritma Naïve Bayes dan K-Nearest Neighbor,” Metik J., vol. 6, no. 2, pp. 97–103, 2022, doi: 10.47002/metik.v6i2.372.
[2] M. W. A. Putra, Susanti, Erlin, and Herwin, “Analisis Sentimen Dompet Elektronik Pada Twitter Menggunakan Metode Naïve Bayes Classifier,” IT J. Res. Dev., vol. 5, no. 1, pp. 72–86, 2020, doi: 10.25299/itjrd.2020.vol5(1).5159.
[3] 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.
[4] A. Maulana, Inayah Khasnaputri Afifah, Asghafi Mubarrak, Kiagus Rachmat Fauzan, Ardhan Dwintara, and B. P. Zen, “Comparison of Logistic Regression, Multinomialnb, Svm, and K-Nn Methods on Sentiment Analysis of Gojek App Reviews on the Google Play Store,” J. Tek. Inform., vol. 4, no. 6, pp. 1487–1494, 2023, doi: 10.52436/1.jutif.2023.4.6.863.
[5] E. Ogi, I. Pratiwi, and W. Yustanti, “Analisis Sentimen Kualitas Layanan Teknologi Pembayaran Elektronik pada Twitter (Studi Kasus Ovo dan Dana),” Jeisbi, vol. 02, no. 03, p. 2021, 2021.
[6] H. Junianto, R. E. Saputro, B. A. Kusuma, and D. I. S. Saputra, “Comparison of Logistic Regression and Random Forest in Sentiment Analysis of Disdukcapil Application Reviews,” J. Tek. Inform., vol. 5, no. 6, pp. 1539–1547, 2024, doi: 10.52436/1.jutif.2024.5.6.1802.
[7] E. P. Efendi and M. A. Barata, “KOMPARASI ALGORITMA SVM DAN C4 . 5 DENGAN BACKWARD ELIMINATION UNTUK KLASIFIKASI TINGKAT STRES MAHASISWA COMPARATIVE ANALYSIS OF SVM AND C4 . 5 ALGORITHMS WITH BACKWARD,” no. 3, pp. 539–549, 2024, doi: 10.26798/jiko.v9i3.2130.
[8] S. A. H. Bahtiar, C. K. Dewa, and A. Luthfi, “Comparison of Naïve Bayes and Logistic Regression in Sentiment Analysis on Marketplace Reviews Using Rating-Based Labeling,” J. Inf. Syst. Informatics, vol. 5, no. 3, pp. 915–927, 2023, doi: 10.51519/journalisi.v5i3.539.
[9] B. Ramadhani and R. R. Suryono, “Komparasi Algoritma Naïve Bayes dan Logistic Regression Untuk Analisis Sentimen Metaverse,” J. Media Inform. Budidarma, vol. 8, no. 2, p. 714, 2024, doi: 10.30865/mib.v8i2.7458.
[10] S. A. S. Mola, D. L. B. Baun, I. O. Nunes, and M. M. A. R. Sani, “Analisis Sentimen Aplikasi Halo Bca Di Google Play Store Menggunakan Metode Naive Bayes, Support Vector Machine Dan Random Forest,” HOAQ (High Educ. Organ. Arch. Qual. J. Teknol. Inf., vol. 15, no. 2, pp. 69–79, 2024, doi: 10.52972/hoaq.vol15no2.p69-79.
[11] C. Rahmawati and P. Sukmasetya, “Sentimen Analisis Opini Masyarakat Terhadap Kebijakan Kominfo atas Pemblokiran Situs non-PSE pada Media Sosial Twitter,” vol. 9, no. 5, pp. 1393–1400, 2022, doi: 10.30865/jurikom.v9i5.4950.
[12] N. Hadi and D. Sugiarto, “Analisis Sentimen Pembangunan IKN pada Media Sosial X Menggunakan Algoritma SVM, Logistic Regression dan Naïve Bayes,” J. Inform. J. Pengemb. IT, vol. 10, no. 1, pp. 37–49, 2025, doi: 10.30591/jpit.v10i1.7106.
[13] M. Algoritma, S. V. M. Dan, R. Forest, and F. N. Firzatullah, “Analisis Sentimen Pengguna Aplikasi Byond BSI Pada Google Play Store,” pp. 356–363, 2025, doi: 10.47002/metik.v9i2.1089.
[14] H. Barus, I. N. Fajri, and Y. Pristyanto, “Sentiment Classification Analysis of Tokopedia Reviews Using TF-IDF , SMOTE , and Traditional Machine Learning Models,” vol. 9, no. 5, pp. 2552–2561, 2025.
[15] D. N. Agustia, R. R. Suryono, U. T. Indonesia, L. Ratu, and K. B. Lampung, “COMPARISON OF NAÏVE BAYES , RANDOM FOREST , AND LOGISTIC REGRESSION ALGORITHMS FOR SENTIMENT ANALYSIS ONLINE GAMBLING KOMPARASI ALGORITMA NAÏVE BAYES , RANDOM FOREST , DAN LOGISTIC REGRESION UNTUK ANALISIS,” vol. 10, no. 1, pp. 284–295, 2025.
[16] R. T. Agustin, Y. Cahyana, K. A. Baihaqi, and T. Rohana, “Public Sentiment Analysis on the Boycott Israel Movement on Platform X Using Random Forest and Logistic Regression Algorithms,” vol. 9, no. 3, pp. 938–945, 2025.
[17] A. Y. Pratama, G. A. Sanjaya, N. K. Lubis, and M. R. Aditya, “Analisis Sentimen Publik Terkait Danantara Menggunakan Algoritma IndoBERT pada Platform Media Sosial,” 2025, doi: 10.47002/metik.v9i1.1055.
[18] A. N. Syafia, M. F. Hidayattullah, and W. Suteddy, “Studi Komparasi Algoritma SVM Dan Random Forest Pada Analisis Sentimen Komentar Youtube BTS,” vol. 8, no. 3, pp. 207–212, 2023.
[19] Z. T. Zafira, K. D. Tania, W. K. Sari, S. Informasi, and U. Sriwijaya, “Sentiment-Based Knowledge Discovery of Wondr by BNI App Reviews Using SVM , KNN , and Naive Bayes for CRM Enhancement,” vol. 9, no. 5, pp. 2498–2508, 2025.
[20] I. Yunanto and S. Yulianto, “TWITTER SENTIMENT ANALYSIS PEDULILINDUNGI APPLICATION USING NAÏVE BAYES AND SUPPORT VECTOR MACHINE ANALISIS SENTIMEN TWITTER APLIKASI PEDULILINDUNGI,” vol. 3, no. 4, pp. 807–814, 2022.
[21] R. R. Pratama, R. R. Suryono, S. Informasi, and U. T. Indonesia, “PERFORMANCE COMPARISON OF NAIVE BAYES , SUPPORT VECTOR MACHINE AND RANDOM FOREST ALGORITHMS FOR APPLE VISION PRO SENTIMENT ANALYSIS PERBANDINGAN PERFORMA ALGORITMA NAIVE BAYES , SUPPORT VECTOR MACHINE DAN RANDOM FOREST UNTUK ANALISIS SENTIMEN APPLE,” vol. 6, no. 1, pp. 31–39, 2025.
[22] I. Septiana and D. Alita, “Perbandingan Random Forest dan SVM dalam Analisis Sentimen Quick Count Pemilu 2024,” vol. 9, no. 3, pp. 224–233, 2024, doi: 10.30591/jpit.v9i3.6640.
[23] I. M. Sumertajaya, Y. Angraini, J. R. Harahap, and A. Fitrianto, “Sentiment Analysis on Covid-19 Vaccination in Indonesia Using Support Vector Machine and Random Forest,” vol. 10, no. 1, pp. 1–8, 2022.
[24] S. Sumayah, F. Sembiring, W. Jatmiko, J. S. Informasi, U. Nusa, and P. Sukabumi, “ANALYSIS OF SENTIMENT OF INDONESIAN COMMUNITY ON METAVERSE ANALISIS SENTIMEN MASYARAKAT INDONESIA TERHADAP METAVERSE,” vol. 4, no. 1, pp. 143–150, 2023.
[25] I. H. Kusuma and N. Cahyono, “Analisis Sentimen Masyarakat Terhadap Penggunaan E-Commerce Menggunakan Algoritma K-Nearest Neighbor,” vol. 8, no. 3, pp. 302–307, 2023
[26]F. S. Pratiwi et al., “IMPLEMENTASI METODE SMOTE DAN RANDOM OVER- SAMPLING PADA ALGORITMA MACHINE LEARNING UNTUK,” vol. 8, no. 1, pp. 87–98, 2025.
[27]S. Putri, A. F. Rozi, U. Mercu, B. Yogyakarta, and D. I. Yogyakarta, “SENTIMENT ANALYSIS OF TELEGRAM APPLICATION USER SATISFACTION ON GOOGLE PLAY STORE USING NAÏVE BAYES , LOGISTIC REGRESSION AND SVM ANALISIS SENTIMEN KEPUASAN PENGGUNA APLIKASI TELEGRAM PADA GOOGLE PLAY STORE MENGGUNAKAN METODE NAÏVE BAYES , LOGISTIC REGRESSION DAN SVM,” vol. 10, no. 2, pp. 857–867, 2025.
