DOI: https://doi.org/10.14710/j.gauss.15.1.36-45

PENERAPAN METODE RANDOM FOREST UNTUK ANALISIS SENTIMEN PENGGUNA APLIKASI BANK DIGITAL SEABANK

*Fenansia Clara Hana Bangun  -  Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro, Indonesia
Mustafid Mustafid  -  Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro, Indonesia
Deby Fakhriyana  -  Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro, Indonesia
Open Access Copyright 2026 Jurnal Gaussian under http://creativecommons.org/licenses/by-nc-sa/4.0.

Citation Format:
Abstract
Sentiment analysis on reviews of an application becomes an option to see users responses to the service of that particular application. Random Forest is one of the classification modeling techniques that originate from a combination of Decision Trees, providing the final result based on majority voting. This research aims to improve the performance of sentiment classification on customer reviews of Seabank, one of the most widely used digital banking services in Indonesia, by utilizing the Random Forest algorithm. The study involves sentiment analysis of user reviews on the Seabank application, collected from 15,000 reviews on Google Playstore. The review features available on Google Playstore are used as a means to convey opinions as user feedback for an application. Random Forest is trained to classify reviews into 3 sentiment classes: positive, neutral, and negative. Based on the research conducted with model evaluation using Confusion Matrix, an accuracy value of 94.1% was obtained, indicating that Random Forest's accuracy in classifying Seabank customer reviews is 94.1%. This demonstrates the effectiveness of using Random Forest in text review classification due to its high accuracy value.

Note: This article has supplementary file(s).

Fulltext View|Download |  Research Instrument
CTA Jurnal Gaussian Fenansia Clara Hana Bangun 24050119130147
Subject
Type Research Instrument
  Download (416KB)    Indexing metadata
Email colleagues
Keywords: Classification; Random Forest; Confusion Matrix

Article Metrics:

Article Info
Section: Articles
Language : EN
  1. Adiyana, & Hakim. (2015). Implementasi Text Mining pada Mesin Pencarian Twitter untuk Menganalisis Topik Topik terkait KPK dan Jokowi. Seminar Nasional Universitas Muhammadiyah
  2. Breiman, L. (2001). Random Forest. Machine Learning, 5-32
  3. Damanik, R. M. (2014). Pengembangan Aplikasi Pencarian Dokumen Menggunakan Text Mining Berbasis Web. Jurnal Universitas Atma Jaya
  4. Feldman, & Sanger. (2007). The Text Mining Handbook. New York: Cambridge
  5. Harinaldi. (2005). Prinsip-Prinsip Statistik Untuk Teknik dan Sains. Jakarta: Erlangga
  6. Larose, & Daniel. (2005). Discovering Knowledge In Data : An Introduction to Data Mining. John Willey & Sons, Inc
  7. Liu. (2012). Sentiment Analysis and Opinion Mining. Morgan & Claypool Publisher
  8. Mahendrajaya, Rachmad, Buntoro, Ghulam Asrofi, Setyawan, & M Bhanu. (2019). Analisis Sentimen Pengguna Gopay Menggunakan Metode Lexicon Based dan Support Vector Machine. Repository Muhammadiyah University of Ponorogo, 52-63
  9. Melita R, Amrizal V, Suseno H B, & Dirjam T. (2018). Penerapan Metode Term Frequency Inverse Document Frequency (Tf-Idf) Dan Cosine Similarity Pada Sistem Temu Kembali Informasi Untuk Mengetahui Syarah Hadits Berbasis Web ( Studi Kasus : Syarah Umdatil Ahkam ). Jurnal Teknik Informatika, 11, 149-165
  10. Mujilahwati S. (2016). Pre-Processing Text Mining Pada Data Twitter. Seminar Nasional Teknologi Informasi dan Komunikasi
  11. Refaeilzadeh, Lei Tang, & Huan Liu. (2009). Cross-Validation. Encyclopedia of Database Systems, 532-538
  12. Sahoo, P. (2015). Probability and Mathematical Statistics. USA: University of Louisville
  13. Santoso, B. (2007). Data Mining : Teknik Pemanfaatan Data Untuk Keperluan Bisnis. Yogyakarta: Garah Ilmu
  14. Tamardina, F. A., Yasin, H., & Ispriyanti, D. (2022). ANALISIS SENTIMEN REVIEW APLIKASI CRYPTOCURRENCY MENGGUNAKAN ALGORITMA MAXIMUM ENTROPY DENGAN METODE PEMBOBOTAN TF, TF-IDF DAN BINARY. Jurnal Gaussian, 01-10
  15. Walpole, R. (1995). Pengantar Statistika, Edisi Ke-3. Jakarta: Gramedia Pustaka Utama

Last update:

No citation recorded.

Last update:

No citation recorded.