SENTIMENT ANALYSIS OF USER REVIEWS BRI MOBILE APPLICATION WITH GRADIENT BOOST METHOD

Nanang Ruhyana; Kanita Salsabila; Andri Agung; Tati Mardiana

doi:10.34288/jri.v7i2.342

Authors

Nanang Ruhyana Universitas Nusa Mandiri
Kanita Salsabila Universitas Nusa Mandiri
Andri Agung Universitas Nusa Mandiri
Tati Mardiana Universitas Nusa Mandiri

(*) Corresponding Author

DOI:

https://doi.org/10.34288/jri.v7i2.342

Keywords:

BRI Mobile, Review, Sentiment, Gradient Boost

Abstract

BRI Mobile application is a digital banking service launched in 2019 by Bank Rakyat Indonesia, which provides facilities such as mobile banking, internet banking, and electronic money. The presence of this application aims to facilitate customers in accessing and managing financial services efficiently through mobile devices. Reviews have become a very important source on platforms such as Google Playstore become a very important source of information to evaluate and improve service quality. However, manually identifying sentiment representations from thousands of reviews is a time-consuming and inefficient process. This research aims to perform sentiment analysis automatically on BRI Mobile application user reviews by utilizing text mining methods. The sentiment classification process is carried out using the Gradient Boosting algorithm approach and initial analysis using the VADER Sentiment method to provide initial data labelling. Based on the classification results, 344 data with positive sentiment, 333 data with negative sentiment, and 333 data with neutral sentiment were obtained. The model built was then evaluated using the accuracy metric, and an accuracy value of 97% was obtained. The results of this research are expected to be a strategic input for application developers in understanding user perceptions more objectively and efficiently.

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References

Arminda, Nanda Fibriyanti, Nina Sulistiyowati, and Tesa Nur Padilah. 2023. “Implementasi Algoritma Multinomial Naive Bayes Pada Analisis Sentimen Terhadap Ulasan Pengguna Aplikasi BRIMO.” Jurnal Mahasiswa Teknik Informatika 7(3):1817–22.

Arum Prabowo, Galih, Basuki Rahmat, and Henni Endah Wahanani. 2024. “Aspect-Based Sentiment Analysis Iphone 14 Pro Menggunakan Algoritma XGBoost.” JATI (Jurnal Mahasiswa Teknik Informatika) 7(6):3947–52. doi: 10.36040/jati.v7i6.7831.

Astuti, Anggi Puji, Syariful Alam, and Irsan Jaelani. 2022. “Komparasi Algoritma Support Vector Machine Dengan Naive Bayes Untuk Analisis Sentimen Pada Aplikasi BRImo.” Bangkit Indonesia XI(02).

Efendi, and Rina Noviana. 2021. “Perancangan Web Sistem Analisis Sentimen Media Sosial Twitter Dengan Metode Valence Aware Dictionary And Sentimen Reasoner (Vader) Menggunakan PHP & MysSQL Pada Pemerintah Kota Bekasi.” Jurnal Ilmiah Komputasi 20(1). doi: 10.32409/jikstik.20.1.369.

Hadisantoso, Erwin, Tuti Dharmawati, Muhammad Asdar, Muhammad Syaiful Saehu, and Sulfian Sulfian. 2023. “Faktor-Faktor Yang Mempengaruhi Minat Penggunaan Uang Elektronik (E-Money) Pada Aplikasi OVO (Studi Empiris Pada Mahasiswa Akuntansi Universitas Halu Oleo).” Jurnal Akuntansi Dan Keuangan 8(1).

Hasibuan, Ernianti, and Elmo Allistair Heriyanto. 2022. “Analisis Sentimen Pada Ulasan Aplikasi Amazon Shopping Di Google Playstore Menggunakan Naive Bayes Classifier.” JTS Jurnal Teknik Dan Science 1(3).

Hendrawan, Ivan Rifky. 2022. “Perbandingan Algoritma Naive Bayes, SVM, Dan XGBoost Dalam Klasifikasi Teks Sentimen Masyarakat Terhadap Produk Lokal Di Indonesia.” TRANSFORMASI 18(1). doi: 10.56357/jt.v18i1.295.

Imam, Muhamad, and Syafii Informatika. 2023. “Sentimen Analisis Pada Media Sosial Menggunakan Metode Naive Bayes Classifier (NBC).” Teknologipintar.Org 3(2):1.

Ismamudi, Ismamudi, Nani Hartati, and Sakum Sakum. 2023. “Peran Bank Dan Lembaga Keuangan Dalam Pengembangan Ekonomi: Tinjauan Literatur.” Jurnal Akuntansi Neraca 1(2):35–44. doi: 10.59837/jan.v1i2.10.

Moh Hasan Basri. 2024. “Pemodelan Topik Dan Analisis Sentimen Pada Teks Ulasan Pengguna Aplikasi Perbankan Seluler Di Indonesia.” The Indonesian Journal of Computer Science 13(4). doi: 10.33022/ijcs.v13i4.4200.

Multhazam, Riana, Marhawati Marhawati, Muhammad Hasan, Mustari Mustari, and Muh. Ihsan Said. 2021. “Analisis Faktor-Faktor Yang Mempengaruhi Minat Penggunaan Uang Elektronik Pada Aplikasi OVO.” Business and Accounting Education Journal 2(3):252–59. doi: 10.15294/baej.v2i3.55829.

Mulyadi, Achmad Hari, and Sri Lestari. 2022. “Analisis Sentimen Terhadap Sekolah Saat Covid-19 Pada Twitter Menggunakan Metode Lexicon Based.” JIKOM Jurnal Informatika Dan Teknologi Komputer 3(1):17–23.

Nurfadila, Nida, Maya Ariyanti, and Nurvita Trianasari. 2023. “Analisis Kualitas Layanan Mobile Banking New Livin’ By Mandiri Menggunakan Sentiment Analysis.” Journal of Indonesia Business Research 1(1).

Ridwansyah, Tengku. 2022. “Implementasi Text Mining Terhadap Analisis Sentimen Masyarakat Dunia Di Twitter Terhadap Kota Medan Menggunakan K-Fold Cross Validation Dan Naïve Bayes Classifier.” KLIK Kajian Ilmiah Informatika Dan Komputer 2(5):178–85.

Umair, Muhammad, and Erliyan Redy Sutanto. 2024. “Analisis Sentimen Ulasan Pengguna Pada Aplikasi BRImo BRI Menggunakan Metode Klasifikasi Algoritma Naive Bayes.” JURNAL MEDIA INFORMATIKA BUDIDARMA 8:1149–59. doi: 10.30865/mib.v8i2.7381.

Utomo, Danang Wahyu. 2024. “Rekomendasi Produk E-Commerce Berbasis Klasifikasi Ulasan Menggunakan Ensemble Random Forest Dan Teknik Boosting.” INFOTEKMESIN 15(2).

Yulia Nur Afifah, Anisatul Kamilah, Ahmad Indriyanto Nugroho, and Ayyu Ainin Mustafidah. 2024a. “Optimalisasi BRI Mobile Sebagai Bentuk Previlege Kepada Nasabah Di BRI Unit Puger.” Journal Of Indonesian Social Society (JISS) 2(2):64–68. doi: 10.59435/jiss.v2i2.232.

Yulia Nur Afifah, Anisatul Kamilah, Ahmad Indriyanto Nugroho, and Ayyu Ainin Mustafidah. 2024b. “Optimalisasi BRI Mobile Sebagai Bentuk Previlege Kepada Nasabah Di BRI Unit Puger.” Journal Of Indonesian Social Society (JISS) 2(2):64–68. doi: 10.59435/jiss.v2i2.232.

Yuniar, Eka, Dwi Safiroh Utsalinah, and Dian Wahyuningsih. 2022. “Implementasi Scrapping Data Untuk Sentiment Analysis Pengguna Dompet Digital Dengan Menggunakan Algoritma Machine Learning.” Jurnal Janitra Informatika Dan Sistem Informasi 2(1):35–42. doi: 10.25008/janitra.v2i1.145.

Zelina, Nur, and Afiyati Afiyati. 2024. “Analisis Sentimen Ulasan Pengguna Aplikasi M-Banking Mengunakan Algoritma Support Vector Machine Dan Desicion Tree.” Jurnal Linguistik Komputasional 7(1).