IMPLEMENTASI METODE BIDIRECTIONAL ENCODER REPRESENTATIONS FROM TRANSFORMERS UNTUK ANALISIS SENTIMEN TERHADAP ULASAN APLIKASI ACCESS
Universitas Gunadarma
Indonesia
Universitas Gunadarma
Indonesia
Article Submitted: 09 September 2024
Article Published: 14 December 2024
Abstract
Technological developments in this digital era are growing rapidly in various fields, one of which is the field of public transportation. The purpose of this study is to conduct a sentiment analysis of Access by KAI application users on the Google Play Store so that it can be used as a suggestion to improve the quality of the application. This paper uses the Bidirectional Encoding Representations from Transformers (BERT) method with the pretrained IndoBERT model to train the Indonesian dataset. This writing method uses the CRISP-DM method with 6 stages, namely Business Understanding, Data Understanding, Data Preparation, Modelling, Evaluation, and Deployment. The dataset used was 10,000 reviews and after being processed into 9260. The model that was built managed to predict sentiment quite well with a percentage of 85%. However, in neutral sentiment, the number of wrong predictions was more than the number of correct predictions, which was 22 reviews, and the number of wrong predictions, which was 150 reviews. The number of correct predictions for negative sentiment is 2,822 reviews and the number of wrong predictions is 345 reviews. The number of correct predictions for positive sentiment was 234 reviews and the number of wrong predictions was 131 reviews. The model has also been successfully deployed in the form of a website prototype and can strengthen sentiment predictions quite well.
Keywords
References
A. Nurian, “Analisis Sentimen Ulasan Pengguna Aplikasi Google Play Menggunakan Naïve Bayes,” J. Inform. dan Tek. Elektro Terap., vol. 11, no. 3s1, pp. 829–835, 2023, doi: 10.23960/jitet.v11i3s1.3348.
H. Jayadianti, W. Kaswidjanti, A. T. Utomo, S. Saifullah, F. A. Dwiyanto, and R. Drezewski, “Sentiment analysis of Indonesian reviews using fine-tuning IndoBERT and R-CNN,” Ilk. J. Ilm., vol. 14, no. 3, pp. 348–354, 2022, doi: 10.33096/ilkom.v14i3.1505.348-354.
R. Mas, R. W. Panca, K. Atmaja1, and W. Yustanti2, “Analisis Sentimen Customer Review Aplikasi Ruang Guru dengan Metode BERT (Bidirectional Encoder Representations from Transformers),” Jeisbi, vol. 02, no. 03, p. 2021, 2021.
J. U. S. Lazuardi and A. Juarna, “Analisis Sentimen Ulasan Pengguna Aplikasi Joox Pada Android Menggunakan Metode Bidirectional Encoder Representation From Transformer (Bert),” J. Ilm. Inform. Komput., vol. 28, no. 3, pp. 251–260, 2023, doi: 10.35760/ik.2023.v28i3.10090.
R. Kusnadi, Y. Yusuf, A. Andriantony, R. Ardian Yaputra, and M. Caintan, “Analisis Sentimen Terhadap Game Genshin Impact Menggunakan Bert,” Rabit J. Teknol. dan Sist. Inf. Univrab, vol. 6, no. 2, pp. 122–129, 2021, doi: 10.36341/rabit.v6i2.1765.
N. Z. Rania and R. D. Syah, “Analisis Sentimen Terhadap Aplikasi Gojek Pada Play Store Menggunakan Metode Random Forest Classifier,” J. Ilm. Inform. Komput., vol. 29, no. 2, pp. 144–153, Jul. 2024, doi: 10.35760/ik.2024.v29i2.11877.
S. M. Fani, R. Santoso, and S. Suparti, “Penerapan Text Mining Untuk Melakukan Clustering Data Tweet Akun Blibli Pada Media Sosial Twitter Menggunakan K-Means Clustering,” J. Gaussian, vol. 10, no. 4, pp. 583–593, 2021, doi: 10.14710/j.gauss.v10i4.30409.
R. Nurul Ikhsani and F. Fauzi Abdulloh, “Optimasi SVM dan Decision Tree Menggunakan SMOTE Untuk Mengklasifikasi Sentimen Masyarakat Mengenai Pinjaman Online,” J. Media Inform. Budidarma, vol. 7, no. 4, pp. 1667–1677, 2023, doi: 10.30865/mib.v7i4.6809.
D. Musfiroh, U. Khaira, P. E. P. Utomo, and T. Suratno, “Analisis Sentimen terhadap Perkuliahan Daring di Indonesia dari Twitter Dataset Menggunakan InSet Lexicon,” MALCOM Indones. J. Mach. Learn. Comput. Sci., vol. 1, no. 1, pp. 24–33, 2021, doi: 10.57152/malcom.v1i1.20.
R. R. Salam, M. F. Jamil, Y. Ibrahim, R. Rahmaddeni, S. Soni, and H. Herianto, “Analisis Sentimen Terhadap Bantuan Langsung Tunai (BLT) Bahan Bakar Minyak (BBM) Menggunakan Support Vector Machine,” MALCOM Indones. J. Mach. Learn. Comput. Sci., vol. 3, no. 1, pp. 27–35, 2023, doi: 10.57152/malcom.v3i1.590.
A. Santosa, I. Purnamasari, and Mayasari Rini, “Pengaruh Stopword Removal dan Stemming Terhadap Performa Klasifikasi Teks Komentar Kebijakan New Normal Menggunakan Algoritma LSTM,” J. Sains Komput. Inform., vol. 6, no. 1, pp. 81–93, 2022.
A. S. dan N. Surojudin, “Analisis Dan Perbandingan Stemming Algoritma Porter Dengan Algoritma Ahmad Yusoff Sembok Dalam Dokumen Teks Bahasa Indonesia,” Pros. Semin. SeNTIK, vol. 4, no. 1, pp. 347–357, 2020, [Online]. Available: https://ejournal.jak-stik.ac.id/index.php/sentik/article/view/3304.
A. E. Budiman and A. Widjaja, “Analisis Pengaruh Teks Preprocessing Terhadap Deteksi Plagiarisme Pada Dokumen Tugas Akhir,” J. Tek. Inform. dan Sist. Inf., vol. 6, no. 3, pp. 475–488, 2020, doi: 10.28932/jutisi.v6i3.2892.
A. Awalina, F. A. Bachtiar, and F. Utaminingrum, “Perbandingan Pretrained Model Transformer pada Deteksi Ulasan Palsu,” J. Teknol. Inf. dan Ilmu Komput., vol. 9, no. 3, pp. 597–604, 2022, doi: 10.25126/jtiik.2022935696.
M. Fadli and R. A. Saputra, “Klasifikasi Dan Evaluasi Performa ModelRandom Forest Untuk Prediksi Stroke,” JT J. Tek., vol. 12, no. 2, pp. 72–80, 2023, [Online]. Available: http://jurnal.umt.ac.id/index.php/jt/index.
