Sentiment Analysis on Digital Korlantas POLRI Application Reviews Using the Distilbert Model

Nabila Rizky Amalia Putri; Trimono Trimono; Aviolla Terza Damaliana

doi:10.29103/jreece.v4i2.17197

Sentiment Analysis on Digital Korlantas POLRI Application Reviews Using the Distilbert Model

Nabila Rizky Amalia Putri, Trimono Trimono, Aviolla Terza Damaliana

Abstract

The implementation of digitalization in public services by Korlantas Polri has facilitated faster administration, wider access, and improved service quality. The Korlantas Polri Digital app has garnered more than 5 million downloads on the Google Play Store, with a rating of 3.7 and around 110 thousand reviews. Given that an app's reputation can be significantly affected by criticism, sentiment analysis becomes very important to categorize user reviews as positive, negative, or neutral, thus assisting developers in identifying app shortcomings. This study uses DistilBERT, a deep learning model distilled from BERT, to assess the effectiveness of sentiment analysis on reviews. Data was collected from user reviews on the Google Play Store between September 1, 2023 and May 31, 2024, resulting in 8,752 reviews retained for analysis. Model performance was evaluated at three data ratios: 60:40, 70:30, and 80:20, with the best performance results seen at a ratio of 80:20, achieving 88% accuracy. Increasing the training data ratio from 60:20 to 80:20 has a positive impact on the model, suggesting that the model can learn better with larger training data.

Keywords

Digitalization; Digital Korlantas Polri; Sentiment Analysis; DistilBERT

Full Text:

PDF

References

Aulia, S., & Maulana, A. (2023). Daftar Satpas Yang Menerima Pengajuan SIM Secara Online. Kompas.Com.

Cahyani, L. I., Fiiki Nurrohman, M., Setiowati, M. I., Taufiq, R. I., & Mukti, A. (2021). Transformasi Manajemen Kepolisian Melalui Pelayanan Publik Berbasis Aplikasi Sim Nasional Presisi (Sinar). Jurnal Mahasiswa Administrasi Negara (JMAN), 5(2), 34–41.

Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2019). BERT: Pre-Training Of Deep Bidirectional Transformers For Language Understanding. NAACL HLT 2019 - 2019 Conference Of The North American Chapter Of The Association For Computational Linguistics: Human Language Technologies - Proceedings Of The Conference, 1(Mlm), 4171–4186.

Fajri, F., Tutuko, B., & Sukemi, S. (2022). Membandingkan Nilai Akurasi BERT Dan Distilbert Pada Dataset Twitter. JUSIFO (Jurnal Sistem Informasi), 8(2), 71–80. Https://Doi.Org/10.19109/Jusifo.V8i2.13885

Hermawan, A., Jowensen, I., Junaedi, J., & Edy. (2023). Implementasi Text-Mining Untuk Analisis Sentimen Pada Twitter Dengan Algoritma Support Vector Machine. JST (Jurnal Sains Dan Teknologi), 12(1), 129–137. Https://Doi.Org/10.23887/Jstundiksha.V12i1.52358

Kang, Y., Cai, Z., Tan, C.-W., Huang, Q., & Liu, H. (2020). Natural Language Processing (NLP) In Management Research: A Literature Review. Journal Of Management Analytics, 7(2), 139–172.

Khatoon, S., Romman, L. A., & Hasan, M. M. (2020). Domain Independent Automatic Labeling System For Large-Scale Social Data Using Lexicon And Web-Based Augmentation. Information Technology And Control, 49(1), 36–54.

Maharani, T., & Meiliana, D. (2021). Korlantas Polri Luncurkan Aplikasi SINAR, Perpanjang SIM Cukup Lewat Handphone. Kompas.Com.

Mahira Putri, Sutanto, T. E., & Inna, S. (2023). Studi Empiris Model BERT Dan Distilbert Analisis Sentimen Pada Pemilihan Presiden Indonesia. Indonesian Journal Of Computer Science, 12(5), 2972–2980. Https://Doi.Org/10.33022/Ijcs.V12i5.3445

Mhd. Zamil. (2019). Klasifikasi Kalimat Ofensif Pada Media Sosial Twitter Menggunakan Metode Naïve Bayes Classifier. Universitas Islam Negeri Sultan Syarif Kasim Riau, 2(5), 107–110.

Pasek, P., Mahawardana, O., Arya, G., Agus, I. P., & Pratama, E. (2022). Analisis Sentimen Berdasarkan Opini Dari Media Sosial Twitter Terhadap “ Figure Pemimpin ” Menggunakan Python. JITTER- Jurnal Ilmiah Teknologi Dan Komputer, 3(1).

Prasetiya, A. Y., & Niswah, F. (2020). Strategi Peningkatan Pelayanan Pembayaran Surat Izin Mengemudi Melalui Program Cashless Payment System ( CPS ) Di Kantor Satlantas Polres Gresik. Publika, 8(4), 1–10.

Putra, S. H. W., & Febriawan, D. (2024). Analisis Sentimen Ulasan Aplikasi Digital Korlantas POLRI Menggunakan Naïve Bayes Pada Google Play Store. KLIK: Kajian Ilmiah Informatika Dan Komputer, 4, 1962–1971. Https://Doi.Org/10.30865/Klik.V4i4.1600

Sanh, V., Debut, L., Chaumond, J., & Wolf, T. (2019). Distilbert, A Distilled Version Of BERT: Smaller, Faster, Cheaper And Lighter. 2–6.

Sutrisno, E. P., & Amini, S. (2023). Implementasi Algoritma K-Nearest Neighbor Pada Implementation Of K-Nearest Neighbor Algorithm In Sentiment Analysis Of User Reviews For Digital. 2(April 2021), 687–695.

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention Is All You Need. Advances In Neural Information Processing Systems, 2017-Decem(Nips), 5999–6009.

Wankhade, M., Rao, A. C. S., & Kulkarni, C. (2022). A Survey On Sentiment Analysis Methods, Applications, And Challenges. Artificial Intelligence Review, 55(7), 5731–5780.

Wiranti, N. E., & Frinaldi, A. (2023). Meningkatkan Efisiensi Pelayanan Publik Dengan Teknologi Di Era Digital. JIM: Jurnal Ilmiah Mahasiswa Pendidikan Sejarah, 8(2), 748–754.

Yulanda, A., & Fachri Adnan, M. (2023). Transformasi Digital: Meningkatkan Efisiensi Pelayanan Publik Ditinjau Dari Perspektif Administrasi Publik. Jurnal Ilmu Sosial Dan Humaniora (Isora), 1(3), 103–110.

Yunitasari, Y., Musdholifah, A., & Sari, A. K. (2019). Sarcasm Detection For Sentiment Analysis In Indonesian Tweets. IJCCS (Indonesian Journal Of Computing And Cybernetics Systems), 13(1), 53. Https://Doi.Org/10.22146/Ijccs.41136

DOI: https://doi.org/10.29103/jreece.v4i2.17197