ANALISIS SENTIMEN OPINI MASYARAKAT TERKAIT PENGGUNAAN MOBILE JKN PADA MEDIA SOSIAL X MENGGUNAKAN ALGORITMA K-NEARESTNEIGHBOR (KNN) DAN LOGISTIC REGRESSION (LR)
Abstract
BPJS Kesehatan launched the Mobile JKN application in 2017 to enhance healthcare accessibility and simplify membership management. As of September 2024, 98.67% of Indonesia’s population has joined the JKN program, highlighting the importance of public opinion in the application’s development. This study analyzes public sentiment toward Mobile JKN through the social media platform X using the K-Nearest Neighbor (KNN) and Logistic Regression (LR) algorithms. From 4,777 reviews, 4,648 clean data were obtained after preprocessing and labeled using IndoBERT, resulting in 2,066 positive reviews and 2,581 negative reviews. The evaluation results show that KNN achieved an accuracy of 83.87%, while LR performed better with 87.85% along with higher precision, recall, and f1-score.
The findings reveal the dominance of negative sentiment in user reviews and provide insights for BPJS Kesehatan to improve Mobile JKN into a more responsive application that meets users’ needs and feedback.
References
Adya Febriana Putri, M., Adi Sastra Wijaya, K. and Wayan Supriliyani, N. (2024) ‘Efektivitas aplikasi Mobile Jaminan Kesehatan Nasional (JKN) dalam meningkatkan kualitas pelayanan (Studi kasus Kantor BPJS Cabang Denpasar)’, Communication and Policy Review, 1(2). Available at: https://ijespgjournal.org/index.php/shkr
Afdal, M., Rahma Elita, L. and Studi Sistem Informasi, P. (2022) ‘Penerapan text mining pada aplikasi Tokopedia menggunakan algoritma K-Nearest Neighbor’, Jurnal Ilmiah Rekayasa dan Manajemen Sistem Informasi, 8(1).
Alrajak, M.S. and Ernawati, I. (2020) ‘Analisis sentimen terhadap pelayanan PT PLN di Jakarta pada Twitter dengan algoritma K-Nearest Neighbor (K-NN)’.
Arifin, A.J. and Nugroho, A. (2023) ‘Uji akurasi penggunaan metode KNN dalam analisis sentimen kenaikan harga BBM pada media Twitter’.
Arifin, N., Enri, U. and Sulistiyowati, N. (2021) ‘Penerapan algoritma support vector machine (SVM) dengan TF-IDF n-gram untuk text classification’.
Auliya Agustina, D., Subanti, S. and Zukhronah, E. (2020) ‘Implementasi text mining pada analisis sentimen pengguna Twitter terhadap marketplace di Indonesia menggunakan algoritma support vector machine’.
Azis Adjie Sumanjaya, A. and Ridok, A. (2022) ‘Analisis sentimen data tweets terhadap penanganan Covid-19 di Indonesia menggunakan metode Naïve Bayes dan pemilihan kata bersentimen menggunakan lexicon based’, J-PTIIK, 6(4). Available at: http://j-ptiik.ub.ac.id
BPJS (2024) Data JKN.
Chandani, V. and Wahono, R.S. (2015) ‘Komparasi algoritma klasifikasi machine learning dan feature selection pada analisis sentimen review film’, Journal of Intelligent Systems, 1(1). Available at: http://journal.ilmukomputer.org
Dewi, S.P., Nurwati, N. and Rahayu, E. (2022) ‘Penerapan data mining untuk prediksi penjualan produk terlaris menggunakan metode K-Nearest Neighbor’, Building of Informatics, Technology and Science (BITS), 3(4), pp.639–648. doi:10.47065/bits.v3i4.1408
Dharmawan, L.R., Arwani, I. and Ratnawati, D.E. (2020) ‘Analisis sentimen pada sosial media Twitter terhadap layanan sistem informasi akademik mahasiswa Universitas Brawijaya dengan metode K-Nearest Neighbor’, J-PTIIK, 4(3). Available at: http://j-ptiik.ub.ac.id
Duei Putri, D., Nama, G.F. and Sulistiono, W.E. (2022) ‘Analisis sentimen kinerja Dewan Perwakilan Rakyat (DPR) pada Twitter menggunakan metode Naïve Bayes Classifier’, Jurnal Informatika dan Teknik Elektro Terapan, 10(1). doi:10.23960/jitet.v10i1.2262
Furqan, M. and Mayang Sari, S. (2022) ‘Analisis sentimen menggunakan K-Nearest Neighbor terhadap new normal masa Covid-19 di Indonesia’, Jurnal Teknologi Informasi, 21(1).
Hafiz, Y.A. and Sudarmilah, E. (2023) ‘Implementasi web scraping pada portal berita online’.
Harahap, E.P., Purnomo, H.D., Iriani, A., Sembiring, I. and Nurtino, T. (2024) ‘Trends in sentiment of Twitter users towards Indonesian tourism: analysis with the K-Nearest Neighbor method’, Computer Science and Information Technologies, 5(1), pp.19–28. doi:10.11591/csit.v5i1.pp19-28
Copyright (c) 2025 Jurnal Sistem Informasi (JUSIN)
This work is licensed under a Creative Commons Attribution 4.0 International License.
