Analisis Sentimen Ulasan Mobile JKN pada Playstore dengan Perbandingan Akurasi Algoritma Naïve Bayes dan SVM
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Eka Arya Pranata
*
Universitas Dian Nuswantoro, Semarang,
Indonesia
- Fikri Budiman Universitas Dian Nuswantoro, Semarang, Indonesia
- Defri Kurniawan Universitas Dian Nuswantoro, Semarang, Indonesia (*) Corresponding Author
Abstract
The facilities provided by BPJS Health by releasing the Mobile JKN application, with this application the administrative process that previously had to be done directly can be done online and more flexibly. This research aims to see the sentiment of the community towards the JKN Mobile application review by comparing the SVM and Naïve Bayes algorithms. As well as optimizing the Naïve Bayes algorithm by using grid search. Reviews are taken from Google play with the help of Google Play Scraper API, the dataset taken amounted to 7,000 reviews. The results of using Naïve Bayes with an accuracy value of 86%, after tuning optimization using Grid Search significantly increases the accuracy value of the Naïve Bayes algorithm to 91% and for the SVM algorithm has an accuracy value of 92%. From the trial, it was found that the SVM algorithm is still better than the Naïve Bayes algorithm even though it has been optimized, but by optimizing the accuracy value Naïve Bayes is closer to SVM performance. This research can provide insight into the comparison of the two algorithms in identifying JKN Mobile reviews and the need for optimization to improve the performance of algorithms in sentiment analysis, besides that this research also contributes to the improvement and development of the JKN Mobile application so that it is useful for the community.
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References
Z. Yunizar et al., “Analisis Sentimen Pada Twitter Terhadap Aplikasi Mobile Jkn Menggunakan Metode Naïve Bayes Classifier Sentiment Analysis on Twitter Regarding the Jkn Mobile Application Using the Naïve Bayes Classifier Method,” J. Informatics Comput. Sci., vol. 9, no. 2, pp. 103–111, 2023.
N. Maulida, N. Suarna, and W. Prihartono, “Analisis Ulasan Sentimen Aplikasi Mobile Jkn Dengan Algoritma Support Vector Machine Berbasis Particle Swarm Optimization,” JATI (Jurnal Mhs. Tek. Inform., vol. 8, no. 2, pp. 1651–1658, 2024, doi: 10.36040/jati.v8i2.9105.
M. Angelita, S. Lukman, and I. Tahir, “Inovasi Dan Efektivitas Pelayanan Melalui Mobile Jkn Pada Bpjs Kesehatan Di Jakarta Selatan,” Medium, vol. 9, no. 2, pp. 292–305, 2022, doi: 10.25299/medium.2021.vol9(2).10073.
T. Sugihartono and R. R. C. Putra, “Penerapan Metode Support Vector Machine Dalam Classifikasi Ulasan Pengguna Aplikasi Mobile Jkn,” SKANIKA Sist. Komput. dan Tek. Inform., vol. 7, no. 2, pp. 144–153, 2024, doi: 10.36080/skanika.v7i2.3193.
R. RINJANI and N. Sari, “Analisis Penerapan Aplikasi Mobile Jkn Terhadap Peserta Badan Penyelenggara Jaminan Sosial Kesehatan Cabang Subulussalam,” PUBLIKA J. Ilmu Adm. Publik, vol. 8, no. 2, pp. 209–223, 2022, doi: 10.25299/jiap.2022.vol8(2).10491.
O. B. Kusumawardhani, A. Octaviana, and Y. M. Supitra, “Efektivitas Mobile JKN Bagi Masyarakat : Literature Review,” Pros. Semin. Inf. Kesehat. Nas., pp. 64–69, 2022, [Online]. Available: https://ojs.udb.ac.id/index.php/sikenas/article/view/1665
I. Aida Sapitri and M. Fikry, “Pengklasifikasian Sentimen Ulasan Aplikasi Whatsapp Pada Google Play Store Menggunakan Support Vector Machine,” J. TEKINKOM, vol. 6, no. 1, pp. 1–7, 2023, doi: 10.37600/tekinkom.v6i1.773.
C. Annisa, M. Afdal, and T. K. Ahsyar, “Perbandingan Algoritma Naïve Bayes Classifier Dan K-Nearest Neighbor Pada Sentimen Review Aplikasi Mobile JKN,” J. MEDIA Inform. BUDIDARMA, vol. 7, no. 3, p. 1033, 2023, doi: 10.30865/mib.v7i3.6242.
E. A. Putri, “Penerapan Algoritma Naïve Bayes pada Analisis Sentimen Aplikasi Traveloka pada Platform Playstore,” vol. 6, no. 3, pp. 1467–1476, 2024, doi: 10.47065/bits.v6i3.6130.
D. Kurniawan, M. Najib, and D. Satria, “Analisis Sentimen Opini Publik Tentang Gempa Megathrust di Indonesia Menggunakan Metode Support Vector Machine dan Naïve Bayes,” Build. Informatics, Technol. Sci., vol. 6, no. 3, 2024, doi: 10.47065/bits.v6i3.6213.
Y. Ikhsani, I. Permana, F. N. Salisah, and N. E. Rozanda, “Perbandingan Algoritma Support Vector Machine dan Naïve Bayes dalam Menganalisis Sentimen Pinjaman Online di Twitter,” Build. Informatics, Technol. Sci., vol. 6, no. 3, 2024, doi: 10.47065/bits.v6i3.6106.
M. Sulhan, “Perbandingan Metode Naïve Bayes Dengan SVM Pada Analisis Sentimen Aplikasi Pemesanan Tiket Kapal Ferizy,” vol. 6, no. 4, pp. 0–9, 2025, doi: 10.47065/bits.v6i4.6715.
Tommy Suhendra, B. Intan, and A. T. Martadinata, “Analisis Sentimen Pengguna Aplikasi Bukalapak di Platform Playstore Menggunakan Metode Naïve Bayes,” ESCAF 3rd, vol. 2, no. 2, pp. 1011–1022, 2024, doi: 10.47065/bits.v6i2.5528.
A. Nurian, M. S. Ma’arif, I. N. Amalia, and C. Rozikin, “Analisis Sentimen Pengguna Aplikasi Shopee Pada Situs Google Play Menggunakan Naive Bayes Classifier,” J. Inform. dan Tek. Elektro Terap., vol. 12, no. 1, 2024, doi: 10.23960/jitet.v12i1.3631.
S. F. Amrilah, D. Krisbiantoro, and A. Prasetyo, “Penerapan Metode K-Nearest Neighbors dan Naïve Bayes pada Analisis Sentimen Pengguna Aplikasi Bstation melalui Platform Playstore,” Build. Informatics, Technol. Sci.,vol. 6, no. 3, pp. 1281–1292, 2024, doi: 10.47065/bits.v6i3.5863.
N. Wijaya and E. S. Panjaitan, “Analisis Sentimen Ulasan Aplikasi Instagram di Google Play Store : Pendekatan Multinomial Naive Bayes dan Berbasis Leksikon,” Build. Informatics, Technol. Sci., vol. 6, no. 2, pp. 921–929, 2024, doi: 10.47065/bits.v6i2.5615.
M. Gamma, A. Hakim, and F. Irwiensyah, “Analisis Sentimen Terhadap Ulasan Pengguna Pada Aplikasi BCA Mobile Menggunakan Metode Naïve Bayes,” J. Inf. Syst. Res., vol. 5, no. 4, pp. 911–921, 2024, doi: 10.47065/bits.v6i3.6119.
Y. Laia, S. Sandino Berutu, el Pieter Sumihar, and H. Budiati, “Implementasi Library Textblob dan Metode Support Vector Machine Pada Analisis Sentimen Pelanggan Terhadap Jasa Transportasi Online,” Technol. Sci., vol. 6, no. 1, pp. 1–10, 2024, doi: 10.47065/bits.v6i1.5090.
R. T. Aldisa and P. Maulana, “Analisis Sentimen Opini Masyarakat Terhadap Vaksinasi Booster COVID-19 Dengan Perbandingan Metode Naive Bayes, Decision Tree dan SVM,” Build. Informatics, Technol. Sci., vol. 4, no. 1, pp. 106–109, 2022, doi: 10.47065/bits.v4i1.1581.
K. S. Putri, I. R. Setiawan, and A. Pambudi, “Analisis Sentimen Terhadap Brand Skincare Lokal Menggunakan Naïve Bayes Classifier,” Technol. J. Ilm., vol. 14, no. 3, p. 227, 2023, doi: 10.31602/tji.v14i3.11259.
Sarimole Frencis Matheos and Septian Wahyu, “Analisis Sentimen Masyarakat Terhadap Isu Penundaan Pemilu 2024 Pada Twitter Dengan Metode Naive Bayes Dan Support Vector Machine,” J. Sains dan Teknol., vol. 5, no. 3, p. 2024, 2024, [Online]. Available: https://doi.org/10.55338/saintek.v5i1.2789
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