Twitter Comment Predictions on Dues Changes BPJS Health In 2020
DOI:
10.33395/sinkron.v5i1.10588Keywords:
Bpjs Health, dues, predictions, comments, Twitter, Ensembles VoteAbstract
The Social Security Administering Body (BPJS) is a facility established by The government in providing services to citizens in The field of health welfare. The Spirit of cooperation in the utilization of health services which is very much currently a constraint in the budget is still insufficient in covering health services as a whole. For this reason, government policy is following with PERPRES No. 75 in 2019, the Government officially raised the BPJS Health contributions for 2020. The increase in BPJS Health contributions certainly caused a lot of comments. Namely Twitter, one of the social media that is used by the public to express disapproval or support for this government policy. This study, testing was carried out related to the prediction of comments from social media on community responses to the increase in BPJS Health contributions taken by the government. In the test carried out 3 (three) input algorithms. For every single algorithm including getting results through the K-NN method with an accuracy of 71.83% and AUC value of 0812, for the Naïve Bayes method produces an accuracy of 81.63% and AUC value of 0586. As for the C 4.5 method, the accuracy is 65.37% and the AUC value is 0628. While testing conducted through the Ensembles Vote method which combines the 3 algorithms above gives the best results with an accuracy of 80.10% and AUC value is 0871 for Twitter comment predictions.
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Amriana, A., Joefrie, Y. Y., & Meidji, F. N. (2019). Penerapan Data Mining Untuk Pengelompokan Hasil Diagnosa Penyakit Pasien Pengguna BPJS Kesehatan (Studi Kasus Pada Rsud Undata Palu). ScientiCO : Computer Science and Informatics Journal, 1(1), 51. https://doi.org/10.22487/j26204118.2018.v1.i1.11901
Amrullah, A. A., Tantoni, A., Hamdani, N., Bau, R. T. R. L., Ahsan, M. R., & Utami, E. (2016). Reviewatas Analisis Sentimen Pada Twitter Sebagai Representasi Opini Publik Terhadap Bakal Calon Pemimpin. Prosiding Seminar Nasional Multi Disiplin Ilmu & Call for Papers Unisbank (Sendi_U), 2(1), 978–979.
Amriana, A., Joefrie, Y. Y., & Meidji, F. N. (2019). Penerapan Data Mining Untuk Pengelompokan Hasil Diagnosa Penyakit Pasien Pengguna BPJS Kesehatan (Studi Kasus Pada Rsud Undata Palu). ScientiCO : Computer Science and Informatics Journal, 1(1), 51. https://doi.org/10.22487/j26204118.2018.v1.i1.11901
Aristianto, D., & Finandhita, A. (2015). Penerapan Data Mining Untuk Mengklasifikasikan Penyakit Berdasarkan Wilayah Dengan Metode Klasifikasi dan Algoritma C4.5.
Aprilla Donny Aji Baskoro Lia Ambarwati I Wayan Simri Wicaksana Editor, D. C., & Sanjaya, R. (n.d.). Identitas Belajar Data Mining dengan RapidMiner Hak Cipta © pada Penulis Hak Guna mengikuti Open Content model Desain sampul: Dennis Aprilla C.
Bina Nusantara, U. (2018). 10 Fold-Cross Validation.
Bruno, L. (2019). 済無No Title No Title. Journal of Chemical Information and Modeling, 53(9), 1689–1699. https://doi.org/10.1017/CBO9781107415324.004
Deviyanto, A., & Wahyudi, M. D. R. (2018). Penerapan Analisis Sentimen Pada Pengguna Twitter Menggunakan Metode K-Nearest Neighbor. JISKA (Jurnal Informatika Sunan Kalijaga), 3(1), 1. https://doi.org/10.14421/jiska.2018.31-01
Dzikrulloh, N. N., & Setiawan, B. D. (2017). Penerapan Metode K – Nearest Neighbor ( KNN ) dan Metode Weighted Product ( WP ) Dalam Penerimaan Calon Guru Dan Karyawan Tata Usaha Baru Berwawasan Teknologi ( Studi Kasus : Sekolah Menengah Kejuruan Muhammadiyah 2 Kediri ). Pengembangan Teknologi Informasi Dan Ilmu Komputer, 1(5), 378–385.
Ertek, G., Tapucu, D., & Arin, I. (2013). Text mining with rapidminer. In RapidMiner: Data Mining Use Cases and Business Analytics Applications. https://doi.org/10.1201/b16023-21
Ferdiana, R., Jatmiko, F., Purwanti, D. D., Sekar, A., Ayu, T., Dicka, W. F., … Indonesia, B. (2019). Dataset Indonesia untuk Analisis Sentimen. 8(4), 334–339.
Han, J., Kamber, M., & Pei, J. (2012). Data Mining: Concepts and Techniques. In Data Mining: Concepts and Techniques. https://doi.org/10.1016/C2009-0-61819-5
Heristian, S., Program, M., Ilmu, S., Tinggi, S., Informatika, M., Komputer, D. A. N., & Mandiri, N. (2019). ANALISIS SENTIMEN BERITA ONLINE TERHADAP EKONOMI INDONESIA DENGAN METODE NAÏVE BAYES CLASSIFIER DAN SUPPORT VECTOR MACHINE.
Hermanto, B., & SN, A. (2017). Klasifikasi Nilai Kelayakan Calon Debitur Baru Menggunakan Decision Tree C4.5. IJCCS (Indonesian Journal of Computing and Cybernetics Systems), 11(1), 43. https://doi.org/10.22146/ijccs.15946
Ignatow, G., & Mihalcea, R. (2018). Sentiment Analysis. https://doi.org/10.4135/9781483399782.n14
Informatikalogi. (2017). Algoritma Naïve Bayes | INFORMATIKALOGI. Retrieved from Informatikalogi website: https://informatikalogi.com/algoritma-naive-bayes/
Kabari, L. G., & Onwuka, U. C. (2019). Comparison of Bagging and Voting Ensemble Machine Learning Algorithm as a Classifier. International Journals of Advanced Research in Computer Science and Software Engineering, 9(3), 19–23. Retrieved from www.ijarcsse.com,
Kesehatan, K., & Indonesia, R. (2011). Kementerian Kesehatan Republik Indonesia Tahun 2011.
Kemenkes no. 82. (2018). Peraturan Presiden Republik Indonesia No. 82 Tahun 2018 Tentang Jaminan Kesehatan (p. 74). p. 74.
Luthfi, K. A. E. T. (2009). Algoritma Data Mining Yogyakarta. Yogyakarta, Indonesia: Andi Offset.
Lestari, M. (2014). Penerapan Algoritma Klasifikasi Nearest Neighbor (K-NN) untuk Mendeteksi Penyakit Jantung. Faktor Exacta, 7(September 2010), 366–371.
Maimon, O, Rokach, L. (2011). Data mining and knowledge discovery handbook. In Choice Reviews Online (Vol. 48). https://doi.org/10.5860/choice.48-5729
Mentari, N. D., Fauzi, M. A., & Muflikhah, L. (2018). Analisis Sentimen Kurikulum 2013 Pada Sosial Media Twitter Menggunakan Metode K-Nearest Neighbor dan Feature Selection Query Expansion Ranking. Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer (J-PTIIK) Universitas Brawijaya, 2(8), 2739–2743.
Nawawi, H. M., Purnama, J. J., Hikmah, A. B., Komputer, S. I., Informasi, S. S., Bina, U., & Informatika, S. (2019). Komparasi Algoritma Neural Network dan Naive Bayes untuk Memprediksi Penyakit Jantung (Pascasarjana Magister Ilmu Komputer STMIK Nusa Mandiri; Vol. 15). https://doi.org/10.33480/pilar.v15i2.669
Nurhuda, F., Widya Sihwi, S., & Doewes, A. (2016). Analisis Sentimen Masyarakat terhadap Calon Presiden Indonesia 2014 berdasarkan Opini dari Twitter Menggunakan Metode Naive Bayes Classifier. Jurnal Teknologi & Informasi ITSmart, 2(2), 35. https://doi.org/10.20961/its.v2i2.630
peraturan badan penyelenggara jaminan sosial. (2018). Peraturan Badan Penyelenggara Jaminan Sosial Kesehatan No 1 Thn 2018. 1–16.
Princy Christy, A., & Rama, N. (2019). Improving prediction capability of ensemble of classifiers through weighted average probabilities. International Journal of Engineering and Advanced Technology, 8(5), 2540–2543.
Rana, S., & Singh, A. (2017). Comparative analysis of sentiment orientation using SVM and Naive Bayes techniques. Proceedings on 2016 2nd International Conference on Next Generation Computing Technologies, NGCT 2016, (October), 106–111. https://doi.org/10.1109/NGCT.2016.7877399
Rosdiansyah, D. (2014). Analisis Sentimen Twitter Menggunakan Metode K-Nearest Neighbor dan Pendekatan Lexicone. Tugas Akhir Jurusan Teknik Informatika, 1–15.
Rosyidi, R. (2019). PERBANDINGAN ALGORITMA K-NN DAN CART PADA DATA. 4(2), 169–177.
Salini, A., Jeyapriya, U., College, S. M., & College, S. M. (2018). A Majority Vote Based Ensemble Classifier for Predicting Students Academic Performance. International Journal of Pure and Applied Mathematics, 118(24), 1–11.
Sentimen, A., Menilai, U., Terhadap, M., Kementrian, K., Pembangunan, K., Dan, M., … Mandiri, N. (2018). KOORDINATOR PEMBANGUNAN MANUSIA DAN KEBUDAYAAN MENGGUNAKAN SUPPORT VECTOR MACHINE DAN NAIVE BAYES.
Studi, P., Santoso, I., Komputer, I., Mining, D., & Bayes, N. (n.d.). Program Studi Ilmu Komputer (S2) STMIK Nusa Mandiri i.
Vanich Sajee. (2015). Data Mining: Data Mining (เหมืองข้อมูล) (Vol. 2). Retrieved from http://sajeegm301.blogspot.com/2015/11/data-mining.html
Vinodhini, G. R. C. (2012). Sentiment Analysis and Opinion Mining : A Survey. International Journal of Advanced Research in Computer Science and Software Engineering, 2(6), 282–292.
Vulandari Tri Retno. (2017). Data Mining Teori dan Aplikasi Rapidminer. Retrieved from www.infogavamedia@yahoo.com
Wahono, R. S. (2015). Proses Data Mining. In Data Mining. Retrieved from http://romisatriawahono.net/dm
Wahyudi, M., & Kristiyanti, D. A. (2016). Sentiment analysis of smartphone product review using support vector machine algorithm-based particle swarm optimization. Journal of Theoretical and Applied Information Technology, 91(1), 189–201.
Yanis, R. Y. (2018). Sentiment Analysis of Bpjs Kesehatan Services To Smk Eklesia and Bina Insani Jailolo Teachers. Jurnal Terapan Teknologi Informasi, 2(2), 25–34. https://doi.org/10.21460/jutei.2018.22.105
Zhang, Y., Zhang, H., Cai, J., & Yang, B. (2014). A weighted voting classifier based on differential evolution. Abstract and Applied Analysis, 2014. https://doi.org/10.1155/2014/376950
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How to Cite
Fahlapi, R., & Rianto, Y. (2020). Twitter Comment Predictions on Dues Changes BPJS Health In 2020. Sinkron : Jurnal Dan Penelitian Teknik Informatika, 5(1), 170-183. https://doi.org/10.33395/sinkron.v5i1.10588
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