Framework for Analyzing Netizen Opinions on BPJS Using Sentiment Analysis and Social Network Analysis (SNA)
Abstract
The Social Security Administrative Body is a legal entity established to administer social security programs. News about BPJS policies is often found online and social media that has received responses from netizens as a form of public opinion on the policy. One of them is the opinion of netizens on social media Twitter. Ideas can be positive, neutral, or negative. These opinions are processed using the Support Vector Machine (SVM) method, in some SVM studies still getting unsatisfactory results, with rates below 60%. For this reason, it is necessary to have feature selection or a combination with the other methods to obtain higher accuracy. To see the actors who influence the opinion of netizens on the topic of BPJS, the Social Network Analysis (SNA) method is used. Based on the SVM Method's test results, the best accuracy results are obtained in combining the SVM Method with Adaboost, with an accuracy rate of 92%. Compared to the pure SVM method by 91%, the Combination of SVM Particle Swarm Optimization (PSO) by 87% and SVM using Feature Selection Genetic Algorithm (GA) by 86%.
Downloads
References
H. Sutrisno, “Pengaruh Bpjs Ketenagakerjaan Dalam Meningkatkan Kesejahteraan Tenaga Kerja,” PREPOTIF J. Kesehat. Masy., vol. 4, no. 1, pp. 78–84, 2020, doi: 10.31004/prepotif.v4i1.670.
R. R. Farza, A. M. Karsona, and B. Rubiati, “Berdasarkan Pancasila dan Undang-Undang Dasar Negara Kesatuan Republik Indonesia Tahun 1945 Pasal 27 ayat ( 2 ) dan Pasal 28 menyatakan bahwa , pekerjaan merupakan hak azasi Ketenagakerjaan . Secara yuridis dalam hukum ketenagakerjaan kedudukan pengusaha,” J. Bina Mulia Huk., vol. 4, no. 1, pp. 150–166, 2019, doi: 10.23920/jbmh.v4n1.9.
M. K. Anam, “Analisis Respons Netizen Terhadap Berita Politik Di Media Online,” J. Ilm. Ilmu Komput., vol. 3, no. 1, pp. 14–21, 2017, doi: 10.35329/jiik.v3i1.62.
M. Naranjo-Zolotov, T. Oliveira, S. Casteleyn, and Z. Irani, "Continuous usage of e-participation: The role of the sense of virtual community," Gov. Inf. Q., vol. 36, no. 3, pp. 536–545, 2019, doi: 10.1016/j.giq.2019.05.009.
B. R. Pflughoeft and I. E. Schneider, "Social media as E-participation: Can a multiple hierarchy stratification perspective predict public interest? " Gov. Inf. Q., vol. 37, no. 1, 2020, doi: 10.1016/j.giq.2019.101422.
E. D. Wardhani, S. K. Areka, A. W. Nugroho, A. R. Zakaria, A. D. Prakasa, and R. Nooraeni, "Sentiment Analysis Using Twitter Data Regarding BPJS Cost Increase and Its Effect on Health Sector Stock Prices," Indones. J. Artif. Intell. Data Min., vol. 3, no. 1, p. 1, 2020, doi: 10.24014/ijaidm.v3i1.8245.
R. Fahlapi and Y. Rianto, “Twitter Comment Predictions on Dues Changes BPJS Health In 2020,” SinkrOn, vol. 5, no. 1, p. 170, 2020, doi: 10.33395/sinkron.v5i1.10588.
M. A. Laagu and A. Setyo Arifin, "Analysis the Issue of Increasing National Health Insurance (BPJS Kesehatan) Rates through Community Perspectives on Social Media: A Case Study of Drone Emprit," Proceeding - ICoSTA 2020 2020 Int. Conf. Smart Technol. Appl. Empower. Ind. IoT by Implement. Green Technol. Sustain. Dev., 2020, doi: 10.1109/ICoSTA48221.2020.1570615599.
R. Y. Yanis and A. Iriani, “Sentiment Analysis of Bpjs Kesehatan Services To Smk Eklesia and Bina Insani Jailolo Teachers,” J. Terap. Teknol. Inf., vol. 2, no. 2, pp. 25–34, 2018, doi: 10.21460/jutei.2018.22.105.
I. Rasyada, Y. Setiowati, A. Barakbah, and M. T. Fiddin Al Islami, "Sentiment Analysis of BPJS Kesehatan's Services Based on Affective Models," IES 2020 - Int. Electron. Symp. Role Auton. Intell. Syst. Hum. Life Comf., no. January 2019, pp. 549–556, 2020, doi: 10.1109/IES50839.2020.9231940.
A. N. Ulfah and M. K. Anam, “Analisis Sentimen Hate Speech Pada Portal Berita Online Menggunakan Support Vector Machine (SVM),” JATISI (Jurnal Tek. Inform. dan Sist. Informasi), vol. 7, no. 1, pp. 1–10, 2020, doi: 10.35957/jatisi.v7i1.196.
M. Al-Smadi, O. Qawasmeh, M. Al-Ayyoub, Y. Jararweh, and B. Gupta, "Deep Recurrent neural network vs support vector machine for aspect-based sentiment analysis of Arabic hotels' reviews," J. Comput. Sci., vol. 27, pp. 386–393, 2018, doi: 10.1016/j.jocs.2017.11.006.
E. Indrayuni, “Komparasi Algoritma Naive Bayes Dan Support Vector Machine Untuk Analisa Sentimen Review Film,” J. Pilar Nusa Mandiri, vol. 14, no. 2, p. 175, 2018, doi: 10.33480/pilar.v14i2.918.
S. Fransiska and A. Irham Gufroni, "Sentiment Analysis Provider by. U on Google Play Store Reviews with TF-IDF and Support Vector Machine (SVM) Method," Sci. J. Informatics, vol. 7, no. 2, pp. 2407–7658, 2020, doi: 10.15294/sji.v7i2.25596.
A. A. Lutfi, A. E. Permanasari, and S. Fauziati, "Sentiment Analysis in the Sales Review of Indonesian Marketplace by Utilizing Support Vector Machine," J. Inf. Syst. Eng. Bus. Intell., vol. 4, no. 1, pp. 58–64, 2018, doi: doi: http://dx.doi.org/10.20473/jisebi.4.1.57-64.
S. R. Hakim, M. A. Rizki, N. I. Zekha F, N. Fitri, Y. R. A, and R. Nooraeni, “Analisis Sentimen Pengguna Instagram Terhadap Kebijakan Kemdikbud Mengenai Bantuan Kuota Internet Dengan Metode Support Vector Machine (Svm),” J. MSA ( Mat. dan Stat. serta Apl. ), vol. 8, no. 2, p. 15, 2020, doi: 10.24252/msa.v8i2.16795.
V. I. Santoso, G. Virginia, and Y. Lukito, “Penerapan Sentiment Analysis Pada Hasil Evaluasi Dosen Dengan Metode Support Vector Machine,” J. Transform., vol. 14, no. 2, p. 72, 2017, doi: 10.26623/transformatika.v14i2.439.
P. H. Prastyo, I. Ardiyanto, and R. Hidayat, "A Combination of Query Expansion Ranking and GA-SVM for Improving Indonesian Sentiment Classification Performance," in Procedia CIRP, 2021, vol. 189, pp. 108–115, doi: 10.1016/j.procs.2021.05.074.
R. Maulana, P. A. Rahayuningsih, W. Irmayani, D. Saputra, and W. E. Jayanti, "Improved Accuracy of Sentiment Analysis Movie Review Using Support Vector Machine Based Information Gain," in Journal of Physics: Conference Series, 2020, vol. 1641, no. 1, doi: 10.1088/1742-6596/1641/1/012060.
Z. I. Alfianti, D. Gunawan, and A. F. Amin, "Sentiment Analysis of Cosmetic Review Using Naive Bayes and Support Vector Machine Method Based on Particle Swarm Optimization," J. Ris. Inform., vol. 2, no. 3, pp. 169–178, 2020, doi: 10.34288/jri.v2i3.149.
W. Chang, Y. Liu, X. Wu, Y. Xiao, S. Zhou, and W. Cao, "A New Hybrid XGBSVM Model: Application for Hypertensive Heart Disease," IEEE Access, vol. 7, pp. 175248–175258, 2019, doi: 10.1109/ACCESS.2019.2957367.
A. Andreyestha and A. Subekti, “Analisa Sentiment Pada Ulasan Film Dengan Optimasi Ensemble Learning,” J. Inform., vol. 7, no. 1, pp. 15–23, 2020, doi: 10.31311/ji.v7i1.6171.
Y. Al Amrani, M. Lazaar, and K. E. El Kadirp, "Random forest and support vector machine-based hybrid approach to sentiment analysis," in Procedia Computer Science, 2018, vol. 127, pp. 511–520, doi: 10.1016/j.procs.2018.01.150.
P. Kalaivani, "Machine Learning Approach to Analyze Ensemble Models and Neural Network Model for E-Commerce Application," Indian J. Sci. Technol., vol. 13, no. 28, pp. 2849–2857, 2020, doi: 10.17485/ijst/v13i28.927.
M. K. Anam, T. P. Lestari, Latifah, M. B. Firdaus, and S. Fadli, “Analisis Kesiapan Masyarakat Pada Penerapan Smart City di Sosial Media Menggunakan SNA,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 5, no. 1, pp. 69–81, 2021, doi: https://doi.org/10.29207/resti.v5i1.2742.
I. Febrianti, M. K. Anam, Rahmiati, and Tashid, “Tren Milenial Memilih Jurusan Di Perguruan Tinggi Menggunakan Metode Social Network Analysis,” Techo.COM, vol. 19, no. 3, pp. 216–226, 2020, doi: https://doi.org/10.33633/tc.v19i3.3483.
A. Kartino, M. Khairul Anam, Rahmaddeni, and Junadhi, “Analisis Akun Twitter Berpengaruh terkait Covid-19 menggunakan Social Network Analysis,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 5, no. 4, pp. 697–704, 2021, doi: 10.29207/resti.v5i4.3160.
I. Jayusman and O. A. K. Shavab, “Aktivitas Belajar Mahasiswa Dengan Menggunakan Media Pembelajaran Learning Management System (Lms) Berbasis Edmodo Dalam Pembelajaran Sejarah,” J. Artefak, vol. 7, no. 1, p. 13, 2020, doi: 10.25157/ja.v7i1.3180.
R. Rasenda, H. Lubis, and R. Ridwan, “Implementasi K-NN Dalam Analisa Sentimen Riba Pada Bunga Bank Berdasarkan Data Twitter,” J. Media Inform. Budidarma, vol. 4, no. 2, p. 369, 2020, doi: 10.30865/mib.v4i2.2051.
E. S. Romaito, M. K. Anam, Rahmaddeni, and A. N. Ulfah, “Perbandingan Algoritma SVM Dan NBC Dalam Analisa Sentimen Pilkada Pada Twitter,” CSRID J., vol. 13, no. 3, pp. 169–179, 2021, doi: 10.22303/csrid.13.3.2021.169-179.
I. Syarif, A. Prugel-bennett, and G. Wills, "SVM Parameter Optimization Using Grid Search and Genetic Algorithm to Improve Classification Performance," TELKOMNIKA, vol. 14, no. 4, pp. 1502–1509, 2016, doi: 10.12928/TELKOMNIKA.v14i4.3956.
O. Somantri and M. Khambali, “Feature Selection Klasifikasi Kategori Cerita Pendek Menggunakan Naïve Bayes dan Algoritme Genetika,” JNTETI, vol. 6, no. 3, pp. 301–306, 2017, doi: 10.22146/jnteti.v6i3.332.
S. D. Anggita and Ikmah, “Jurnal restiKomparasi Algoritma Klasifikasi Berbasis Particle Swarm Optimization Pada Analisis Sentimen Ekspedisi Barang,” J. RESTI, vol. 4, no. 2, pp. 362 – 369, 2020, doi: 10.29207/resti.v4i2.1840.
Y. Zhou, N. Wang, and W. Xiang, "Clustering Hierarchy Protocol in Wireless Sensor Networks Using an Improved PSO Algorithm," IEEE Access, vol. 5, pp. 2241–2253, 2017, doi: 10.1109/ACCESS.2016.2633826.
Y. Pristyanto, “PENERAPAN METODE ENSEMBLE UNTUK MENINGKATKAN KINERJA ALGORITME KLASIFIKASI PADA IMBALANCED DATASET,” J. TEKNOINFO, vol. 13, no. 1, pp. 11–16, 2019, doi: 10.33365/jti.v13i1.184.
E. Listiana and M. A. Muslim, “PENERAPAN ADABOOST UNTUK KLASIFIKASI SUPPORT VECTOR MACHINE GUNA MENINGKATKAN AKURASI PADA DIAGNOSA CHRONIC KIDNEY DISEASE,” in Prosiding SNATIF Ke -4 Tahun 2017, 2017, pp. 875–881.
C. Dedhia and J. Ramteke, "Ensemble model for Twitter sentiment analysis," in Proceedings of the International Conference on Inventive Systems and Control, ICISC 2017, 2017, pp. 1–5, doi: 10.1109/ICISC.2017.8068711.
N. Fitriyah, B. Warsito, and D. A. I. Maruddani, “Analisis Sentimen Gojek Pada Media Sosial Twitter Dengan Klasifikasi Support Vector Machine (Svm,” J. Gaussian, vol. 9, no. 3, pp. 376–390, 2020, doi: 10.14710/j.gauss.v9i3.28932.
A. Rahman Isnain, A. Indra Sakti, D. Alita, and N. Satya Marga, “Sentimen Analisis Publik Terhadap Kebijakan Lockdown Pemerintah Jakarta Menggunakan Algoritma Svm,” Jdmsi, vol. 2, no. 1, pp. 31–37, 2021, doi: 10.33365/jdmsi.v2i1.1021.
P. Arsi and R. Waluyo, “Analisis Sentimen Wacana Pemindahan Ibu Kota Indonesia Menggunakan Algoritma Support Vector Machine (SVM),” J. Teknol. Inf. dan Ilmu Komput., vol. 8, no. 1, p. 147, 2021, doi: 10.25126/jtiik.0813944.
V. K. S. Que, A. Iriani, and H. D. Purnomo, “Analisis Sentimen Transportasi Online Menggunakan Support Vector Machine Berbasis Particle Swarm Optimization,” J. Nas. Tek. Elektro dan Teknol. Inf., vol. 9, no. 2, pp. 162–170, 2020, doi: 10.22146/jnteti.v9i2.102.
H. Wang, X. Lang, and W. Mao, "Voyage optimization combining genetic algorithm and dynamic programming for fuel/emissions reduction," Transp. Res. Part D Transp. Environ., vol. 90, no. December 2020, p. 102670, 2021, doi: 10.1016/j.trd.2020.102670.
N. Azhar, P. P. Adikara, and S. Adinugroho, “Sentiment Analysis for Coffee Shop Reviews Using Naïve Bayes,” J. Teknol. Inf. dan Ilmu Komput., vol. 8, no. 3, pp. 609–618, 2021, doi: 10.25126/jtiik.202184436.
R. Aryanti, A. Saryoko, A. Junaidi, S. Marlina, Wahyudin, and L. Nurmalia, "Comparing Classification Algorithm with Genetic Algorithm in Public Transport Analysis," in Journal of Physics: Conference Series, 2020, vol. 1641, no. 1, doi: 10.1088/1742-6596/1641/1/012017.
R. T. Prasetio, "Genetic Algorithm to Optimize k-Nearest Neighbor Parameter for Benchmarked Medical Datasets Classification," J. Online Inform., vol. 5, no. 2, p. 153, 2020, doi: 10.15575/join.v5i2.656.
Z. Soumaya, B. Drissi Taoufiq, N. Benayad, K. Yunus, and A. Abdelkrim, "The detection of Parkinson disease using the genetic algorithm and SVM classifier," Appl. Acoust., vol. 171, p. 107528, 2021, doi: 10.1016/j.apacoust.2020.107528.
D. Anggraeni, W. S. M. Sanjaya, M. Y. S. Nurasyidiek, and M. Munawwaroh, "The Implementation of Speech Recognition using Mel-Frequency Cepstrum Coefficients ( MFCC ) and Support Vector Machine ( SVM ) method based on Python to Control Robot Arm The Implementation of Speech Recognition using Mel- Frequency Cepstrum Coefficients," in The 2nd Annual Applied Science and Engineering Conference (AASEC 2017), 2018, pp. 1–10, doi: 10.1088/1757-899X/288/1/012042.
I. M. B. S. Darma, R. S. Perdana, and Indriati, “Penerapan Sentimen Analisis Acara Televisi Pada Twitter Menggunakan Support Vector Machine dan Algoritma Genetika sebagai Metode Seleksi Fitur,” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 2, no. 3, pp. 998–1007, 2018, [Online]. Available: http://j-ptiik.ub.ac.id.
E. Indrayuni and A. Nurhadi, "Optimizing Genetic Algorithms for Sentiment Analysis of Apple Product Reviews Using SVM," SinkrOn, vol. 4, no. 2, p. 172, 2020, doi: 10.33395/sinkron.v4i2.10549.
R. Wati, S. Ernawati, and I. Maryani, “Optimasi Parameter Pso Berbasis Svm Untuk Analisis Sentimen Review Jasa Maskapai Penerbangan,” Evolusi J. Sains dan Manaj., vol. 8, no. 2, pp. 64–71, 2020, doi: 10.31294/evolusi.v8i2.9248.
A. H. Yunial, “Analisis Optimasi Algoritma Klasifikasi Support Vector Machine , Decision Trees , dan Neural Network Menggunakan Adaboost dan Bagging,” J. Inform. Univ. Pamulang, vol. 5, no. 3, pp. 247–260, 2020, doi: 10.32493/informatika.v5i3.6609.
J. Li, L. Sun, and R. Li, "Nondestructive detection of frying times for soybean oil by NIR-spectroscopy technology with Adaboost-SVM (RBF)," Optik (Stuttg)., vol. 206, no. January, p. 164248, 2020, doi: 10.1016/j.ijleo.2020.164248.
N. L. P. Merawati, A. Z. Amrullah, and Ismarmiaty, “Analisis Sentimen dan Pemodelan Topik Pariwisata Lombok Menggunakan Algoritma Naive Bayes dan Latent Dirichlet Allocation,” RESTI, vol. 5, no. 1, pp. 123–131, 2021, doi: 10.29207/resti.v5i1.2587.
Copyright (c) 2022 M Khairul Anam, Muhammad Ihza Mahendra, Wirta Agustin, Rahmaddeni Rahmaddeni, Nurjayadi Nurjayadi
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish with this journal agree to the following terms:
1. Copyright on any article is retained by the author(s).
2. The author grants the journal, right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work’s authorship and initial publication in this journal.
3. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal’s published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
4. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
5. The article and any associated published material is distributed under the Creative Commons Attribution-ShareAlike 4.0 International License
