Implementasi Metode Association Rule untuk Menganalisis Data Twitter tentang Badan Penyelenggara Jaminan Sosial dengan Algoritma Frequent Pattern-Growth

*Jemaictry Tamaela  -  Universitas Kristen Satya Wacana, Indonesia
Eko Sediyono  -  Universitas Kristen Satya Wacana Salatiga, Indonesia
Adi Setiawan  -  Universitas Kristen Satya Wacana Salatiga, Indonesia
Received: 25 Jan 2018; Published: 30 Apr 2018.
Open Access
Citation Format:

BPJS services cannot be separated from criticism and complaints of the people in Indonesia. Twitter is one of the social media choose to share experiences related to things about BPJS. The information that is shared can be processed to gain new knowledge (knowledge discovery), which is related to public opinion about BPJS. Tweets collected from the national BJPS twitter are divided into words, then, specified words can be used as items to form the itemset. The association rule technique with the FP-Growth algorithm that is implemented in the application can process text data from Twitter to form the item set. Each item set contains a collection of tweets that are responses and the opinion of the community about an event or phenomenon related to BPJS services. The tree structure of FP-Growth simplifies the process of the validation because it can track and display the frequency of occurrence of each word and itemset, before and after branch pruning which is not included in the support value. The OSM API integration with the application in this study provides visual information about where the tweet comes from, so it can be used to generate itemset from a collection of tweets from a particular region.

Keywords: Association rule; Data Mining; Knowledge Discover; FP-Growth; Twitter; BPJS.

Article Metrics:

  1. Apriliyanti, 2015. Sentiment Analysis Dengan Naive Bayes untuk Melihat Persepsi Masyarakat Terhadap Batik Pada Jejaring Sosial Twitter, Pros. Semin. Nas. Mat. dan Pendidik. Mat. UMS
  2. Ashktorab, Z., Brown, C., Nandi, M., Culotta, A. 2014. Tweedr: Mining Twitter to Inform Disaster Response. International System for Crisis Response and Management
  3. Chakrabarti, S., Ester, M., Fayyad, U., Gehrke, J., Han, J., Morishita, S., Gregory Piatetsky-Shapiro, W. W. 2015. Data Mining Curriculum. curriculum/ index.html. Diakses 5 Agustus 2017
  4. Fayyad, U., Piatetsky-Shapiro, G., Smyth, P. 1996. From Data Mining to Knowledge Discovery in Databases. AI magazine 17, 37
  5. Godfrey, D., Johns, C., Meyer, C., Race, S., Sadek, C. 2014. A Case Study in Text Mining: Interpreting Twitter Data From World Cup Tweet. Cornell University Library, arXiv preprint rXiv:1408.5427
  6. Han, J., Pei, J., Yin, Y., Mao, R. 2004. Mining Frequent Patterns Without Candidate Generation: A Frequent-Pattern Tree Approach. Data Mining and Knowledge Discovery 8, 53–87. (doi: 10.1023/B:DAMI.0000005258.31418.83)
  7. Han, J., Pei, J., Kamber, M. 2012. Data Mining: Concepts and Technique 3rd Edition, Morgan Kaufmann Publisher (doi: 10.1017/CBO9781107415324.004)
  8. Herman., Mononimbar, D, A. 2017. Indonesia Fifth-Largest Country in Terms of Twitter Users. Jakarta Globe. Diakses 20 Februari 2018
  9. Kushima, M., Araki, K.., Yamazaki, T., Araki, S., Ogawa, T., Sonehara, N. 2017. Text Data Mining of Care Life Log by the Level of Care Required Using KeyGraph. In Proceedings of the International Multi Conference of Engineers and Computer Scientists
  10. Ludwig, S., De Ruyter, K.., Friedman, M., Brüggen, E.C., Wetzels, M. & Pfann, G. 2013. More Than Words: The Influence of Affective Content and Linguistic Style Matches in Online Reviews on Conversion Rates. Journal of Marketing 77, 87–103
  11. Ordenes, F.V., Theodoulidis, B., Burton, J., Gruber, T., Zaki, M. 2014. Analyzing Customer Experience Feedback Using Text Mining: A Llinguistics-based Approach. Journal of Service Research 17, 278–295
  12. Sari, F.P., Syafrizal. 2015. Persepsi Masyarakat Pengguna Badan Penyelenggara Jaminan Sosial (BPJS) Kesehatan Mandiri Dalam Pelayanan RSUD Lubuk Basung Kabupaten Agam. Fakultas Ilmu Sosial dan Ilmu Politik, Jural Online Mahasiswa 2, No.2
  13. Ulinuha, F.E. 2014. Kepuasan Pasien BPJS (Badan Penyelenggara Jaminan Sosial) Terhadap Pelayanan di Unit Rawat Jalan (URJ) Rumah Sakit Permata Medika Semarang Tahun 2014. Skripsi, Fakultas Kesehatan, Universitas Dian Nuswantoro
  14. Ur-Rahman, N., Harding, J. A. 2012. Textual Data Mining for Industrial Knowledge Management and Text Classification: A Business Oriented Approach. Expert Systems with Applications 39, 4729-4739
  15. Widada, T., Pramusinto, A., Lazuadi, L. 2017. Peran Badan Penyelenggara Jaminan Sosial (BPJS) Penyelenggara Jaminan Sosial Terhadap Ketahanan Masyarakat (Studi di RSUD Damrah Mana, Kabupaten Bengkulu Selatan, Provinsi Bengkulu). Jurnal Ketahanan Nasional 23, No.2. (doi:
  16. Zaki, M. J. 2000. Generating Non-Redundant Association Rules. Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining , 34-43
  17. Zhang, S., Zhang, C., Yang, Q. 2010. Data Preparation For Data Mining. Applied Artificial Intelligence 17, 2003

Last update: 2021-03-03 00:18:05

No citation recorded.

Last update: 2021-03-03 00:18:06

No citation recorded.