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All Journal Enrichment: Journal of Multidisciplinary Research and Development
Amellia Fahezha Cahyaningrum
Universitas Jenderal Achmad Yani, Cimahi, Jawa Barat, Indonesia
Humanities Economics, Econometrics & Finance Engineering Health Professions Social Sciences

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Classification of Sentiment Towards BPJS Services Using the C50 Algorithm Amellia Fahezha Cahyaningrum; Yulison Herry Chrisnanto; Ade Kania Ningsih
Enrichment: Journal of Multidisciplinary Research and Development Vol. 1 No. 8 (2023): Enrichment: Journal of Multidisciplinary Research and Development
Publisher : International Journal Labs

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55324/enrichment.v1i8.71

Abstract

Social media provides a timely source of public feedback on services delivered by the Social Security Administering Body for Health (BPJS Health), a State-Owned Enterprise responsible for Indonesia's public health insurance program. This study aimed to evaluate the ability of the C5.0 algorithm to classify positive and negative sentiment toward BPJS services in Twitter data. Applied quantitative research with an experimental text-classification design was conducted using a secondary dataset obtained from Kaggle. The implemented database displayed 3,060 documents. Data were processed through cleaning, case folding, tokenization, filtering, stemming, and TF-IDF weighting, followed by C5.0 classification and confusion-matrix evaluation using an 80:20 split. The reported test matrix comprised 621 cases: 6 true positives, 579 true negatives, 4 false positives, and 32 false negatives. These values produced 94.2% accuracy, 60.0% precision, and 15.8% recall. Although the aggregate accuracy was high, the low recall shows that the model detected only a small proportion of the positive class and was strongly influenced by the majority class. Therefore, the current model demonstrates the technical feasibility of applying C5.0 to BPJS-related tweets but cannot yet be considered balanced or fully reliable for service evaluation. Future optimization should address class imbalance, verify dataset labeling, and report complementary metrics before the results are used to support BPJS service-improvement decisions.
Co-Authors Ade Kania Ningsih Yulison Herry Chrisnanto