Published: 2023-04-01
Analisis Performa Algoritma Klasifikasi Naive Bayes dan C4.5 untuk Prediksi Penerima Bantuan Jaminan Kesehatan
DOI: 10.35870/jtik.v7i2.756
Nurfazriah Attamami, Agung Triayudi , Rima Tamara Aldisa
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Abstract
Health is the basis of the level of humanity. However, in reality, not everyone with social welfare problems has national health insurance. The large number of patient files that must be checked makes it difficult for officers to identify potential beneficiaries. Based on these problems, a procedure or method is needed that can assist officers in identifying potential beneficiaries. From the results of the performance testing of the two models using a confusion matrix with 730 records used as training data and 313 records used as test data, the C4.5 classification algorithm gets the highest accuracy value, which is 99.04%. A total of 310 data records were predicted correctly with an error rate or error of 0.96% or as many as 3 data records from 313 data tested. While the Naive Bayes classification algorithm gets an accuracy value of 92.97%. A total of 291 data records were predicted to be correct with an error rate of 7.03% or as many as 22 data records were predicted to be incorrect from the 313 data tested
Keywords
Data Mining ; Decision Tree ; Naïve Bayes ; Assistance Program ; Health Insurance
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Article Information
This article has been peer-reviewed and published in the Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi). The content is available under the terms of the Creative Commons Attribution 4.0 International License.
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Issue: Vol. 10 No. 3 (2026)
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Section: Computer & Communication Science
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Published: %750 %e, %2023
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License: CC BY 4.0
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Copyright: © 2023 Authors
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DOI: 10.35870/jtik.v7i2.756
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Nurfazriah Attamami
Fakultas Teknologi Komunukasi dan Indormatika, Universitas Nasional, Kota Jakarta Selatan, Daerah Khusus Ibukota Jakarta, Indonesia
Agung Triayudi
Fakultas Teknologi Komunukasi dan Indormatika, Universitas Nasional, Kota Jakarta Selatan, Daerah Khusus Ibukota Jakarta, Indonesia
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