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

No Cover Available

Downloads

Article Metrics
Share:

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

Peer Review Process

This article has undergone a double-blind peer review process to ensure quality and impartiality.

Indexing Information

Discover where this journal is indexed at our indexing page to understand its reach and credibility.

Open Science Badges

This journal supports transparency in research and encourages authors to meet criteria for Open Science Badges by sharing data, materials, or preregistered studies.

Similar Articles

You may also start an advanced similarity search for this article.

Most read articles by the same author(s)