Early Detection for Determinant Factor of National Health Insurance Membership for Workers in Central Java Using C4.5 Algorithm
Central Java Province has a membership coverage rate below national coverage. Several data sources up to October 2023 show that NHI participation coverage in Central Java is 93.13%, with 72.72% active participants. The highest type of participation is Contribution Assistance Recipients. A survey of...
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| Format: | Article |
| Language: | English |
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EDP Sciences
2024-01-01
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| Series: | BIO Web of Conferences |
| Online Access: | https://www.bio-conferences.org/articles/bioconf/pdf/2024/52/bioconf_icophtcd2024_00025.pdf |
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| author | Aprianti Aprianti Sifai Izzatul Alifah Nurjanah Nurjanah Ratna Wulan Widya Ratnawati Juli Ahsan Abdillah |
| author_facet | Aprianti Aprianti Sifai Izzatul Alifah Nurjanah Nurjanah Ratna Wulan Widya Ratnawati Juli Ahsan Abdillah |
| author_sort | Aprianti Aprianti |
| collection | DOAJ |
| description | Central Java Province has a membership coverage rate below national coverage. Several data sources up to October 2023 show that NHI participation coverage in Central Java is 93.13%, with 72.72% active participants. The highest type of participation is Contribution Assistance Recipients. A survey of 451 tobacco workers in Central Java found that 55.2% did not have health insurance. This research aims to apply the C4.5 algorithm for the early detection of determinant factors of national health insurance membership for workers in Central Java. Research was done in Central Java districts: Semarang City, Semarang Regency, Jepara Regency, and Kendal Regency. These four districts were chosen because they have the largest workforce in Central Java Province. The total sample was 400, with 100 respondents taken from each city/district. The sampling technique was cluster sampling; two sub-districts were taken from each district—data analysis using the Data Mining 4.5 algorithm. The results show that employment status is the most influential factor in determining someone to become an NHI participant, where fishermen are the group of workers most predicted not to become NHI participants with a gain value of 0.3887. Measurement using RapidMiner software proves that the C4.5 algorithm has an accuracy of 88.93%. At the same time, the AUC curve has a value of 0.941, which, according to Gorunescu, is included in the Superior Classification. The program is expected to prioritize fishermen groups as members of the NHI program. |
| format | Article |
| id | doaj-art-c97bc2af9c9742148d3ec10630d2d9e5 |
| institution | OA Journals |
| issn | 2117-4458 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | BIO Web of Conferences |
| spelling | doaj-art-c97bc2af9c9742148d3ec10630d2d9e52025-08-20T02:13:02ZengEDP SciencesBIO Web of Conferences2117-44582024-01-011330002510.1051/bioconf/202413300025bioconf_icophtcd2024_00025Early Detection for Determinant Factor of National Health Insurance Membership for Workers in Central Java Using C4.5 AlgorithmAprianti Aprianti0Sifai Izzatul Alifah1Nurjanah Nurjanah2Ratna Wulan Widya3Ratnawati Juli4Ahsan Abdillah5Faculty of Health Science, Universitas Dian NuswantoroFaculty of Health Science, Universitas Dian NuswantoroFaculty of Health Science, Universitas Dian NuswantoroFaculty of Health Science, Universitas Dian NuswantoroFaculty of Economics and Business, Universitas Dian NuswantoroFaculty of Economics and Business, Universitas IndonesiaCentral Java Province has a membership coverage rate below national coverage. Several data sources up to October 2023 show that NHI participation coverage in Central Java is 93.13%, with 72.72% active participants. The highest type of participation is Contribution Assistance Recipients. A survey of 451 tobacco workers in Central Java found that 55.2% did not have health insurance. This research aims to apply the C4.5 algorithm for the early detection of determinant factors of national health insurance membership for workers in Central Java. Research was done in Central Java districts: Semarang City, Semarang Regency, Jepara Regency, and Kendal Regency. These four districts were chosen because they have the largest workforce in Central Java Province. The total sample was 400, with 100 respondents taken from each city/district. The sampling technique was cluster sampling; two sub-districts were taken from each district—data analysis using the Data Mining 4.5 algorithm. The results show that employment status is the most influential factor in determining someone to become an NHI participant, where fishermen are the group of workers most predicted not to become NHI participants with a gain value of 0.3887. Measurement using RapidMiner software proves that the C4.5 algorithm has an accuracy of 88.93%. At the same time, the AUC curve has a value of 0.941, which, according to Gorunescu, is included in the Superior Classification. The program is expected to prioritize fishermen groups as members of the NHI program.https://www.bio-conferences.org/articles/bioconf/pdf/2024/52/bioconf_icophtcd2024_00025.pdf |
| spellingShingle | Aprianti Aprianti Sifai Izzatul Alifah Nurjanah Nurjanah Ratna Wulan Widya Ratnawati Juli Ahsan Abdillah Early Detection for Determinant Factor of National Health Insurance Membership for Workers in Central Java Using C4.5 Algorithm BIO Web of Conferences |
| title | Early Detection for Determinant Factor of National Health Insurance Membership for Workers in Central Java Using C4.5 Algorithm |
| title_full | Early Detection for Determinant Factor of National Health Insurance Membership for Workers in Central Java Using C4.5 Algorithm |
| title_fullStr | Early Detection for Determinant Factor of National Health Insurance Membership for Workers in Central Java Using C4.5 Algorithm |
| title_full_unstemmed | Early Detection for Determinant Factor of National Health Insurance Membership for Workers in Central Java Using C4.5 Algorithm |
| title_short | Early Detection for Determinant Factor of National Health Insurance Membership for Workers in Central Java Using C4.5 Algorithm |
| title_sort | early detection for determinant factor of national health insurance membership for workers in central java using c4 5 algorithm |
| url | https://www.bio-conferences.org/articles/bioconf/pdf/2024/52/bioconf_icophtcd2024_00025.pdf |
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