K-Nearest Neighbor and Naive Bayes Classifier Algorithm in Determining The Classification of Healthy Card Indonesia Giving to The Poor
Health is a human right and one of the elements of welfare that must be realized in the form of giving various health efforts to all the people of Indonesia. Poverty in Indonesia has become a national problem and even the government seeks efforts to alleviate poverty. For example, poor families have...
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| Format: | Article |
| Language: | English |
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Universitas Negeri Semarang
2018-05-01
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| Series: | Scientific Journal of Informatics |
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| Online Access: | https://journal.unnes.ac.id/nju/index.php/sji/article/view/12057 |
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| author | Yofi Firdan Safri Riza Arifudin Much Aziz Muslim |
| author_facet | Yofi Firdan Safri Riza Arifudin Much Aziz Muslim |
| author_sort | Yofi Firdan Safri |
| collection | DOAJ |
| description | Health is a human right and one of the elements of welfare that must be realized in the form of giving various health efforts to all the people of Indonesia. Poverty in Indonesia has become a national problem and even the government seeks efforts to alleviate poverty. For example, poor families have relatively low levels of livelihood and health. One of the new policies of the Sakti Government Card Program issued by the government includes three cards, namely Indonesia Smart Card (KIP), Healthy Indonesia Card (KIS) and Prosperous Family Card (KKS). In this study to determine the feasibility of a healthy Indonesian card (KIS) required a method of optimal accuracy. The data used in this study is KIS data which amounts to 200 data records with 15 determinants of feasibility in 2017 taken at the Social Service of Pekalongan Regency. The data were processed using the K-Nearest Neighbor algorithm and the combination of K-Nearest Neighbor-Naive Bayes Classifier algorithm. This can be seen from the accuracy of determining the feasibility of K-Nearest Neighbor algorithm of 64%, while the combination of K-Nearest Neighbor-Naive Bayes Classifier algorithm is 96%, so the combination of K-Nearest Neighbor-Naive Bayes Classifier algorithm is the optimal algorithm in determining the feasibility of healthy Indonesian card recipients with an increase of 32% accuracy. This study shows that the accuracy of the results of determining feasibility using a combination of K-Nearest Neighbor-Naive Bayes Classifier algorithms is better than the K-Nearest Neighbor algorithm. |
| format | Article |
| id | doaj-art-7ef9927a226a4ddfad0aaa3f7c461b59 |
| institution | DOAJ |
| issn | 2407-7658 |
| language | English |
| publishDate | 2018-05-01 |
| publisher | Universitas Negeri Semarang |
| record_format | Article |
| series | Scientific Journal of Informatics |
| spelling | doaj-art-7ef9927a226a4ddfad0aaa3f7c461b592025-08-20T03:14:16ZengUniversitas Negeri SemarangScientific Journal of Informatics2407-76582018-05-015110.15294/sji.v5i1.120577436K-Nearest Neighbor and Naive Bayes Classifier Algorithm in Determining The Classification of Healthy Card Indonesia Giving to The PoorYofi Firdan SafriRiza ArifudinMuch Aziz MuslimHealth is a human right and one of the elements of welfare that must be realized in the form of giving various health efforts to all the people of Indonesia. Poverty in Indonesia has become a national problem and even the government seeks efforts to alleviate poverty. For example, poor families have relatively low levels of livelihood and health. One of the new policies of the Sakti Government Card Program issued by the government includes three cards, namely Indonesia Smart Card (KIP), Healthy Indonesia Card (KIS) and Prosperous Family Card (KKS). In this study to determine the feasibility of a healthy Indonesian card (KIS) required a method of optimal accuracy. The data used in this study is KIS data which amounts to 200 data records with 15 determinants of feasibility in 2017 taken at the Social Service of Pekalongan Regency. The data were processed using the K-Nearest Neighbor algorithm and the combination of K-Nearest Neighbor-Naive Bayes Classifier algorithm. This can be seen from the accuracy of determining the feasibility of K-Nearest Neighbor algorithm of 64%, while the combination of K-Nearest Neighbor-Naive Bayes Classifier algorithm is 96%, so the combination of K-Nearest Neighbor-Naive Bayes Classifier algorithm is the optimal algorithm in determining the feasibility of healthy Indonesian card recipients with an increase of 32% accuracy. This study shows that the accuracy of the results of determining feasibility using a combination of K-Nearest Neighbor-Naive Bayes Classifier algorithms is better than the K-Nearest Neighbor algorithm.https://journal.unnes.ac.id/nju/index.php/sji/article/view/12057determination of feasibility, poverty, k-nearest neighbor, combination of k-nearest neighbor-naive bayes classifier algorithm |
| spellingShingle | Yofi Firdan Safri Riza Arifudin Much Aziz Muslim K-Nearest Neighbor and Naive Bayes Classifier Algorithm in Determining The Classification of Healthy Card Indonesia Giving to The Poor Scientific Journal of Informatics determination of feasibility, poverty, k-nearest neighbor, combination of k-nearest neighbor-naive bayes classifier algorithm |
| title | K-Nearest Neighbor and Naive Bayes Classifier Algorithm in Determining The Classification of Healthy Card Indonesia Giving to The Poor |
| title_full | K-Nearest Neighbor and Naive Bayes Classifier Algorithm in Determining The Classification of Healthy Card Indonesia Giving to The Poor |
| title_fullStr | K-Nearest Neighbor and Naive Bayes Classifier Algorithm in Determining The Classification of Healthy Card Indonesia Giving to The Poor |
| title_full_unstemmed | K-Nearest Neighbor and Naive Bayes Classifier Algorithm in Determining The Classification of Healthy Card Indonesia Giving to The Poor |
| title_short | K-Nearest Neighbor and Naive Bayes Classifier Algorithm in Determining The Classification of Healthy Card Indonesia Giving to The Poor |
| title_sort | k nearest neighbor and naive bayes classifier algorithm in determining the classification of healthy card indonesia giving to the poor |
| topic | determination of feasibility, poverty, k-nearest neighbor, combination of k-nearest neighbor-naive bayes classifier algorithm |
| url | https://journal.unnes.ac.id/nju/index.php/sji/article/view/12057 |
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