REGIONS GROUPING IN CENTRAL SULAWESI PROVINCE BY TRANSMITTED DISEASE USING FUZZY GUSTAFSON KESSEL
Health is one of the main indicators in determining the human development index. This is in contradiction with the situation in several areas in Indonesia where infectious diseases are the cause of death and have become extraordinary events. It was recorded in Central Sulawesi that in 2020 there wer...
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Universitas Pattimura
2023-04-01
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| Series: | Barekeng |
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| Online Access: | https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/6985 |
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| author | Mohammad Fajri Rais Rais Lilies Handayani |
| author_facet | Mohammad Fajri Rais Rais Lilies Handayani |
| author_sort | Mohammad Fajri |
| collection | DOAJ |
| description | Health is one of the main indicators in determining the human development index. This is in contradiction with the situation in several areas in Indonesia where infectious diseases are the cause of death and have become extraordinary events. It was recorded in Central Sulawesi that in 2020 there were 8 extraordinary events due to infectious diseases which made this province become relatively high infectious diseases. One of the efforts that can be made to identify infectious diseases in an area is to form a grouping of locations into a group that has similarities and same characteristics. This is intended to provide information related to health in each region. Cluster analysis is one of method that can be used to grouping the data. Cluster analysis is the process of dividing data into a group based on the degree of similarity. Data with similar characteristics will be gathered in one group. One of the algorithms in cluster analysis is Fuzzy Gustafson Kessel which can produce relatively better groupings compared to the basic algorithms in cluster analysis. This study will use data on infectious diseases in Central Sulawesi Province with several recorded infectious diseases. From 13 regions, 5 clusters were formed. Clusters 1, 2 and 3 each consist of 3 regions, while clusters 4 and 5 each consist of 2 regions. |
| format | Article |
| id | doaj-art-fe540fb333fe4a7abea6b47ceea54dfb |
| institution | Kabale University |
| issn | 1978-7227 2615-3017 |
| language | English |
| publishDate | 2023-04-01 |
| publisher | Universitas Pattimura |
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| spelling | doaj-art-fe540fb333fe4a7abea6b47ceea54dfb2025-08-20T03:37:33ZengUniversitas PattimuraBarekeng1978-72272615-30172023-04-011710275028410.30598/barekengvol17iss1pp0275-02846985REGIONS GROUPING IN CENTRAL SULAWESI PROVINCE BY TRANSMITTED DISEASE USING FUZZY GUSTAFSON KESSELMohammad Fajri0Rais Rais1Lilies Handayani2Department of Statistics, Faculty of Mathematics and Natural Sciences, Tadulako University, IndonesiaDepartment of Statistics, Faculty of Mathematics and Natural Sciences, Tadulako University, IndonesiaDepartment of Statistics, Faculty of Mathematics and Natural Sciences, Tadulako University, IndonesiaHealth is one of the main indicators in determining the human development index. This is in contradiction with the situation in several areas in Indonesia where infectious diseases are the cause of death and have become extraordinary events. It was recorded in Central Sulawesi that in 2020 there were 8 extraordinary events due to infectious diseases which made this province become relatively high infectious diseases. One of the efforts that can be made to identify infectious diseases in an area is to form a grouping of locations into a group that has similarities and same characteristics. This is intended to provide information related to health in each region. Cluster analysis is one of method that can be used to grouping the data. Cluster analysis is the process of dividing data into a group based on the degree of similarity. Data with similar characteristics will be gathered in one group. One of the algorithms in cluster analysis is Fuzzy Gustafson Kessel which can produce relatively better groupings compared to the basic algorithms in cluster analysis. This study will use data on infectious diseases in Central Sulawesi Province with several recorded infectious diseases. From 13 regions, 5 clusters were formed. Clusters 1, 2 and 3 each consist of 3 regions, while clusters 4 and 5 each consist of 2 regions.https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/6985infectious diseasescluster analysisfuzzy gustafson kessel |
| spellingShingle | Mohammad Fajri Rais Rais Lilies Handayani REGIONS GROUPING IN CENTRAL SULAWESI PROVINCE BY TRANSMITTED DISEASE USING FUZZY GUSTAFSON KESSEL Barekeng infectious diseases cluster analysis fuzzy gustafson kessel |
| title | REGIONS GROUPING IN CENTRAL SULAWESI PROVINCE BY TRANSMITTED DISEASE USING FUZZY GUSTAFSON KESSEL |
| title_full | REGIONS GROUPING IN CENTRAL SULAWESI PROVINCE BY TRANSMITTED DISEASE USING FUZZY GUSTAFSON KESSEL |
| title_fullStr | REGIONS GROUPING IN CENTRAL SULAWESI PROVINCE BY TRANSMITTED DISEASE USING FUZZY GUSTAFSON KESSEL |
| title_full_unstemmed | REGIONS GROUPING IN CENTRAL SULAWESI PROVINCE BY TRANSMITTED DISEASE USING FUZZY GUSTAFSON KESSEL |
| title_short | REGIONS GROUPING IN CENTRAL SULAWESI PROVINCE BY TRANSMITTED DISEASE USING FUZZY GUSTAFSON KESSEL |
| title_sort | regions grouping in central sulawesi province by transmitted disease using fuzzy gustafson kessel |
| topic | infectious diseases cluster analysis fuzzy gustafson kessel |
| url | https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/6985 |
| work_keys_str_mv | AT mohammadfajri regionsgroupingincentralsulawesiprovincebytransmitteddiseaseusingfuzzygustafsonkessel AT raisrais regionsgroupingincentralsulawesiprovincebytransmitteddiseaseusingfuzzygustafsonkessel AT lilieshandayani regionsgroupingincentralsulawesiprovincebytransmitteddiseaseusingfuzzygustafsonkessel |