COMPARISON OF FUZZY C-MEANS AND FUZZY GUSTAFSON-KESSEL CLUSTERING METHODS IN PROVINCIAL GROUPING IN INDONESIA BASED ON CRIMINALITY-RELATED FACTORS
Indonesia is a country that has a population density that is increasing every year, with the increase in population density, the crime rate in Indonesia is increasing. Criminal acts arise because they are supported by factors that cause crime. To improve the security and welfare of the Indonesian pe...
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Universitas Pattimura
2023-06-01
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| Series: | Barekeng |
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| Online Access: | https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/8298 |
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| author | Bella Destia Mujiati Dwi Kartikasari |
| author_facet | Bella Destia Mujiati Dwi Kartikasari |
| author_sort | Bella Destia |
| collection | DOAJ |
| description | Indonesia is a country that has a population density that is increasing every year, with the increase in population density, the crime rate in Indonesia is increasing. Criminal acts arise because they are supported by factors that cause crime. To improve the security and welfare of the Indonesian people, the authors grouped each province in Indonesia based on the factors that influence crime. This study uses a comparison of the Fuzzy C-Means Clustering (FCM) and Fuzzy Gustafson-Kessel Clustering (FGK) methods by using the validation index for determining the optimal cluster, namely the Davies Bouldin Index The data used is secondary data in the form of variables forming factors that affect the crime rate in Indonesia, where the data obtained comes from the website of the Central Statistics Agency (BPS). The results obtained in this study for the FGK method are better than the FCM method because they have a smaller standard deviation ratio. The results of grouping using the best method, namely FGK, it was found that the optimal number of clusters formed was 5 clusters with the results of grouping cluster 1 consisting of 6 provinces, cluster 2 consisting of 4 provinces, cluster 3 consisting of 11 provinces, cluster 4 consisting of 5 provinces, and cluster 5 consisting of 8 provinces. |
| format | Article |
| id | doaj-art-4bad9e2e54b545f29b8c922c8feb1084 |
| institution | DOAJ |
| issn | 1978-7227 2615-3017 |
| language | English |
| publishDate | 2023-06-01 |
| publisher | Universitas Pattimura |
| record_format | Article |
| series | Barekeng |
| spelling | doaj-art-4bad9e2e54b545f29b8c922c8feb10842025-08-20T03:05:41ZengUniversitas PattimuraBarekeng1978-72272615-30172023-06-011721093110210.30598/barekengvol17iss2pp1093-11028298COMPARISON OF FUZZY C-MEANS AND FUZZY GUSTAFSON-KESSEL CLUSTERING METHODS IN PROVINCIAL GROUPING IN INDONESIA BASED ON CRIMINALITY-RELATED FACTORSBella Destia0Mujiati Dwi Kartikasari1Department of Statistics, Universitas Islam Indonesia, IndonesiaDepartment of Statistics, Universitas Islam Indonesia, IndonesiaIndonesia is a country that has a population density that is increasing every year, with the increase in population density, the crime rate in Indonesia is increasing. Criminal acts arise because they are supported by factors that cause crime. To improve the security and welfare of the Indonesian people, the authors grouped each province in Indonesia based on the factors that influence crime. This study uses a comparison of the Fuzzy C-Means Clustering (FCM) and Fuzzy Gustafson-Kessel Clustering (FGK) methods by using the validation index for determining the optimal cluster, namely the Davies Bouldin Index The data used is secondary data in the form of variables forming factors that affect the crime rate in Indonesia, where the data obtained comes from the website of the Central Statistics Agency (BPS). The results obtained in this study for the FGK method are better than the FCM method because they have a smaller standard deviation ratio. The results of grouping using the best method, namely FGK, it was found that the optimal number of clusters formed was 5 clusters with the results of grouping cluster 1 consisting of 6 provinces, cluster 2 consisting of 4 provinces, cluster 3 consisting of 11 provinces, cluster 4 consisting of 5 provinces, and cluster 5 consisting of 8 provinces.https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/8298clusteringcrimefuzzyfuzzy c-meansfuzzy gustafson-kessel |
| spellingShingle | Bella Destia Mujiati Dwi Kartikasari COMPARISON OF FUZZY C-MEANS AND FUZZY GUSTAFSON-KESSEL CLUSTERING METHODS IN PROVINCIAL GROUPING IN INDONESIA BASED ON CRIMINALITY-RELATED FACTORS Barekeng clustering crime fuzzy fuzzy c-means fuzzy gustafson-kessel |
| title | COMPARISON OF FUZZY C-MEANS AND FUZZY GUSTAFSON-KESSEL CLUSTERING METHODS IN PROVINCIAL GROUPING IN INDONESIA BASED ON CRIMINALITY-RELATED FACTORS |
| title_full | COMPARISON OF FUZZY C-MEANS AND FUZZY GUSTAFSON-KESSEL CLUSTERING METHODS IN PROVINCIAL GROUPING IN INDONESIA BASED ON CRIMINALITY-RELATED FACTORS |
| title_fullStr | COMPARISON OF FUZZY C-MEANS AND FUZZY GUSTAFSON-KESSEL CLUSTERING METHODS IN PROVINCIAL GROUPING IN INDONESIA BASED ON CRIMINALITY-RELATED FACTORS |
| title_full_unstemmed | COMPARISON OF FUZZY C-MEANS AND FUZZY GUSTAFSON-KESSEL CLUSTERING METHODS IN PROVINCIAL GROUPING IN INDONESIA BASED ON CRIMINALITY-RELATED FACTORS |
| title_short | COMPARISON OF FUZZY C-MEANS AND FUZZY GUSTAFSON-KESSEL CLUSTERING METHODS IN PROVINCIAL GROUPING IN INDONESIA BASED ON CRIMINALITY-RELATED FACTORS |
| title_sort | comparison of fuzzy c means and fuzzy gustafson kessel clustering methods in provincial grouping in indonesia based on criminality related factors |
| topic | clustering crime fuzzy fuzzy c-means fuzzy gustafson-kessel |
| url | https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/8298 |
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