NON HIERARCHICAL K-MEANS ANALYSIS TO CLUSTERING PRIORITY DISTRIBUTION OF FUEL SUBSIDIES IN INDONESIA
The growth rate of inflation in Indonesia continues to increase from day to day. The inflation rate in Indonesia reached 1.17% in September 2022 which is the highest inflation rate in the last seven years. One of the causes of high inflation is caused by the increasing demand for motor vehicle fuel....
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Universitas Pattimura
2023-09-01
|
| Series: | Barekeng |
| Subjects: | |
| Online Access: | https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/8804 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849407892690567168 |
|---|---|
| author | Ani Budi Astuti Abdi Negara Guci Viky Iqbal Azizul Alim Laila Nur Azizah Meirida Karisma Putri Wigbertus Ngabu |
| author_facet | Ani Budi Astuti Abdi Negara Guci Viky Iqbal Azizul Alim Laila Nur Azizah Meirida Karisma Putri Wigbertus Ngabu |
| author_sort | Ani Budi Astuti |
| collection | DOAJ |
| description | The growth rate of inflation in Indonesia continues to increase from day to day. The inflation rate in Indonesia reached 1.17% in September 2022 which is the highest inflation rate in the last seven years. One of the causes of high inflation is caused by the increasing demand for motor vehicle fuel. Therefore, there is a need for appropriate action from the government in determining related policies. K-Means multivariate cluster analysis is a non-hierarchical cluster method that is popularly used, one of which is used in Machine Learning algorithms, especially Unsupervised Learning. The purpose of this research is to clustering that are priority distribution of subsidies in Indonesia based on the characteristics formed. The data in this study consist of the percentage of poverty, the percentage of total transportation, the percentage of transportation use, and the percentage of area. Data were analyzed using multivariate cluster analysis with the K-Means method. Based on the research results, information was obtained that the data fulfilled a representative sample with value of KMO >50%. In addition, there are 4 optimal clusters which are the results of the calculation of the Elbow and Silhoutte methods, so 4 provincial clusters are formed with their respective characteristics. Cluster 1 is a province that is highly prioritized to receive fuel subsidies, Cluster 2 is a province that is not highly prioritized for fuel subsidies, Cluster 3 is a province that is prioritized to receive fuel subsidies, and Cluster 4 is a province that is not prioritized to receive fuel subsidies. |
| format | Article |
| id | doaj-art-d05ce05d9a954e2f8aa75564550bcdbb |
| institution | Kabale University |
| issn | 1978-7227 2615-3017 |
| language | English |
| publishDate | 2023-09-01 |
| publisher | Universitas Pattimura |
| record_format | Article |
| series | Barekeng |
| spelling | doaj-art-d05ce05d9a954e2f8aa75564550bcdbb2025-08-20T03:35:54ZengUniversitas PattimuraBarekeng1978-72272615-30172023-09-011731663167210.30598/barekengvol17iss3pp1663-16728804NON HIERARCHICAL K-MEANS ANALYSIS TO CLUSTERING PRIORITY DISTRIBUTION OF FUEL SUBSIDIES IN INDONESIAAni Budi Astuti0Abdi Negara Guci1Viky Iqbal Azizul Alim2Laila Nur Azizah3Meirida Karisma Putri4Wigbertus Ngabu5Department of Statistics, Faculty of Mathematics and Natural Sciences, Brawijaya University, IndonesiaDepartment of Mathematics, Faculty of Mathematics and Natural Sciences, Brawijaya University, IndonesiaDepartment of Statistics, Faculty of Mathematics and Natural Sciences, Brawijaya University, IndonesiaDepartment of Statistics, Faculty of Mathematics and Natural Sciences, Brawijaya University, IndonesiaDepartment of Mathematics, Faculty of Mathematics and Natural Sciences, Brawijaya University, IndonesiaDepartment of Statistics, Faculty of Mathematics and Natural Sciences, Brawijaya University, IndonesiaThe growth rate of inflation in Indonesia continues to increase from day to day. The inflation rate in Indonesia reached 1.17% in September 2022 which is the highest inflation rate in the last seven years. One of the causes of high inflation is caused by the increasing demand for motor vehicle fuel. Therefore, there is a need for appropriate action from the government in determining related policies. K-Means multivariate cluster analysis is a non-hierarchical cluster method that is popularly used, one of which is used in Machine Learning algorithms, especially Unsupervised Learning. The purpose of this research is to clustering that are priority distribution of subsidies in Indonesia based on the characteristics formed. The data in this study consist of the percentage of poverty, the percentage of total transportation, the percentage of transportation use, and the percentage of area. Data were analyzed using multivariate cluster analysis with the K-Means method. Based on the research results, information was obtained that the data fulfilled a representative sample with value of KMO >50%. In addition, there are 4 optimal clusters which are the results of the calculation of the Elbow and Silhoutte methods, so 4 provincial clusters are formed with their respective characteristics. Cluster 1 is a province that is highly prioritized to receive fuel subsidies, Cluster 2 is a province that is not highly prioritized for fuel subsidies, Cluster 3 is a province that is prioritized to receive fuel subsidies, and Cluster 4 is a province that is not prioritized to receive fuel subsidies.https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/8804cluster analysiselbowoil subsidiesshilouetteinflationk-means |
| spellingShingle | Ani Budi Astuti Abdi Negara Guci Viky Iqbal Azizul Alim Laila Nur Azizah Meirida Karisma Putri Wigbertus Ngabu NON HIERARCHICAL K-MEANS ANALYSIS TO CLUSTERING PRIORITY DISTRIBUTION OF FUEL SUBSIDIES IN INDONESIA Barekeng cluster analysis elbow oil subsidies shilouette inflation k-means |
| title | NON HIERARCHICAL K-MEANS ANALYSIS TO CLUSTERING PRIORITY DISTRIBUTION OF FUEL SUBSIDIES IN INDONESIA |
| title_full | NON HIERARCHICAL K-MEANS ANALYSIS TO CLUSTERING PRIORITY DISTRIBUTION OF FUEL SUBSIDIES IN INDONESIA |
| title_fullStr | NON HIERARCHICAL K-MEANS ANALYSIS TO CLUSTERING PRIORITY DISTRIBUTION OF FUEL SUBSIDIES IN INDONESIA |
| title_full_unstemmed | NON HIERARCHICAL K-MEANS ANALYSIS TO CLUSTERING PRIORITY DISTRIBUTION OF FUEL SUBSIDIES IN INDONESIA |
| title_short | NON HIERARCHICAL K-MEANS ANALYSIS TO CLUSTERING PRIORITY DISTRIBUTION OF FUEL SUBSIDIES IN INDONESIA |
| title_sort | non hierarchical k means analysis to clustering priority distribution of fuel subsidies in indonesia |
| topic | cluster analysis elbow oil subsidies shilouette inflation k-means |
| url | https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/8804 |
| work_keys_str_mv | AT anibudiastuti nonhierarchicalkmeansanalysistoclusteringprioritydistributionoffuelsubsidiesinindonesia AT abdinegaraguci nonhierarchicalkmeansanalysistoclusteringprioritydistributionoffuelsubsidiesinindonesia AT vikyiqbalazizulalim nonhierarchicalkmeansanalysistoclusteringprioritydistributionoffuelsubsidiesinindonesia AT lailanurazizah nonhierarchicalkmeansanalysistoclusteringprioritydistributionoffuelsubsidiesinindonesia AT meiridakarismaputri nonhierarchicalkmeansanalysistoclusteringprioritydistributionoffuelsubsidiesinindonesia AT wigbertusngabu nonhierarchicalkmeansanalysistoclusteringprioritydistributionoffuelsubsidiesinindonesia |