Open access Journal SINTA 3
NON HIERARCHICAL K-MEANS ANALYSIS TO CLUSTERING PRIORITY DISTRIBUTION OF FUEL SUBSIDIES IN INDONESIA
BAREKENG: Jurnal Ilmu Matematika dan Terapan · Vol. 17 No. 3 (2023) · pp. 1663–1672
- ISSN
- 2615-3017, 1978-7227
- Published
- 2023-09-30
- Publisher
- PATTIMURA UNIVERSITY
- Language
- eng
Abstract
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.
Collaboration network of the authors
36 authors · 102 co-authorship links · 11 across different organisations
Hover or tap a name to highlight its co-authors. Double-click to open the author's page.
- University of Brawijaya
- State University of Malang
- Universitas Negeri Surabaya
- Affiliation unknown
- Different organisations
- Same organisation
- Affiliation unknown
- Thicker line = more joint articles · bigger circle = more articles
Show as table
| Co-author | Organisation | Joint articles |
|---|---|---|
| Atiek Iriany | University of Brawijaya | 4 |
| Henny Pramoedyo | University of Brawijaya | 3 |
| Suci Astutik | University of Brawijaya | 2 |
| Rahma Fitriani | University of Brawijaya | 2 |
| Adji Fernandes | Affiliation unknown | 1 |
| Solimun | Affiliation unknown | 1 |
| Dea Saraswati Pramaningrum | University of Brawijaya | 1 |
| Fachira Haneinanda Junianto | University of Brawijaya | 1 |
| Nabila Ayunda Sovia | Affiliation unknown | 1 |
| Luthfatul Amaliana | Affiliation unknown | 1 |
| Rossanda Sevia Gadis | Affiliation unknown | 1 |
| Naurah Atikah Rabbani | Affiliation unknown | 1 |
| Sri Handayani | University of Brawijaya | 1 |
| Nur Silviyah Rahmi | University of Brawijaya | 1 |
| Alifiandi Rafi Muhammad | University of Brawijaya | 1 |
| Ulfah Maisaroh | University of Brawijaya | 1 |
| Danang Arianto | University of Brawijaya | 1 |
| Arditama Putra | University of Brawijaya | 1 |
| Achmad Efendi | University of Brawijaya | 1 |
| Devi Azarina Manzilir Rohmah | University of Brawijaya | 1 |
| Syaiful Anam | University of Brawijaya | 1 |
| Fery Widhiatmoko | University of Brawijaya | 1 |
| Mila Kurniawaty | University of Brawijaya | 1 |
| Komang Agus Arta Wijaya | University of Brawijaya | 1 |
| Danang Ariyanto | Universitas Negeri Surabaya | 1 |
| Sativandi Riza | University of Brawijaya | 1 |
| Agis Wahyu Lestari | University of Brawijaya | 1 |
| Firdha Mahrifatul Zana | State University of Malang | 1 |
| Candra R. W. S Weni Utomo | University of Brawijaya | 1 |
| Arifin Arifin | State University of Malang | 1 |
Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.