Machine learning for mapping and forecasting poverty in North Sumatera: a datadriven approach
Discussing poverty is crucial because it affects many facets of society, including socioeconomic disparity, crime, and the inability to obtain high-quality education. One of the provinces with the highest poverty rate in Indonesia is North Sumatra. A strategy is required to gather accurate data to e...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Penerbit Universiti Kebangsaan Malaysia
2024
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| Online Access: | http://journalarticle.ukm.my/24250/ http://journalarticle.ukm.my/24250/1/ST%2018.pdf |
| _version_ | 1848816050176524288 |
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| author | Marpaung, Faridawaty Ramadhani, Fanny Dinata, Dewan |
| author_facet | Marpaung, Faridawaty Ramadhani, Fanny Dinata, Dewan |
| author_sort | Marpaung, Faridawaty |
| building | UKM Institutional Repository |
| collection | Online Access |
| description | Discussing poverty is crucial because it affects many facets of society, including socioeconomic disparity, crime, and the inability to obtain high-quality education. One of the provinces with the highest poverty rate in Indonesia is North Sumatra. A strategy is required to gather accurate data to effectively reduce poverty. Poverty mapping and prediction were conducted in North Sumatra to get a precise spatial distribution of poverty, the operation of the poverty model, and forecasting using machine learning (ML). Poverty prediction was conducted using a random forest (RF) algorithm and poverty mapping was conducted using the K-Means algorithm. The poverty mapping showed a significant inertia value decline in the third and fourth clusters of the elbow graph. The third cluster (0.313) was superior to the fourth cluster (0.244) in the silhouette index. Thus, there were three poverty clusters - low, medium, and high - that were used in the model. The best model was created using the grid search cross-validation, while the best prediction results were created using the RF algorithm, with the following parameters: n-estimator = 50, max depth = 10, min samples split = 2, and min samples leaf = 1. The mean squared error (MSE) of the RF model’s predictions was 0.002617, or satisfactory precision. |
| first_indexed | 2025-11-15T00:59:42Z |
| format | Article |
| id | oai:generic.eprints.org:24250 |
| institution | Universiti Kebangasaan Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T00:59:42Z |
| publishDate | 2024 |
| publisher | Penerbit Universiti Kebangsaan Malaysia |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | oai:generic.eprints.org:242502024-09-25T00:49:44Z http://journalarticle.ukm.my/24250/ Machine learning for mapping and forecasting poverty in North Sumatera: a datadriven approach Marpaung, Faridawaty Ramadhani, Fanny Dinata, Dewan Discussing poverty is crucial because it affects many facets of society, including socioeconomic disparity, crime, and the inability to obtain high-quality education. One of the provinces with the highest poverty rate in Indonesia is North Sumatra. A strategy is required to gather accurate data to effectively reduce poverty. Poverty mapping and prediction were conducted in North Sumatra to get a precise spatial distribution of poverty, the operation of the poverty model, and forecasting using machine learning (ML). Poverty prediction was conducted using a random forest (RF) algorithm and poverty mapping was conducted using the K-Means algorithm. The poverty mapping showed a significant inertia value decline in the third and fourth clusters of the elbow graph. The third cluster (0.313) was superior to the fourth cluster (0.244) in the silhouette index. Thus, there were three poverty clusters - low, medium, and high - that were used in the model. The best model was created using the grid search cross-validation, while the best prediction results were created using the RF algorithm, with the following parameters: n-estimator = 50, max depth = 10, min samples split = 2, and min samples leaf = 1. The mean squared error (MSE) of the RF model’s predictions was 0.002617, or satisfactory precision. Penerbit Universiti Kebangsaan Malaysia 2024 Article PeerReviewed application/pdf en http://journalarticle.ukm.my/24250/1/ST%2018.pdf Marpaung, Faridawaty and Ramadhani, Fanny and Dinata, Dewan (2024) Machine learning for mapping and forecasting poverty in North Sumatera: a datadriven approach. Sains Malaysiana, 53 (7). pp. 1715-1728. ISSN 0126-6039 https://www.ukm.my/jsm/english_journals/vol53num7_2024/contentsVol53num7_2024.html |
| spellingShingle | Marpaung, Faridawaty Ramadhani, Fanny Dinata, Dewan Machine learning for mapping and forecasting poverty in North Sumatera: a datadriven approach |
| title | Machine learning for mapping and forecasting poverty in North Sumatera: a datadriven approach |
| title_full | Machine learning for mapping and forecasting poverty in North Sumatera: a datadriven approach |
| title_fullStr | Machine learning for mapping and forecasting poverty in North Sumatera: a datadriven approach |
| title_full_unstemmed | Machine learning for mapping and forecasting poverty in North Sumatera: a datadriven approach |
| title_short | Machine learning for mapping and forecasting poverty in North Sumatera: a datadriven approach |
| title_sort | machine learning for mapping and forecasting poverty in north sumatera: a datadriven approach |
| url | http://journalarticle.ukm.my/24250/ http://journalarticle.ukm.my/24250/ http://journalarticle.ukm.my/24250/1/ST%2018.pdf |