Modeling Productive Land Determination Using Entropy-Mabac Method Based on Multicriteria Data in Central Java Province
Central Java Province has a diversity of land use characteristics that reflect the potential as well as challenges in regional development, so that optimization of productive land is important to support economic growth, community welfare, and environmental sustainability. For this reason, this rese...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Politeknik Negeri Batam
2025-06-01
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| Series: | Journal of Applied Informatics and Computing |
| Subjects: | |
| Online Access: | https://jurnal.polibatam.ac.id/index.php/JAIC/article/view/9538 |
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| Summary: | Central Java Province has a diversity of land use characteristics that reflect the potential as well as challenges in regional development, so that optimization of productive land is important to support economic growth, community welfare, and environmental sustainability. For this reason, this research was conducted with an objective approach using the Entropy method in determining the weight of each criterion based on actual data variations, as well as the Multi-Attributive Border Approximation Area Comparison (MABAC) method to systematically evaluate and rank the level of land productivity in 35 districts/cities. The results of the analysis show that Demak, Brebes, and Rembang districts ranked the highest in land productivity with the highest score of 0.249, while Wonogiri and Banjarnegara districts ranked the lowest with scores of -0.392 and -0.234. Validation using the Spearman Rank test resulted in a correlation coefficient of 0.82, indicating strong agreement between the method results and historical data. The findings show that the combination of Entropy and MABAC methods is effective in determining productive land, and the results are relevant as a basis for formulating sustainable land use policies, including recommendations for irrigation development, farmland protection, and strengthening spatial policies for low productivity areas. |
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| ISSN: | 2548-6861 |