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Jurnal Wasian
Published by Universitas Muhammadiyah Makassar
ISSN : 23559969     EISSN : 25025198     DOI : doi.org/10.62142
Core Subject : Science, Education, Social,
Earth & Planetary Sciences Environmental Science Physics Other
The Wasian Journal dedicates itself to advancing scientific research that significantly contributes to the conservation of natural resources and the sustainable transformation of landscapes. Our goal is to support the long-term ecological balance and resilience of forests and land. We are committed to publishing cutting-edge research that is innovative and open to rigorous scholarly debate, maintaining the highest standards of quality.
Arjuna Subject : Ilmu Pertanian dan Biologi (Semua) - Kehutanan
Articles 121 Documents
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Will Indonesia's Forests Survive Development Pressure? Machine Learning Predictions for Energy-Critical Tropical Watersheds Utami A, Widyanti; Irlan, Irlan; Syahrir, Nur Hilal A; Rosmaeni, Rosmaeni
Jurnal Wasian Vol. 12 No. 01 (2025): June
Publisher : Forestry Department, University of Muhammadiyah Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62142/hjs6a555

Abstract

Land Use and Land Cover (LULC) changes play an important role in influencing the hydrological conditions of a watershed. The conversion of land such as forests, shrubs and grasslands into agricultural land can disrupt the hydrological balance of the watershed. The availability of information related to LULC dynamics in the future is needed to assist sustainable watershed management planning. Machine learning technology, such as Cellular Automata, can provide accurate predicting. The objective of this research is to simulate LULC based on machine learning in the Mamasa Sub-watershed. Two model combinations were employed to simulate LULC: Artificial Neural Network-Cellular Automata (ANN-CA) and Logistic Regression-Cellular Automata (LR-CA). The research results found that the ANN-CA model achieved percent of correctness and overall kappa of 83.6745 and 0.75412, respectively, which were higher than those of the LR-CA model (82.3498 and 0.73361). The prediction results of both model combinations still fall below the actual LULC values, especially in the case of large LULC classes such as forests, range-shrub, rice, and pasture. Conversely, higher accuracy is observed for smaller classes such as wetlands-forested, orchard, residential, and oak. However, it should be noted that this research did not include several socio-economic variables, such as population and income level, which are considered to influence changes in LULC. Future research is expected to analyse the influence of each variable and include some socio-economic variables that may have a significant influence on LULC change.

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