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All Journal Geoplanning : Journal of Geomatics and Planning Jurnal Ketahanan Nasional Journal of Environmental Engineering and Sustainable Technology The Indonesian Green Technology Journal Jurnal Tata Kota dan Daerah Rekayasa Sipil Local Wisdom : Jurnal Ilmiah Kajian Kearifan Lokal Jurnal Penelitian Pendidikan IPA (JPPIPA) Civil and Environmental Science Journal (CIVENSE) Journal of Indonesian Tourism, Hospitality and Recreation Region : Jurnal Pembangunan Wilayah dan Perencanaan Partisipatif GEOGRAPHY : Jurnal Kajian, Penelitian dan Pengembangan Pendidikan
Agus Dwi Wicaksono
Departemen Perencanaan Wilayah Dan Kota, Fakultas Teknik, Universitas Brawijaya
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Energy Engineering Environmental Science Health Professions Materials Science & Nanotechnology Mechanical Engineering Physics Social Sciences Transportation Other

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Journal : Geoplanning : Journal of Geomatics and Planning

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Comparison of Land Cover Change Prediction Models: A Case Study in Kedungkandang District, Malang City Hariyanto, Annisa Dira; Yudono, Adipandang; Wicaksono, Agus Dwi
Geoplanning: Journal of Geomatics and Planning Vol 11, No 1 (2024)
Publisher : Department of Urban and Regional Planning, Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/geoplanning.11.1.85-98

Abstract

The infrastructure of Malang City is currently being directed towards the eastern and southeastern parts, Kedungkandang District. Infrastructure plays an important role in the aspect of land cover change, which raises the complexity of the emergence of urban forms and dynamics. This study compares three models, Artificial Neural Network (ANN), Logistic Regression (LR), and Multi-Criteria Evaluation (MCE), to predict changes in land cover in the Kedungkandang District using the Cellular Automata (CA) approach. The prediction results indicate that the ANN and MCE models have the highest overall Kappa values (prediction accuracy), while the ANN and LR models have the highest location-specific Kappa values. However, overall, the ANN model demonstrates the highest accuracy and performance among the other two models. This research makes a significant contribution to urban planning by highlighting the importance of using machine learning-based technology to predict land cover changes in Malang City, particularly in the Kedungkandang District. Stakeholders can leverage this technology to design more effective and sustainable infrastructure policies and implement preventive measures to mitigate the negative impacts of uncontrolled urban growth.
Co-Authors Abdul Wahid Hasyim Adipandang Yudono Alighan, Ahmad Ricky Aris Subagiyo Atik Sulasmi Christia Meidiana Delisa, Ahmad Delisa Dian Dinanti Dyaning Wahyu Primasari Eko Dein Kirman Endarwati, Maria Cristina Enock Siankwilimba Eti Kurniati, Eti Faiz, Syauqi Asyraf Gunawan Prayitno Gunawan Prayitno Gunawan Prayitno Hariyanto, Annisa Dira Hedyan Irawati Herlambang, Moch. Faisal Hidayat, Ar Rohman Taufiq Hidayati, Nurin Nikmah Hiep, Nguyen Trong I Nyoman Suluh Wijaya Jenny Ernawati Johan Wahyudi Ludfi Djakfar M. Zainul Arifin Miftahulkhair, Muh Nila Eka Saputri Putra, Syah Gemma Rahmawati Rahmawati Rendra Graha, Dimas Tri Safa’at, Rachmad Septiana Hariyani Septiana Hariyani Utami, Desak Putu Martha Waloejo, Budi Sugiarto Wawargita Permata Wijayanti Yusniar, Lely Yuyun Qomariyah