PEMANFAATAN MODEL CELLULAR AUTOMATA – ARTIFICIAL NEURAL NETWORK UNTUK PREDIKSI PERUBAHAN TUTUPAN LAHAN DI KABUPATEN KENDAL TAHUN 2040

AFDALUL ROIHAN, . (2026) PEMANFAATAN MODEL CELLULAR AUTOMATA – ARTIFICIAL NEURAL NETWORK UNTUK PREDIKSI PERUBAHAN TUTUPAN LAHAN DI KABUPATEN KENDAL TAHUN 2040. Sarjana thesis, UNIVERSITAS NEGERI JAKARTA.

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Abstract

Perubahan tutupan lahan di Kabupaten Kendal semakin intensif akibat urbanisasi, perkembangan kawasan industri, dan meningkatnya aktivitas ekonomi wilayah. Penelitian ini bertujuan menganalisis perubahan tutupan lahan periode 2008–2024 serta memprediksi kondisi tahun 2032 dan 2040 menggunakan metode Cellular Automata–Artificial Neural Network (CA–ANN). Data utama berupa citra Landsat tahun 2008, 2016, dan 2024 diklasifikasikan menggunakan Support Vector Machine (SVM). Faktor determinan yang digunakan meliputi jarak terhadap pusat pelayanan, pusat ekonomi, jaringan jalan, dan kemiringan lereng yang dinormalisasi dengan fuzzy membership. Klasifikasi menghasilkan overall accuracy sebesar 85,78% (Kappa 0,766) pada tahun 2008, 89,93% (Kappa 0,828) pada tahun 2016, dan 94,66% (Kappa 0,919) pada tahun 2024. Tutupan lahan didominasi kelas pertanian/perkebunan, namun luasnya menurun dari 58.130,73 ha menjadi 57.480,52 ha, sedangkan lahan terbangun meningkat dari 16.089,45 ha menjadi 16.707,07 ha, terutama di koridor Pantura dan sekitar Kawasan Ekonomi Kendal. Simulasi CA–ANN menghasilkan tingkat kesesuaian model sebesar 94,48% dengan Kappa Overall 0,906, Kappa Histogram 0,983, dan Kappa Location 0,922. Prediksi tahun 2032 dan 2040 mengindikasikan peningkatan berkelanjutan pada lahan terbangun serta penurunan bertahap lahan pertanian akibat tekanan perkembangan kawasan urban–industri. Perubahan diproyeksikan terkonsentrasi pada wilayah beraksesibilitas tinggi, terutama di sepanjang jalur Pantura dan sekitar Kawasan Ekonomi Kendal. Temuan ini menunjukkan bahwa aksesibilitas jalan dan perkembangan kawasan ekonomi menjadi penggerak utama perubahan tutupan lahan serta dapat menjadi dasar evaluasi tata ruang dan pengendalian pemanfaatan lahan secara berkelanjutan. ***** Changes in land cover in Kendal Regency have become increasingly pronounced as a result of urbanisation, the development of economical estates, and rising economic activity in the region. This study aims to analyse changes in land cover over the period 2008–2024 and to predict conditions for the years 2032 and 2040 using the Cellular Automata–Artificial Neural Network (CA–ANN) method. The primary data, comprising Landsat imagery from 2008, 2016 and 2024, were classified using a Support Vector Machine (SVM). The determining factors used included distance to service centres, economic centres, road networks and slope gradient, normalised using fuzzy membership. The classification yielded an overall accuracy of 85.78% (Kappa 0.766) in 2008, 89.93% (Kappa 0.828) in 2016, and 94.66% (Kappa 0.919) in 2024. Land cover is dominated by the agricultural/plantation class, although its area has decreased from 58,130.73 ha to 57,480.52 ha, whilst built-up land has increased from 16,089.45 ha to 16,707.07 ha, particularly in the Pantura corridor and around the Kendal Economical Estate. The CA–ANN simulation yielded a model agreement level of 94.48%, with an Overall Kappa of 0.906, a Histogram Kappa of 0.983, and a Location Kappa of 0.922. Predictions for 2032 and 2040 indicate a sustained increase in built-up land and a gradual decline in agricultural land due to pressure from urban–industrial development. These changes are projected to be concentrated in areas with high accessibility, particularly along the Pantura corridor and around the Kendal Economical Estate. These findings suggest that road accessibility and Economical development are the primary drivers of land cover change and can serve as a basis for evaluating spatial planning and the sustainable management of land use.

Item Type:	Thesis (Sarjana)
Additional Information:	1). Prof. Dr. Cahyadi Setiawan, M.Si; 2). Tri Wandi Januar, M.Sc., Ph.D.
Subjects:	Geografi, Antropologi > Geografi Geografi, Antropologi > Matematika Geografi, Kartografi Geografi, Antropologi > Geografi Fisik
Divisions:	FIS > S1 Geografi
Depositing User:	Users 34179 not found.
Date Deposited:	04 Aug 2026 01:57
Last Modified:	04 Aug 2026 01:57
URI:	http://repository.unj.ac.id/id/eprint/68339

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