ANALISIS KUALITAS HASIL PREDIKSI KLASIFIKASI PENGGUNAAN LAHAN MENGGUNAKAN CA MARKOV MODEL BERDASARKAN PETA RENCANA TATA RUANG

Fauzi Janu Amarrohman; Tristika Putri; Bambang Sudarsono; Moehammad Awaluddin; Sawitri Subiyanto

doi:10.14710/elipsoida.2020.9200

DOI: https://doi.org/10.14710/elipsoida.2020.9200

ANALISIS KUALITAS HASIL PREDIKSI KLASIFIKASI PENGGUNAAN LAHAN MENGGUNAKAN CA MARKOV MODEL BERDASARKAN PETA RENCANA TATA RUANG

Fauzi Janu Amarrohman

, Tristika Putri, Bambang Sudarsono, Moehammad Awaluddin, Sawitri Subiyanto

Departemen Teknik Geodesi, Fakultas Teknik, Universitas Diponegoro, Indonesia

Received: 17 Oct 2020; Published: 4 Dec 2020.

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Abstract

Land use changes due to community activities and mobility occur because of the increasingly complex need for land. Spatial analysis is needed to identify land use changes which are subsequently reviewed by the Regional Spatial Plan in accordance with Government Regulation Number 8 of 2013 concerning the accuracy of the RTRW map. In this study, the study area taken was Pati Regency around the South Ring Road which includes four districts. From the acquisition of high resolution satellite imagery data in 2009, 2015 and 2019, predictions were made for the years 2023 and 2030 to determine the development of the area around the South Ring Road. The results of the prediction of land use using CA Markov in 2023 will be compared with the prediction in 2030 to determine the quality of the prediction results of the classification of land use in the prediction year with the same input data interval and exceeding the input data interval by conducting a suitability analysis with the RTRW. In 2023, the category of conformity is 95.41341%, and in 2030 amounting to 95.41340%. This shows that the prediction results of land use change with CA Markov for the same year with the time interval of the input data have insignificant differences with the predicted results with longer intervals when compared to the current RTRW.

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In Vol 3, No 02 (2020): Volume 03 Issue 02 Year 2020

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