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Optimalisasi model prediksi kesesuaian lahan kelapa sawit menggunakan algoritme pohon keputusan spasial

Optimization for prediction model of palm oil land suitability using spatial decision tree algorithm

1Department of Informatics, Universitas Teknokrat Indonesia, Indonesia

2Department of Computer Science, Institut Pertanian Bogor, Indonesia

Received: 4 Feb 2020; Revised: 3 May 2020; Accepted: 5 May 2020; Available online: 11 May 2020; Published: 31 Jul 2020.
Open Access Copyright (c) 2020 Jurnal Teknologi dan Sistem Komputer under

Citation Format:
Land suitability evaluation has a vital role in land use planning aimed to increase food production effectiveness. Palm oil is a leading and strategic commodity for Indonesian people, which is predicted consumption will exceed production in the future. This study aims to evaluate palm oil land suitability using a spatial decision tree algorithm that is conventional decision tree modification for spatial data classification with adding spatial join relation. The spatial dataset consists of eight explanatory layers (soil nature and characteristics), and a target layer (palm oil land suitability) in Bogor District, Indonesia. This study produced three models, where the best model was obtained based on optimizing accuracy (98.18 %) and modeling time (1.291 seconds). The best model has 23 rules, soil texture as the root node, two variables (drainage and cation exchange capacity) are uninvolved, with land suitability visualization obtains percentage S2 (29.94 %), S3 (53.16 %), N (16.57 %), and water body (0.33 %).

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Keywords: land suitability; palm oil; spatial decision tree; spatial join relation
Funding: Universitas Teknokrat Indonesia;Institut Pertanian Bogor

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