Algoritme Spatial Decision Tree untuk Evaluasi Kesesuaian Lahan Padi Sawah Irigasi

Abstract

Agriculture has a strategic role in a country whereas food self-sufficiency being the main goal to be achieved. Indonesia has set a strategic plan for increasing the productivity of several commodities, including rice, especially irrigated lowland rice. That matter can be done by agricultural land extensification, which requires a land suitability directional map. This study aims to produce irrigated lowland rice land suitability maps which can be obtained by evaluation using spatial decision tree algorithm. The model is made in two different types, where model Y is an optimized version of model X. The dataset consists of two categories, namely eleven explanatory layers which are land and weather characteristics, and a target layer that represents irrigated lowland rice land suitability in study area of Grobogan Regency, Central Java Province. As an addition to planting requirements, two spatial weather datasets were generated using ordinary cokriging interpolation, which was not used in previous research, while actually being important element for determining plant timing an agricultural commodity. Based on accuracy, model Y is the best model with 96.67%, compared to model X with 86%. Both models make relief variable as the root node, but in spatial decision tree result, model X involves all variables, while model Y does not involve an elevation variable. The addition of weather variables in models is appropriate, as evidenced by the involvement in rules.

Prediction Model for Soybean Land Suitability Using C5.0 Algorithm

“…The three attributes are cation exchange capacity, soil mineral depth, and rainfall, as shown in Table 5 and Table 6. This non-involvement means that three attributes are not essential for determining land suitability class [16], which in this study is commodity soybean.…”

Evaluasi Kesesuaian Lahan Pertanian Untuk Tanaman Jagung Di Kecamatan Kualuh Hilir, Labuhan Batu Utara, Sumatera Utara

“…Menurut (Julianto et al, 2021) penilaian kesesuaian lahan digunakan dalam mengukur potensi penggunaan lahan berdasarkan persyaratan dan kualitas lahan. Penilaian kesesuaian lahan yang digunakan merujuk berdasarkan Food and Agriculture Organization (FAO), yakni: sangat sesuai (S1), cukup sesuai (S2), sesuai marjinal (S3), dan tidak sesuai (N) (Nurkholis et al, 2020). Evaluasi kesesuaian lahan dapat digunakan untuk memperoleh data kemampuan dan kendala suatu lahan, memaksimalkan produksi tanaman per unit lahan, tenaga kerja dan input dan mengidentifikasi faktor-faktor pembatas utama produksi tanaman serta mengurangi dampak negatif pertanian terhadap lingkungan.…”

“…Hasil dari evaluasi lahan tersebut akan memberikan informasi maupun arahan penggunaan sesuai dengan kebutuhan. Penilaian kesesuaian lahan yang pada umumnya digunakan merujuk berdasarkan Food and Agriculture Organization (FAO), yakni: sangat sesuai (S1), cukup sesuai (S2), sesuai marjinal (S3), dan tidak sesuai (N) (Nurkholis et al, 2020). Data yang dianalisis pada penelitian ini merupakan data hasil penelitian (Simanungkalit et al, 2019) berupa data kesesuaian lahan pertanian padi (Oryza sativa L.) di Kecamatan Kualuh Hilir, Kabupaten Labuhanbatu Utara, Sumatera Utara.…”

Prediksi Morfologi Jamur Menggunakan Algoritma C5.0

“…Decision tree merupakan sebuah algoritma dalam machine learning dengan mengguanan aturan untuk membuat atau menentukan sebuah keputusan [1], [6]. Decision tree memiliki struktur seperti pohoh yang sangat memungkinkan hasil, biaya, utility, serta kemungkinan resiko.…”