Model Jaringan Syaraf Tiruan untuk Memprediksi Indeks Plastisitas Tanah

Abstract

Rahmawati W, Suharyatun S, Sugianti C. 2019. Artificial neural networks model to predict soil plasticity index. In: Herlinda S et al. (Eds.), Prosiding Seminar Nasional Lahan Suboptimal 2019, Palembang 4-5 September 2019. pp. 418-423. Palembang: Unsri Press.
  
Soil index plasticity is an important soil physical property of the soil related to the tillage intensity , especially if it is done by machine such as a tractor. This study aim is to build an artificial neural network (ANN) model that connects the soil texture with the  soil index plasticity. The research was conducted in several stages, namely: (1) soil texture determination, plastic limit and liquid limit in the laboratory, (2) plasticity index calculation, (3) Soil texture-soil plasticity index ANN model built. ANN models are created using 3 input variables, namely x1: clay content, x2: silt content and x3: sand content. The model uses 2 layers, with a logsig-tangig-purelin activation function. The results of the model training resulted in a RMSE (Root Mean Square Error) value of 1.6542 and an R2 value of 0.9570. Model validation produces a correlation value of predictive data and R2 observation data of 0.9332.

Keywords: artificial neural network models, soil consistency, soil physical properties, soil texture