Prediksi Pertumbuhan Tanaman Wortel Berdasarkan Hasil Penginderaan Kondisi Lingkungan Tanam Menggunakan Multiple Linear Regression

Abstract

Controlling plant growth is still done manually by observers, which can take quite a long time. For this reason, it is necessary to have tools for observers to monitor plant growth and development and predict so that there are no defects and their economic value does not decrease. The aim of this research is to predict plant growth based on parameter values from sensing the conditions of the planting environment using multiple linear regression. The value that will be predicted is plant growth involving several parameters including temperature, humidity and soil pH. Researchers used carrot plant growth data collected over 80 days. The stages carried out are (1) Collecting data on plant growth, temperature, humidity and pH. (2) Cleaning data taken from the database. (3) Establishing Train and Validation models. (4) Creating a Multiple Linear Regression model. (5) Output predictions for carrot plant growth and measuring the performance of the prediction model with R-Aquare with an accuracy of 77.77%.