Machine Learning : Factor Analysis of Food Security using Big Data in Indonesia
64th ISI World Statistics Congress - Ottawa, Canada
Format: CPS Abstract
Keywords: factoranalysis, foodsecurity, machinelearning
Session: CPS 88 - Big data II
Thursday 20 July 8:30 a.m. - 9:40 a.m. (Canada/Eastern)
The issue of a global economic recession that will occur in 2023 is getting stronger. This is starting to be seen from the start of the phenomenon of high inflation in various countries which caused central banks in several countries to raise interest rates, including Indonesia. The threat of an economic recession is inseparable from the risk of food insecurity and even the food crisis that has hit various countries. This food insecurity is caused by the price of food, energy and fertilizer as a result of the prolonged conflict between Russia and Ukraine. Limited data in creating measures of food insecurity makes it difficult to determine policies that can be taken by the state in overcoming this food insecurity, therefore it is necessary to conduct research on the available big data. Several studies have conducted trials measuring food insecurity from regional weather and climate variables, availability of agricultural land, satellite imagery, night light data, food price increases, and population density. This study aims to predict food insecurity to these variables with the Indonesian as unit of analysis using the machine learning factor analysis method.