<![CDATA[Chilli consumption keeps increasing along with the annual population increase in Indonesia. Meanwhile, chilli prices also fluctuate due to rainfall, affecting production and inflation. In the industrial era 4.0, IT support is crucial in various fields including in agriculture such as chilli planting to help stakeholders, both in the economy and agriculture sectors, make decisions based on accurate predictive data support. The study aims to compare the accuracy of two machine learning algorithm models, i.e., Multilayer Perceptron (MLP) and Recurrent Neural Network (RNN), for time-series regression implementable to predict chilli prices in Tangerang City. The experimental method stages include business understanding, data understanding, data preparation, modelling, evaluation, and deployment stages. The required dataset attributes include red chilli prices, date, inflation, and rainfall. This research is expected to contribute to machine learning algorithms to assist stakeholders and to be implemented by information system developers. The research result indicates that the MLP algorithm with the rmsprop optimizer performs better than the RNN with the metric measurement of Loss = 0.0038, MSE = 10271959,0 and MAPE = 3.79%. Suggestions for further research include the urgency to innovate architectural models, either for activation functions, optimizers, or other regression algorithms for better metric measurement results.]]>
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