Performance of Time-Based Feature Expansion in Developing ANN Classification Prediction Models on Time Series Data - Sri Suryani Prasetiyowati; Yahya, Arnasli; Rohmawati, Aniq Atiqi (International Journal on Information and Communication Technology (IJoICT), 2023)
The prediction problem in most research is the main goal, to estimate future events related to the field under study. Research on classification that involves the prediction process in it, with spatial-time data and influenced by many features, such as the problem of disease spread, climate change, regional planning, environment, economic growth, requires methods that can predict while solving the problem of features and time. To obtain a time-based classification prediction model using many features, this research uses machine learning methods, one of which is Artificial Neural Network (ANN). The scenario carried out is to develop a t+r classification prediction model by expanding features based on the time t-r of the previous period. The performance of feature expansion in the development of ANN classification prediction models is determined based on the optimal accuracy value of the combination of t-r classification prediction models for the previous time period. By implementing the model on the data, it is found that the performance of time-based feature expansion in ANN classification ranges from 3.5% to 11%. While the optimal accuracy value is obtained from the feature expansion scenario of 3 to 5 time periods earlier.