CRISP-DM for Data Quality Improvement to Support Machine Learning of Stunting Prediction in Infants and Toddlers

Many Machine Learning (ML) projects ended up only as proof concept and failed to be produced. Therefore, this research focused on well-defined processes that must be followed, adopted the Cross-Industry Standard Process for Data Mining (CRISP-DM) with the specifications and requirements of supervised and unsupervised learning which includes a methodology for Classification/grouping. The Data Understanding and Data Preparation phases, used transactional data on examination of infants and toddlers in 2018-2021 on the iPosyandu application. At the Business Understanding stage, the ML was intended to predict stunting, so that data quality of iPosyandu can be informed and then recommendations and feature improvements and assistance for end-users can be made. The output of Data Understanding and Data Preparation was in the form of baby & toddler examination dataset, which was used in the Machine Learning modeling stage, especially to classify and predict nutritional/stunting status. Of the 192 tables contained in the iPosyandu application, there were 5 main tables that were needed to define the dataset. 75,652 data on infants and toddlers were checked with 49,615 data of examinations in 3173 Posyandu, which resulted in clean data of 39,411 rows of datasets for all examinations and 13,868 rows of datasets for the last examination of infants and toddlers. The dataset was combined with the nutritional status of infants and toddlers resulting from the calculation of the baby’s weight, length of the baby’s body, and the comparison of the baby’s height and weight. The dataset was tested into the ML using the Orange Application and produced a Classification model that can be used for prediction. From the results of the modeling evaluation, it can be seen that the Naïve Bayes Algorithm had an advantage with a predictive value of 0.851 while the Tree algorithm was 0.848 and the Neural Net was 0.845. From the overall evaluation, it can be concluded that there is a need to improve data quality by improving the application and improving the literacy of the end-users, so that the data has better quality and ready to be used as a ML dataset. The selected features can be aggregated to simplify the modeling process so as to obtain the expected model.