Integration of Naïve Bayes-Based Stunting Status Classification and GIS Hotspot Mapping for the Identification of Priority Areas in Tomohon City, Indonesia

Abstrak

Stunting remained a public health problem that required data- and area-based monitoring so that interventions could be implemented in a targeted manner. This study aimed to develop an integrated system for classifying stunting status and identifying priority areas in Tomohon City through the combination of WHO Z-Score standards, the Naïve Bayes algorithm, prevalence calculation, and hotspot mapping based on a Geographic Information System (GIS). This study employed a Research and Development (R&D) approach consisting of needs analysis, design, implementation, testing, and evaluation stages. Toddler data were obtained from the Tomohon City Health Office, including age, sex, height or body length, weight, residential area, urban village, district, and community health center. The system was developed using MySQL, Python, PHP Framework CodeIgniter 3, and GIS. The results showed that the system was able to classify toddlers’ nutritional status into normal, stunted, and severely stunted categories, calculate prevalence by urban village, and display the distribution of cases in the form of a digital map. Gaussian Naïve Bayes modeling using 970 training data points and 243 testing data points produced an accuracy of 94.7%, precision of 31.6%, recall of 33.3%, and F1-score of 32.4%. GIS hotspot visualization helped identify priority areas, although data coverage still needed to be expanded to make the results more representative.