# Benchmarking of Multi-Class Algorithms for Classifying Documents Related to Stunting
###### Abstract  
Stunting is a condition in which children experience impaired growth and development, caused by malnutrition, repeated infections, and inadequate psychosocial stimulation. It often remains unrecognized due to a lack of awareness in the community. Therefore, the first step towards developing a solution for stunting is to understand the level of awareness and the sentiment of the community towards issues related to stunting. As online media are widely used in everyday life, they offer significant potential towards providing such an understanding. However, exploiting this potential requires extensive identification of documents containing discussions of stunting among lay people, to accurately gauge the awareness and sentiments of the community towards stunting. This task is a multi-class classification problem. We perform a benchmark study, using data from the Indonesian context, to comparatively evaluate the performances of four algorithms, i.e., logistic regression, naive Bayes, random forest, and support vector machine (SVM), and three extracted features, namely term occurrence, term presence, and term frequency-inverse document frequency (TF-IDF). The SVM method coupled with TF-IDF produced the highest accuracy value of 0.98, with a standard deviation of 0.03, due to its capability to automatically model the interaction between features. s _article_supplementary_supplementary_supplementary_supplementary_supplementary_supplementary_supplementary_supplementary_supplementary_supplementary_supplementary_supplementary_supplementary_supplementary_supplementary_supplementary_supplementary_supplementary_supplementary_supplementary_supplementary_supplementary_supplementary_supplementary_supplementary_supplementary_supplementary_supplementary_supplementary_sup_supplementary_sup_supplementary_sup_sup_supplementary_  
A significant factor that enables the prevalence of stunting is the lack of awareness around issues related to stunting, its causes, and its dangers. Commonly, stunting often advances unrecognized, particularly in communities where a short stature is considered normal [3]. Therefore, there is a need to understand the level of awareness and appreciation from the public regarding the causes and dangers of stunting. Such an understanding can enable stunting campaigns to be better targeted and more effectively reduce stunting rates in Indonesia. In this study, we contribute to the development of such an understanding of community awareness and sentiment towards stunting issues, with a focus on the Indonesian context, by exploring the mining of public discussions and articles regarding stunting in Indonesia. As social media and other online media, including online news and crowd-sourced content, are used widely in everyday life, they offer significant potential for mining information about the level of awareness and the sentiment of the society towards stunting in Indonesia. However, data on social and other online media can pertain to various subjects, and it can be challenging to retrieve data that is specifically related to stunting. Specifically, a document, e.g., a post or an article, might be unrelated to stunting, might contain information about stunting, or might contain a discussion of stunting among lay people. In order to correctly gauge the level of awareness and the sentiment of the community towards stunting, we need to extensively identify documents containing discussions related to stunting among lay people. Since there are three classes of documents as mentioned above, the task of automatically detecting a document containing a discussion of stunting among lay people is a multi-class classification task. In this study, we perform a comparative evaluation of the performances of several algorithms, to provide a benchmark of algorithms for multi-class classification, and specifically for Indonesian language documents related to stunting issues.