Achieving Zero Stunting: A Sustainable Development Goal Interlinkage Approach at District Level

The sustainable development goals (SDGs) provide an integrated framework of targets and indicators, including the elimination of stunting, to support better development planning. Indonesia faces a significant challenge as it ranks fourth globally in terms of stunting prevalence, exacerbated by disparities across regions, gender, and socioeconomic status, further compounded by the ongoing COVID-19 pandemic. Given the interlinked nature of SDGs, this study provides empirical support for the prioritization of SDG indicators, primarily in the context of stunting elimination at the district level in Indonesia. This study employed a combination of economic complexity and network theory, utilizing data from a comprehensive set of 54 indicators spanning 28 targets within 13 SDG goals in 514 districts. The analysis is based on network metrics, including revealed comparative advantage (RCA), proximity, centrality, and density to establish the SDG interlinkage network and identify key priority indicators. The findings highlight the importance of prioritizing indicators such as civil registration, health facilities and services, access to basic facilities and housing, and access to ICT in efforts to reduce stunting, particularly among disadvantaged households. Given the unique resources and capacities of each region, our analysis offers district-specific prioritization strategies for stunting elimination.

Artificial Neural Network-Based Machine Learning Approach to Stock Market Prediction Model on the Indonesia Stock Exchange During the COVID-19

The global COVID-19 pandemic has caused panic. In addition, it disrupted life and economic activities around the world. Prediction of the stock market during the COVID-19 pandemic became a major challenge because the data was not stationary, random, and complex nonlinear system. For this reason, an in-depth study of the following trends is required to develop an adequate predictive model to predict the stock market during the pandemic. This study designs a stock market prediction model during the COVID-19 pandemic on the Indonesia Stock Exchange using a deep learning approach based on artificial neural networks. The object of this research is the pharmaceutical industry in the health sector listed on the IDX. The input variables are the proposed model for predicting stock prices with daily stock price movements, including COVID-19 trend indicators, and the government's response tightness index to COVID-19 in Indonesia. The study results show that all proposed model systems achieve highly accurate forecasting for the stock market price prediction with MAPE 10%. Model 6-20-20-1 is the best model of all tested models, with MSE = 0.00055, RMSE = 0.007418, and MAPE = 1.17%. [ FROM AUTHOR] Copyright of Engineering Letters is the property of Newswood Limited and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)