Pemodelan Indeks Ketahanan Pangan di Indonesia Berdasarkan Pendekatan Regresi Logistik Ordinal Data Panel Efek Acak

Increasing economic activity is part of the Sustainable Development Goals (SDGs) in encouraging economic growth. This encourages the transformation of the digital economy that gave birth to a payment system with electronic money. The use of electronic money has many benefits such as accelerating the circulation of money and providing convenience in every economic and trading activity. In Indonesia, the volume of electronic money transactions has increased significantly in recent years. For this reason, it is very important to forecast the volume of electronic money transactions in Indonesia as a material for evaluating policies to increase economic growth through the role of the digital economy. This is the aim of this research. In this study, the forecasting of the volume of electronic money transactions is carried out by considering several exogenous variables such as electronic money infrastructure and the conditions of the COVID-19 pandemic. The study was conducted by comparing the accuracy of the model obtained from the ARIMAX-GARCH model and Support Vector Regression (SVR) to predict the volume of electronic money transactions in Indonesia. The results showed that the two exogenous variables had a significant effect on the volume of electronic money transactions. In addition, the SVR model shows better accuracy than the ARIMAX-GARCH model which is indicated by the Mean Absolute Percentage Error (MAPE) value of the testing data of 7.01% and 3.35%, respectively. Thus, this research is expected to be used as a basis for formulating policies related to the development of the digital economy, especially the use of electronic money to support economic growth in Indonesia.

Unemployment is someone who has been classified in the active labour force is looking for work at a certain wage level, but not getting the job they want. According to the International Monetary Fund (IMF) 2023, Indonesia is ranked second highest in Southeast Asia, ranked 16th in Asia and ranked 58th in the world with a percentage of 5.45%. The data used in this study is semester data (February and August) regarding the number of open unemployment according to the highest education completed in Indonesia taken from the website of the Central Statistics Agency (BPS) starting from 2000 to 2022. This study using comparison of multi response Fourier series regression with trigonometry method using Gamma and the Vector Error Correction Model (VECM). The result of this study is Fourier series regression method of the cos function with gamma is the best model in predicting because this method has smaller MAPE value compared to VECM method. The MAPE of Fourier Series method is 0.01%, in other hand the MAPE of VECM method is 18.90% which can be categorized as prediction results with the Fourier Series method are very accurate. The results of prediction are expected to be used as reference for government to making ideal future plan to minimalize the rate of open unemployment in Indonesia.

Community welfare is an integral part of efforts to achieve several targets of the Sustainable Development Goals in Indonesia, namely ending poverty, as well as decent work and economic growth. For this reason, the grouping of provinces in Indonesia based on indicators of the level of community welfare is carried out to improve the standard of living of the Indonesian people by comparing the performance of several hierarchical cluster methods. The best cluster analysis method is obtained by looking at the smallest internal cluster dispersion rate (icd rate) and the largest R². The variables used in this study are the percentage of families who have the largest building floor in the form of land, inadequate drinking water sources, do not have electricity, have inadequate sanitation, the unemployment rate, the poverty rate in villages and poverty rate in cities. Based on the analysis result, the most optimal method was obtained in grouping provinces in Indonesia based on level of community welfare indicators, which is Ward’s method, with the optimal number of clusters forming as many as 5. This study is expected to help improving the welfare of the people in Indonesia covering various aspects of life. Thus, the standard of living of the Indonesian people can increase and the SDGs targets in Indonesia can be achieved.

In line with the Sustainable Development Goals (SDGs), the Infant Mortality Rate (AKB) is a very important health indicator, especially in neonatal and perinatal care. West Java Province consistently ranks third nationally in terms of infant mortality in 2020 and 2021. This study analyzes the factors influencing infant mortality in West Java in 2022 using secondary data from the 2022 West Java Provincial Health Profile. The response variable is the number of infant deaths, while the predictor variables include the percentage of K-4 coverage (X1), high-risk pregnancy (X2), family with PHBS (X3), exclusive breastfeeding (X4), and complete immunization coverage (X5). Given the count-based nature of the data, Poisson regression was used, which assumes equidispersion where the variance is equal to the mean. However, the analysis found overdispersion, where the variance significantly exceeds the mean, making Poisson regression unsuitable. To address this, Generalized Poisson Regression (GPR) was applied, as GPR introduces a dispersion parameter that accounts for overdispersion, thus better fitting the data. The initial Poisson regression results showed that X1, X2, X4, and X5 significantly influenced infant mortality, while the GPR model showed that only X2 and X3 were significant factors, with a dispersion parameter of -3.116. The GPR model shows that every additional one high-risk pregnancy increases the infant mortality rate by 1.00006, while an increase of one unit of clean and healthy living practices reduces the mortality rate by 2.66%. Model evaluation using AIC, BIC, and RMSE confirmed that the GPR model better described the relationship between predictor variables and infant mortality rates compared to Poisson regression. These findings emphasize the need to use GPR to model cases with overdispersion in count data, so as to provide more reliable information for policy and intervention strategies.

Democracy is a system of government where citizens participate in political decision-making through freely elected representatives. To measure the quality of democracy in Indonesia, the Indonesian Democracy Index (IDI) is used as a composite indicator reflecting various aspects of political freedoms, civil liberties, and governance. The IDI score declined from 6.71 in 2022 to 6.53 in 2023, the lowest in 14 years, indicating disruption in Indonesia’s democracy. Therefore, it is necessary to identify the root causes of the disruption in Indonesia’s democracy through several indicators. This study analyzes the relationship between predictor variables, including socio-economic and development indicators, and IDI using the Multivariate Adaptive Regression Spline (MARS) approach. This study uses the MARS method by considering six predictor variables, namely the Human Development Index (HDI), Gender Empowerment Index (GEI), Information and Communication Technology Development Index (ICT-DI), Press Freedom Index (PFI), Poverty Depth Index (PDI), and High School Completion Rate (HSCR). The data used is secondary data from 34 Indonesian provinces in 2023 obtained from the Statistics Indonesia-BPS. The results showed that the best model was obtained with a combination of BF = 12, MI = 3, and MO = 1 resulting in a GCV value of 11.27 and R2 of 80%. MARS model interpretation identifies the significant influence of social and economic indicators on IDI and is able to explain 80% of data variability. The significance test shows that all predictor variables significantly affect the IDI, with the highest level of importance on the ICT-DI variable. Therefore, improving ICT-DI in each province needs to be a major concern as a strategic step to improve the democracy index in Indonesia and support the achievement of Sustainable Development Goal 16 on peace, justice, and strong institutions.