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Diabetes is one of the high-risk diseases. The most prominent symptom of this disease is high blood sugar levels. People with diabetes in Indonesia can reach 30 million people. Therefore, this problem needs further research regarding the factors that cause it. Further analysis can be done using ordinal logistic regression and random forest. Both methods were chosen to compare the modelling results in determining the factors causing diabetes conducted in the CDC dataset. The best model obtained in this study is ordinal logistic regression because it generates an accuracy value of 84.52%, which is higher than the ordinal random forest. The four most important variables causing diabetes are body mass index, hypertension, age, and cholesterol.

Educated unemployment is caused by a misalignment of educational development planning and employment development, resulting in underemployed graduates from various educational institutions. Unemployment data in DKI Jakarta shows an unequal class. Unbalanced data is a severe problem of modeling because it can cause prediction errors that affect the accuracy of the resulting model. Using SMOTE to handle unbalanced data will likely increase the model’s accuracy. This study aims to find the best model for identifying the factors influencing the status of educated unemployment using logit and probit models and handling unbalanced data using SMOTE. The results showed that the independent variables that affect the status of educated unemployment in the logit and probit models are the same: age group and participation in training. The independent variables that affect the status of educated unemployment in the logit and probit models with SMOTE are also the same: age group, marital status, and participation in training. Unbalanced data handling using SMOTE can increase the balanced accuracy value significantly. Balanced accuracy values for the logit and probit models with SMOTE are higher than the logit and probit models without SMOTE. The logit model with SMOTE is the best because it has the highest balanced accuracy value compared to other models. According to the logit model with SMOTE, the educated unemployed in DKI Jakarta are young and have never married. There is a need for the government to play a role in improving the quality of educational institutions in producing graduates who meet company qualifications and can be hired by employers. Unemployed people who have attended the training, despite having a higher education, may also become unemployed. The training provided has not been able to reduce the unemployment rate. As a result, the government should be able to provide training to improve entrepreneurship skills while also providing capital in the form of business loans to reduce educated unemployment.

The high population growth rate can impact various fields due to several factors. Some of the impacts of this high rate are high poverty rates, unemployment, consumption levels, inequality in education figures, gender empowerment index, and increasingly narrow land or area. Therefore, research on the rate of population growth using data on poverty, unemployment, consumption levels, education rates, gender empowerment index, and area makes sense. This data was taken from the official website of the Central Statistics Agency for six provinces on the island of Java, Indonesia. The data used contains missing data so that the missing data is presumed by using the k-nearest neighbour method. The estimated missing data values were modelled using binary logistic regression. Variables that significantly affect the rate of population growth, namely the level of consumption, gender empowerment index, and area, are obtained using the backward stepwise method and are selected based on the smallest Aikakes criterion information value or the one with the most excellent accuracy rate.