THE INVOLVEMENT OF ENGLISH LECTURERS AS PARENTS IN TEACHING ENGLISH FOR THEIR CHILDREN AS YOUNG LEARNERS IN THIS 21ST CENTURY

Learning English is very important in this 21st Century where English has a big influence in all aspect of life. The right time to teach English is at the primary age because children have amazing abilities in studying a language. In the process of teaching English not only teacher’s support is needed but also parents’ involvement in this case parents as English Lecturer, it is not enough just in the school but also in the family environment. This research comes to investigate the involvement of Parents in the process of learning English for their children as young learners in this 21st century. The Researcher is quantitative research method, in order there are two instruments are provided as the procedure in collecting the data needed, these are questionnaire and interview. The sample consisted of 24 parents as the English lecturers and have children categorized as young learners in Universitas Muhammadiyah Parepare and Universitas Muhammadiyah Makassar, in Indonesia. The result of this research shown that parents’ as English lecturers  have a very positive perspective in teaching English to their children as young learners more over English will support their children carrier in the future, but their involvement are not very intense.  Even though, they are excited to be involve in teaching English to their children as a foreign language for them without forgetting their local language, however they agreed that they can face some challenges in this process

PREDICTION OF LIFE EXPECTANCY FOR ASIAN POPULATION USING MACHINE LEARNING ALGORITHMS

Predicting life expectancy has become more important nowadays as life has become more vulnerable due to many factors, including social, economic, environmental, education, lifestyle, and health condition. A lot of studies on life expectancy have been carried out. However, studies focusing on the Asian population are limited. This study presents machine learning algorithms for life expectancy based on the Asian population dataset. Comparisons are made between tree classifier models, namely, J48, Random Tree, and Random Forest. Cross validations with 10 and 20 folds are used. Results show that the highest accuracy is obtained with Random Forest with 84% accuracy with 10-fold cross-validation. This study further identifies the most significant factors that influence life expectancy prediction, which includes socioeconomic factors and educational status, health conditions and infectious disease