Objective: Psychological wellbeing of the pilot can affect the flight cognitive function of the pilot, thus endangering the safety of the flight. The level of wellbeing of the pilots is related to the safety climate of the pilot. The purpose of this study is to determine the relationship between the safety climate and psychological wellbeing of civilian pilot in Indonesia. Methods: This was an analytic study using cross-sectional method. The sample was determined by consecutive sampling technique. Data were collected by filling out questionnaires by subjects regarding the variables of the safety climate and psychological wellbeing. The data analysis used was multiple linear regression. Results: The safety climate has a significant effect on psychological wellbeing (ß=0.921). The dimensions of the safety climate which have a significant effect are management (ß=0.135), safety systems (ß=0.143), procedures (ß=0.176), training (ß=0.153), communication (ß=0.232), and operations personnel (ß=0.185). Conclusion: Management, safety systems, procedures, training, communication, and operations personnel have significant effects on psychological wellbeing of civilian pilot in Indonesia.

Breast cancer diagnosis currently relies on clinical information, image radiology and histopathology. However molecular biology aspect needs to be considered for accurate diagnoses. Microarray technology allows the analysis of thousands of gene expression to be used as additional information for breast cancer diagnosis. This study aims to use microarray for breast cancer diagnosis by using machine learning. Machine learning is widely used for pattern analysis and can be used for microarray dataset, such as deep stacking network (DSN). Design of DSN is stacked each of base module which using a simple form of the multilayer perceptron. Using DSN is suitable for complex data like microarray dataset because it has a deep architecture (deep learning). Furthermore, DSN model does not use stochastic gradient descent which is difficult to be implemented on large scale of machine learning. In Indonesia, microarray technology is still not well known, therefore the current studies only use secondary data from cancer patients overseas. DSN which is a deep learning model is suitable to be used for microarray dataset that has a complex structure. Suggested for subsequent study using primary data from patient cancer in Indonesia so that the design model will be more suitable to be implemented for cancer patients in Indonesia.