Indonesia is currently facing a triple burden of nutritional problems, including stunting, wasting, and obesity, as well as micronutrient deficiencies such as anemia. These issues are particularly prevalent among adolescents and adults, with common problems including undernutrition, overnutrition, and iron deficiency leading to anemia.This study was conducted in the Nutrition Study Program, Faculty of Health Sciences, Universitas Muhammadiyah Surakarta. It aimed to examine the relationship between macronutrient intake (carbohydrates, fats, and proteins), physical activity, and the nutritional status of students. A cross-sectional study design was employed to collect data at a single point in time and analyze the associations between variables. The results showed that there was no significant relationship between carbohydrate intake and nutritional status, as indicated by a p-value of 0.217 and a correlation coefficient (r) of 0.135. Therefore, the null hypothesis (H0) was accepted. However, a significant relationship was found between fat intake and nutritional status, with a p-value of 0.049, suggesting that fat consumption plays a role in determining the nutritional status of students. On the other hand, protein intake did not show a significant relationship with nutritional status, as indicated by a p-value of 0.470. Similarly, physical activity was not significantly associated with nutritional status, with a p-value of 0.228. In conclusion, the study found that among the variables examined, only fat intake had a significant effect on students' nutritional status. Carbohydrate intake, protein intake, and physical activity did not demonstrate meaningful associations. These findings can serve as a basis for evaluating and improving dietary patterns and healthy lifestyle habits among university students.

During this pandemic, most of people’s activities are carried out through digital media. Both learning and working processes are using the video-conference platform, a platform deemed effective to facilitate the needs of distance communication. One of the limitations of using video-conference lies in difficulty in understanding emotional conditions based on solely camera video. Hence, speakers generally do not know their interlocutors’ feelings related to the materials being presented. Grounded on this issue, we examined a facial expressions-based emotion recognition tool. Micro expression is one of the micro-languages of communication. Machine learning model developed in this study was Deep Learning with Convolutional Neural Network (CNN). The library that was used was Keras, this was used to recognize micro expression pattern. Additionally, OpenCV was also used for the general face recognition process. Both libraries were operated using Python programming language. The result of the micro expression test involving thirty participants detected three types of facial expression, namely joy, sadness, and anger expression. However, face recognition applied in the present study still needed some improvements, especially for anger and sadness expression. With regard to joy expression, 89% of the expression were recognized. Based on the recorded data, it is necessary to improve the recordings criteria to obtain a clearer expression.