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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.

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.

Parental self-efficacy is an important predictor that influences the positive behavior of parents in parenting. This study aims to determine the effect of smart parenting training on increasing parental self-efficacy of working mothers with toddlers. This study used a quasi-experimental design with the untreated control group design with dependent pretest and posttest samples. A total of 14 participants were divided into two groups, namely seven participants in the experimental group and seven participants in the control group. Smart parenting training uses experiential learning models and e-learning video-based methods. Measurements in this study used a short version of the Self-efficacy for Parenting Tasks Index-Toddler Scale (SEPTI-TS). The results of data analysis using the Mann-Whitney U show that there is a significant difference in parental self-efficacy (ρ=0.002) in the experimental group and the control group, and based on the Wilcoxon sign rank test shows that there is an increase in parental self-efficacy in the experimental group (ρ=0.018) after receiving smart parenting training, whereas in the control group participants (ρ=0.680) there was no increase in parental self-efficacy. The results showed that smart parenting training affected increasing parental self-efficacy for working mothers with toddlers.    

Parental self-efficacy is an important predictor that influences the positive behavior of parents in parenting. This study aims to determine the effect of smart parenting training on increasing parental self-efficacy of working mothers with toddlers. This study used a quasi-experimental design with the untreated control group design with dependent pretest and posttest samples. A total of 14 participants were divided into two groups, namely seven participants in the experimental group and seven participants in the control group. Smart parenting training uses experiential learning models and e-learning video-based methods. Measurements in this study used a short version of the Self-efficacy for Parenting Tasks Index-Toddler Scale (SEPTI-TS). The results of data analysis using the Mann-Whitney U show that there is a significant difference in parental self-efficacy (ρ=0.002) in the experimental group and the control group, and based on the Wilcoxon sign rank test shows that there is an increase in parental self-efficacy in the experimental group (ρ=0.018) after receiving smart parenting training, whereas in the control group participants (ρ=0.680) there was no increase in parental self-efficacy. The results showed that smart parenting training affected increasing parental self-efficacy for working mothers with toddlers.