ABSTRACT
BACKGROUND: Work-at-home jobs may raise the risk of mental health concerns as they blur the boundaries between one's job and personal responsibility. This study aimed to develop and test the feasibility of an online psychological intervention using the WhatsApp (c) messaging application.MATERIALS AND METHOD: During the 14-day intervention period, two messages were sent twice a day consisting of positive psychological affirmation aimed at assisting participants (N= 48;13 males and 35 females) to achieve work-life balance and better sleep quality. The Indonesian-adapted Fisher's work-life balance scale and the sleep disturbance scale were used to measure both constructs. These measures were administered at the beginning and end of the intervention. In addition, we provided participants with brief open-ended questions to check the program's feasibility and acceptance of the intervention.RESULTS: A paired t-test analysis revealed that although there was an increase in work-life balance scores in the post-test com pared to the pre-test, the difference was not significant (t (47) =-1.75, p >.05). Nevertheless, individuals' sleep quality scores improved significantly after the intervention (t (47) = - 2.85, p <.05). DISCUSSION: The findings of this study are expected to provi de a preliminary argument to further pursue the development of behavioral interventions using online strategies in Indonesia during the pandemic.
ABSTRACT
SMPN 174 Jakarta is a junior high school in East Jakarta that has 719 students and 22 classrooms, including being one of the schools with government programs in the form of assistance such as Smart Jakarta Card, Smart Indonesia Program, and School Operational Assistance. To meet educational needs and distribute to students who are entitled to assistance. Currently, the selection process for student beneficiaries at SMPN 174 Jakarta is still done manually, so there is a risk of making decisions that are not on target and require a longer time. Therefore, a decision support system was made to select students who receive assistance. The development of the decision support system used in this research is the Multi-Objective Optimization Method on the Basic of Ratio (MOORA). The tests include black-box testing and comparison testing of calculation results with manual calculations. The Decision Support System for Selection of Student Recipients for the Impact of COVID-19 has been successfully created, and the results of selecting recipients of this assistance are obtained based on the calculation of the highest optimization value. The verifier has approved them with a percentage of 66.7% strongly agreeing and 33.3% agreeing. © 2022 IEEE.
ABSTRACT
High number of deaths due to Covid-19 outbreak affect people in various ways including their economic and psychological side. Previous studies were carried out in analyzing various symptoms in COVID-19 patients. Patients in severe conditions are usually found with a white spot in their lungs. Therefore chest x-ray is one of the necessary medical assessment to examine the patients. This study focus on determining whether a patient suffered from COVID-19 by analyzing their chest X-rays photos. A total of 864 X-rays photos were used as a dataset. Earlier steps in processing the dataset included removing the noise, equalizing the size and increasing the accuracy value. The Local Binary Pattern (LBP) method was used to extract the dataset feature. The performance analysis result was a precision value of 78.5%, recall of 78%, and f-measure of 79%. © 2021 IEEE.