STUDENT'S READINESS TO CARRY OUT FACE-TO-FACE LEARNING AT KENDARI VOCATIONAL HIGH SCHOOL

The slowing spread of Covid-19 infections has brought positive changes in the education sector. The issue of implementing limited face to face learning begins to excite students in gaining knowledge. Online learning effects for approximately two years was relatively difficult to keep students away from themselves. This study time to determine how the level of student learning readiness in facing offline learning. This was quantitative research with a survey type. The population were State vocatoonal high school 2 Kendari students, totaling 558 students. The sample was drawn randomly with a magnitude estimated using the Slovin formula at a significance of 5% so that the total sample size was 233 students. Data were collected by learning readiness scale. Data were analyzed descriptively and comparative statistics. The results showed that the learning readiness of State vocatoonal high school Negeri 2 Kendari students was in the high category and female students had a higher level of learning readiness than male students.

Location Based Sentiment Mapping of Topics Detected in Social Media

Social media has risen as a powerful social link among people. It gives them an easy, unsupervised, rapid and flexible platform for sharing information with each other. It serves like a virtual environment where there is no direct or indirect executive power to dictate their opinions. They feel free to appreciate or criticize the actions of others. That is why social media posts carry true reflection of public feelings over any social event. This very nature of social media content makes it a valuable asset for research community to dig deeper and find meaningful concerns out of its huge chunks. This research work explores how people across different regions of Pakistan think about any social media topic. The research work proposed a model to extract spatial, topical and sentimental information of social media posts and combine them together to perform location-based sentiment mapping of the topics detected in social media. The study area has been restricted to Pakistan.The research work started with collection of tweets using streaming API of the Twitter. The information contained in a 3rd party dataset worldCities was used to filter out Pakistani tweets and perform indirect conversion of user profile location into geo-location, as geo-location was directly not available with tweets. Probabilistic topic model; Latent Dirichlet Allocation (LDA) was used to extract latent topics from the dataset. CRAN library was used for sentiment classification of the tweets into 3 classes: positive, negative and neutral. The processed dataset was uploaded to a persistent storage Google Drive and then imported into a web based interactive self-written Shiny Application to read geo-spatial, topical and sentimental information of the tweets from processed dataset and produce interactive geographical map. The interactive geographical map has also been supported by heatmap, scatter plots, bar graphs and pie charts in order to enhance visualization process. The novelty of this research work is its location-based visualization of the strength of public opinion on any emerging event or topic in Pakistan.