Classification of Student Readiness to Implement Face-to-face Learning in a Pandemic Period with the Naive Bayes Algorithm (Case Study: SMA Negeri 61 Jakarta)
Abstract
Indonesia is one of the countries affected by Covid-19. The impact of the Covid-19 pandemic has made all aspects of life experience obstacles, including the field of education. Finally, teaching and learning activities in schools had to be stopped and replaced with distance learning with the aim of breaking the chain of Covid-19 spread. In 2022, the government will start implementing face-to-face learning by implementing health protocols. But apparently, this policy reaps various pros and cons. Nave Bayes algorithm is one of the most popular classification algorithms. The purpose of this study is to classify student readiness data in carrying out Face-to-face Learning (PTM) during the Covid-19 pandemic at one of the public high schools (SMA) in the Jakarta area, namely SMAN 61 Jakarta. This study uses a dataset of 267 records which will be divided into two data, namely training data and testing data with a ratio of 90:10. The total data used in the training data is 240 records and the total data used as testing data is 27 records. The data will be applied to RapidMiner and MS Excel tools with the nave Bayes algorithm to determine whether or not the classification results of the two are the same. Based on the results of the RapidMiner and MS Excel tools, the results obtained are 92.59% accuracy, 96% precision, and 96% recall.
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