This study aims to analyze the direct and indirect correlation between environment-based curriculum, teacher pedagogical competence and learning quality through literacy learning. This research used a correlational study with a quantitative approach. Two hundred and eighteen junior high school teachers at adiwiyata schools were involved as population of this study. The researchers used proportional random technique and obtained as many as 141 people. The data collection was carried out using instruments consisting of an environment-based curriculum (17 items), teacher pedagogic competence (21 items), literacy learning (13 items) and learning quality (29 items). The validity and reliability tests were performed using the Alpha Cronbach. Then, the data of this study was analyzed using descriptive statistics with path analysis to see direct and indirect correlations between variables, by first carrying out the normality test, homoscedasticity test, multicollinearity test and correlation test. The results of the study show that there is a correlation between: (1) environment-based curriculum and learning quality, (2) teacher pedagogic competence and learning quality, (3) literacy learning and learning quality, (4) environment-based curriculum and literacy learning, (5) competency teacher pedagogy with literacy learning, (6) the correlation between environmentbased curriculum and learning quality through literacy learning, (7) the correlation between teacher pedagogic competence and learning quality through literacy learning
This study aims to examine the architectural performance of the Recurrent Neural Network (RNN) model in predicting Covid-19 cases in Lampung Province. The RNN method is part of Deep Learning which will be used to model data on Covid-19 cases in Lampung Province from March 26, 2020 to March 28, 2021. The RNN model was chosen because the Covid-19 data is in the form of a time series and the advantages of RNN are that it can capture information on the data time series using multiple network layers which allow better modeling and resulting in high prediction accuracy. The data is divided into 3, namely active cases, recovered cases, and dead cases. After preparing the data, the 368 data were divided into 294 initial latih data and 74 test data. After latih on the data for each data, then a test is carried out on the data for each data as a reference for predicting the latest data. The most optimal results show the cumulative active case model with RMSE=0.0022; for cumulative recovery cases obtained RMSE = 0.0007; while the cumulative death cases obtained RMSE = 0.0012. Based on the modeling error, then make predictions on the three cases which results in RMSE = 0.001 for cumulative active cases; RMSE=0.0027 for cumulative recovery cases; and RMSE=0.001 for cumulative death cases.
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