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Structural Equation Model (SEM) merupakan salah satu dari teknik analisis dalam ilmu statistika dengan gabungan dua teknik multivariat yaitu analisis faktor konfirmatori, analisis regresi, dan analisis jalur. Pada penelitian ini diterapkan analisis SEM untuk menganalisa faktor yang memengaruhi masyarakat Lombok yang berdampak Covid-19 terhadap dukungan untuk pariwisata. Data yang digunakan adalah data primer yang diperoleh dengan kuesioner kepada masyarakat Lombok pada tahun 2021. Pada studi kasus ini peneliti mengambil dua faktor yang dapat memengaruhi dukungan terhadap pariwisata (ST) yaitu resiko yang dirasakan (PR) dan solidaritas emosional (ES). Tujuan dari penelitian ini untuk mengetahui pengaruh variabel PR dan ES terhadap ST. Berdasarkan hasil analisis diperoleh solidaritas emosional (ES) berpengaruh signifikan pada dukungan terhadap pariwisata. Sementara itu, resiko yang dirasakan (PR) dan solidaritas emosional (ES) bersama-sama secara signifikan mampu menjelaskan pengaruhnya dukungan terhadap pariwisata (ST) sebesar 72,6% sedangkan 27,4% dijelaskan oleh variabel lain diluar model.

In machine learning study, classification analysis aims to minimize misclassification and also maximize the results of prediction accuracy. The main characteristic of this classification problem is that there is one class that significantly exceeds the number of samples of other classes. SMOTE minority class data is studied and extrapolated so that it can produce new synthetic samples. Random forest is a classification method consisting of a combination of mutually independent classification trees. K-Nearest Neighbors which is a classification method that labels the new sample based on the nearest neighbors of the new sample. SMOTE generates synthesis data in the minority class, namely class 1 (cervical cancer) to 585 observation respondents (samples) so that the total observation respondents are 1208 samples. SMOTE random forest resulted an accuracy of 96.28%, sensitivity 99.17%, specificity 93.44%, precision 93.70%, and AUC 96.30%. SMOTE K-Nearest Neighborss resulted an accuracy of 87.60%, sensitivity 77.50%, specificity 97.54%, precision 96.88%, and AUC 82.27%. SMOTE random forest produces a perfect classification model, SMOTE K-Nearest neighbors classification produces a good classification model, while the random forest and K-Nearest neighbors classification on imbalanced data results a failed classification model.

This study aimed to describe the results of learning through the use of media folder. The subject of research grade II SDN 009 Tanjung Palas. Improved learning outcomes in terms of four categories of goods, namely absorption, the effectiveness of learning, mastery learning completeness students both individually and classical completeness and accuracy of the results of learning. The instrument used for data collection is an oral test and a written test in the form of worksheets performed after the learning process. Results Shows absorption learning outcomes on average 83% is considered very good, very effective learning effectiveness categorized with an average of 70. While the completeness of student learning outcomes Presented complete 100%. In addition, increase the thoroughness of students in each cycle, the first cycle (58.82%), the second cycle (70.59%), the third cycle (88.24%). Based on these results it can be concluded that through active learning model of revisiting the difficulty can improve students' mathematics learning outcomes.

Reporting of the estimated glomerular filtration rate (GFR) in every creatinine test is suggested, despite the fact that every laboratory reports it in their result. Modification of Diet in Renal Disease (MDRD) is a formula that is used to estimate the GFR using serum creatinine. Serum cystatin C is a relatively new test and superior for assessing kidney functions. The purpose of this study was to show the correlation between MDRD and serum cystatin C. A cross-sectional study was conducted using secondary data from 260 subjects, aged 40–70 years old to assess serum creatinine and serum cystatin C at a private laboratory in West Jakarta, during the period of January 2013–September 2014. The result of the study showed that the mean age of subjects was 55.7+7.8 years old, the mean serum cystatin C level was 0.99 + 0.6 mg/L, and the mean MDRD was 80.24±28.1 mL/min/1.73 m2. Pearson correlation analysis showed a negative correlation between MDRD and serum cystatin C with r = -0.767 and p =0.001. In conclusion, higher MDRD presents lower cystatin C serum; hence, MDRD can be used as an alternative renal function test when serum cystatin C is not available. [