This research intends to investigate the effect of digital literacy skills and self-regulation on student creativity in online physics learning practices. The approach used in this research is quantitative, with the data collection technique being a survey model. The research design used a quasi-experimental model with a pretest and posttest design. The samples were physics education students of UIN Sulthan Thaha Saifuddin Jambi, with a total sample of 42 students. Data analysis was conducted through several stages, namely: a) the first stage was to analyze category classification, namely classifying student abilities into very good category (A), good category (B), sufficient category (C), and poor category (D), based on the score of the measurement results of the three variables; b) the second stage is to test the correlation between variables. Correlation data analysis was performed with SPSS 25 software. Based on the data and discussions that have been carried out, it can be concluded that there is an influence between digital literacy skills and self-regulation on students' creativity in online physics learning practices. The effect of digital literacy skills on student creativity in carrying out online physics learning practices is 85%, while self-regulation has an effect of 78%. Both variables together affect students' creativity in learning physics online by 74%.

Bean seed classification is critical in determining the quality of beans. Previously, the same dataset was tested using the MLP, SVM, KNN, and DT algorithms, with SVM producing the best results. The purpose of this study is to determine the most effective model through the use of the BoxCox transformation selection feature and the random forest (RF) algorithm, as well as the gradient boosting machine (GBM), light GBM, and repeated k-folds evaluation model. The bean dataset is available on the UCI Repository website. The BoxCox transformation and repeated k-folds improved the classification prediction's accuracy. The model is used in the optimal training phase for a random forest with decision tree parameters 50 and depth 10, a gradient boosting machine model with a learning rate of 1, and a light gradient boosting machine model with a learning rate of 0.5 and estimator of 500. The best training accuracy results are obtained with light GBM. which is 99 percent accurate, but only 91 percent accurate in terms of validation. According research, the Barbunya, Bombay, Cali, Dermason, Horoz, Seker, and Sira beans classes provided accuracy values of 91 percent, 100 percent, 92 percent, 92 percent, 95 percent, 94 percent, and 84 percent, respectively.

OpenVZ is a capsule-based virtualization technology for OS Linux which allows administrators to deploy multiple Operating Systems with different virtual hardware specifications, called containers, virtual environments, or Virtual Private Servers. In this paper, we propose new security architecture for OpenVZ depends on the type of attacks that commonly happen in servers. This security technic called OpenVzSec. The server is attacked by the client using OS windows and Ubuntu OS which is equipped with an attacker code program based on python language. Type of attacks used in this research: SSH vulnerability, SYN Flood attacks, Attack on the Rootkit vulnerability, and checksum spoofing. Some server attacks on a server and containers able to detected and anticipate by OpenVZSec. The OpenVZSec security model does not decrease performance of the server.