In compilation techniques, the processes and stages carried out relate to translating source languages into target languages (object programs). Source languages are high-level programming languages that are easy to understand and easy to learn by humans, while target languages are low-level languages that are only understood by machines. In this study a compiler machine called Automatic LESSIMIC Analyzer is used which can be used to analyze, including lexical, syntactic, and semantic analysis. Compiler machines that are designed can also synthesize intermediate code, using assembler codes. The compiler engine will produce an analysis of the program code that the user enters in the form of an error message, if the program code is not in accordance with the grammar that applies generally in programming languages. In this research, the simple program code that is inputted is C ++ programming language, and successfully analyzes the lexical, semantic, syntactic, intermediate code generation and successfully detects errors from the program code entered with a success rate of 99%.

Human Computer Interaction is the study how to design, evaluate, and implement computer systems interactive, the systems can make a dialogue with human. This research was designed and implementation leap motion used hand movements into digital models, which can be used to replace the function of the mouse or keyboard and can provide an alternative in the utilization of hand movements as interactions that the naturally between human and computers and allowing users to input gesture commands into an application in place of a mouse or keyboard. Human gesture recognition as technique interaction for deliver interpretation more natural and for communicating with computers. The aim of this research to find usability leap motion, how effectiveness of the Leap Motion controller for hand gesture recognition for solve important problems in human use of mouse or keyboard. Testing and implementation from this reseach focused are tracking, detection, dynamic, and statis gesture recognation. The result 6 gesture recognation double outward swipe, tap, swipe, clap, circular and fly control tap accuracy of gesture interrpretation data has obtained 87.62%. The uses the data for recognation model has obtain very good and testing with Leap Motion visualizer hand gesture recognation are often sensitive to poor resolution.

The following credit card records were used in this study of 284.807 transactions made by credit card holders in Europe for two days from the Kaggle dataset. This is a very poor data set, having 492 transactions, an imbalance of only 0.172% of the 284.807 transactions. The purpose of this study is to obtain the best model and then simulate it by electronically detecting unauthorized financial transactions in bank payment systems. The dataset for this study is unbalanced class data with 99.80% for the major class and 0.2% for the minor class. This type of class-imbalanced data problem is solved by applying method a combination of minority oversampling techniques using Synthetic Minority Oversampling Technique (SMOTE). To determine the most appropriate and accurate classification in solving class balance problems, comparisons were made with the Random Forest Classifier (RFC), Logistic Regression (LGR), and Gradient Boosting Classifier (GBC) algorithms. The test results in this study are the Random Forest Classifier (RFC) algorithm is better than other algorithms because it has the highest accuracy the percentage of data-train is 100% and data-test is 99.99% and the evaluation of the AUC score as a result of algorithm testing is 0.9999.