Credit card fraud is an issue that has affected Indonesia payment system over a decade. Sometimes, the result of the fraud used for terrorism and other crimes. Financial loss is not the only problem that is affected caused by credit card fraud but also Indonesia images in international trade, e-commerce, and the merchant. Currently, a trusted and secured banking payment system is crucial for both customers and banks. The problem from credit card fraud dataset is the data have many features and imbalanced class, this problem leads the paper to propose undersampling technique and feature reduction methods. In this paper we proposed two stage-feature reduction technique because a stage feature reduction could not find the optimal features. On the other hands, we are also applied Instance Hardness Threshold sampling and Random undersampling to deal with imbalance data. The two-stage feature reduction is chosen to eliminate the ineffective feature that cannot eliminate using only one feature reduction. The model from the implemented machine learning methods is evaluated using accuracy, specificity, recall, and Matthews Correlation Coefficient. We implemented our proposed approaches in the ULB credit card fraud detection dataset. According to the result, the undersampling gives a boost in performances which improve the recall and MCC score, the IHT undersampling provide goods results, and in some cases, the result can predict all the test set correctly. However, the two-stages feature reduction fails to improve the accuracy, precision, recall, and MCC score. In one case, the method reduced the accuracy score to 0.302.

The development of credit card use in Indonesia has not been matched by the security provided by credit card service providers. This resulted in significant losses both in terms of banking and customers. The difficulty in finding the characteristics of credit card fraud is one of the biggest challenges. Currently, many are developing machine learning models that can identify credit card fraud to help banks. Unfortunately, the model created is mostly biased towards the class which has more dominant data. This problem is caused by the imbalance of the data on the available dataset. In the previous research, Wang et al found the implementation of Focal loss in XGBoost improve the precision, recall of imbalanced data. However according to Qin et al, the parameter loss from Focal loss have poor judgement in several case of imbalanced data and to handle this weakness, they proposed Weighted -Cross Entropy Loss (W-CEL) loss in Focal loss. According to previous research, we propose a Modified Focal Loss method for Imbalanced XGBoost by entering another parameter from W-CEL loss to Focal Loss to improve the ability of Focal Loss. Focal loss itself is a method that is often used to give weight to classes that are often misinterpreted, so that with the use of imbalance parameters ( ) from W-CEL loss is expected to improve the weighted value of the Focal Loss. We tested our proposed method in credit card fraud dataset from Université Libre de Bruxelles (ULB) machine learning group. Our proposed method produced 100 % accuracy, 0.97 precision, 0.56 recall and 0.72 MCC score in scenario 1 with extreme imbalanced data, in scenario 2 with the mild imbalanced data, the result delivered 99% accuracy, 0.88 precision, 0.87 recall and 0.89 MCC and in scenario 3 with the medium imbalance data, the result delivered 100% accuracy, 0.97 precision score, 0.72 recall score and 0.83 MCC score. The results obtained in this study proved that the proposed method makes machine learning models valid and unbiased, especially in mild-imbalanced data. However, many improvements still need to be made to medium and extreme imbalanced data.

Scheduling lecture is scheduled number of components consisting of courses, lecturer, students, classrooms, and time with a number of restrictions and requirements (constraints) certain to get optimal results and the best. In this paper will be discussed and created scheduling lecture with a problem-solving approach to the science of Artificial Intelligence (Artificial Intelligence), by using an approximation of the mathematical problem that is aiming to find a situation or object that meets a number of requirements or specific criteria (Constraint Satisfaction Problem) to get the optimal scheduling and the best. To solve these problems the solution search techniques used by an algorithm that will result in optimal scheduling and the best (heuristic search) techniques combined with Smart Backtracking and Look Ahead called Intelligent Search to find and resolve problems when encountered a condition where no there is a solution in due course scheduling constraints and requirements are not met (deadlock). The application of these methods and techniques in the course scheduling information system is built, using the PHP programming language and MySQL database to solve the problem of scheduling to get optimal results and the best.

The scheduling of vehicle repair services carried out by most automotive companies is still ineffective and still uses a conventional process by calculating based on employee subjective without using a specific scheduling method. The problem that occurs in most automotive companies is that there is a delay in completing customer vehicle repair, so that it often passes the deadline for the customer's vehicle settlement agreement. The problem of delays occurs because they have not used a specific scheduling method so that there is no prioritized job determination. To solve this problem, a priority rule method is needed, namely by using the Earliest Due Date (EDD) method. The purpose of this research is to analyze and design a vehicle repair scheduling system using the Earliest Due Date (EDD) method, so that it will help minimize delays in the customer vehicle repair process. The data used is data from one of the automotive companies for the period of June 2020 with the stall parameter (the place for the vehicle being repaired) which is used for every day is always constant as much as 2 stalls. The results of the calculation of vehicle sample data using the Earliest Due Date (EDD) method show that the average delay (days) is significantly reduced from 4.17 days to 2.5 days, and the number of late jobs (days) is lower than 25 days to 15 days, with the number of late jobs getting lower, allowing the vehicle repair process to be on schedule.

All businesses, including car manufacturers, need to understand what aspects of their products are perceived as positive and negative based on user reviews so that they can make improvements for the negative aspects and maintain the already positive aspects of their products. One of the available tools for this task is Sentiment Analysis. The traditional document-level and sentence-level sentiment analysis will only classify each document / sentence into a class. This approach is incapable of finding the more fine-grained sentiment for a specific aspect of interest, for example, comfort, price, engine, paint, etc. Therefore, in this case, Aspect-based Sentiment Analysis is used. A total of 22.702 rows of car review data are scraped from the Edmunds website (www.edmunds.com) for a specific car manufacturer. Dependency Parsing and noun phrase extraction were carried out using the SpaCy module in Python, and VADER sentiment analysis was used to determine the polarity of the sentiment for each noun phrase. Results showed that the vast majority of the sentiments are on the positive aspects: comfortable to drive, good fuel economy / mileage, reliability, spaciousness, value for money, helpful rear camera, quiet ride, good acceleration, well-designed, good sound system, and solid build. The results for the negative aspects have some similar aspects with those in the positive class but has a very low frequency. This finding means that the vast majority of the users are satisfied with multiple aspects of the produced cars. The limitation of this research and future research direction are discussed.