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All Journal The Indonesian Journal of Computer Science
Ashqi Saeed, Vaman
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Computer Science & IT Electrical & Electronics Engineering Engineering

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Credit Card Fraud Detection using KNN, Random Forest and Logistic Regression Algorithms : A Comparative Analysis Ashqi Saeed, Vaman; Abdulazeez, Adnan Mohsin
The Indonesian Journal of Computer Science Vol. 13 No. 1 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i1.3707

Abstract

Because credit cards are utilized so frequently, fraud appears to be a significant concern in the credit card industry. It is challenging to quantify the effects of misrepresentation. Globally, credit card fraud has cost institutions and consumers billions of dollars. Despite the existence of numerous anti-fraud mechanisms, fraudsters continue to seek out novel methods and strategies to commit fraud. An additional challenge in the estimation of credit card fraud loss is that the magnitude of unreported or undetected forgeries cannot be determined, only losses associated with those frauds that have been detected can be measured. Implementing effective fraud detection algorithms through the utilization of machine-learning techniques is crucial in order to mitigate these losses and provide support to fraud investigators. This paper presents a machine learning-based method for the detection of credit card fraud. Three methodologies are implemented on the raw and pre-processed data. Python is used to implement the work. By comparing the accuracy-based performance evaluations of k-nearest neighbor and logistic regression with Random Forest, it is determined that the former exhibits superior performance.
Enhancing AdaBoost Performance: Comparative Analysis of CPU Parallel Processing on Breast Cancer Classification Ashqi Saeed, Vaman; Zeebaree, Subhi R. M.
The Indonesian Journal of Computer Science Vol. 13 No. 2 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i2.3793

Abstract

The implementation of time-sharing across processes in a real-time way has the potential to increase the execution efficiency of multiprocessor systems like the one described above. The system is able to carry out tasks that make use of a large number of processors in an effective way as a result of this. The aim of this research is to design a system with two primary goals: to enhance accuracy and to minimise the amount of time necessary with processing. This will be accomplished by integrating the ADABoost model with the decision tree algorithm. Furthermore, the statistics unambiguously demonstrate that the accuracy remains the same regardless of whether or not the central processing unit (CPU) makes use of parallel processing, which suggests that there is no variation in parallelization. As a consequence of this, there is a direct connection between the amount of time that is spent and an increase in the amount of parallel processing that is carried out by the central processing unit pertaining to the breast cancer dataset that is being investigated. This research was carried out using Python, which was the programming language that was used for the coding technique that was carried out during the course of its execution.
Co-Authors Abdulazeez, Adnan Mohsin Zeebaree, Subhi R. M.