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All Journal Scientific Journal of Informatics
Fauzan Firdaus
Universitas Ibrahimy
Computer Science & IT

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PERBANDINGAN ALGORITMA K-NEAREST NEIGHBOUR DAN NAÏVE BAYES UNTUK MENDETEKSI PENIPUAN KARTU KREDIT Fauzan Firdaus; Ahmad Homaidi; Jarot Dwi Prasetyo; Hermanto Hermanto; Ach. Zubairi; Lukman Fakih Lidimilah
Jurnal Ilmiah Informatika Vol. 10 No. 2 (2025): Jurnal Ilmiah Informatika
Publisher : Department of Science and Technology Ibrahimy University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35316/.v10i2.8992

Abstract

Credit card fraud is a serious problem in the financial industry that continues to increase with the development of digital transaction technology. This study aims to compare the performance of the K-Nearest Neighbour (KNN) and Naive Bayes algorithms in detecting credit card fraud by considering various evaluation metrics evaluation metrics, including not only accuracy but also precision, recall, and F1-score. The dataset used was sourced from Kaggle, comprising a total of 10,000 transaction records, which included financial transaction attributes and user behaviour. The research process included data pre-processing, attribute selection, data normalisation, and the application of both algorithms using RapidMiner software. The test results showed that the KNN algorithm produced an accuracy of 98.43%, a precision of 98.53%, and a recall of 99.90%, while Naive Bayes obtained an accuracy 98.20% accuracy, 99.69% precision, and 98.48% recall. Although KNN showed slightly superior performance in detecting fraudulent transactions, the T-Test statistical test showed that the difference in performance between the two algorithms was not statistically significant. KNN has an advantage in recognising complex patterns, but requires greater computational time, while Naive Bayes is more efficient in terms of speed. This study concludes that the selection of a fraud detection algorithm needs to consider the trade-off between accuracy and computational efficiency according to system requirements.
KLASIFIKASI MUTIARA BANGSRING UNDERWATER BERDASARKAN CACAT LUBANG MENGGUNAKAN ARTIFICIAL NEURAL NETWORK BERBASIS SVM-RFE Fauzan Firdaus; A. Hamdani
Jurnal Ilmiah Informatika Vol. 11 No. 1 (2026): Jurnal Ilmiah Informatika
Publisher : Department of Science and Technology Ibrahimy University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35316/.v11i1.10380

Abstract

Pearls are highly valued gemstones, where quality is significantly determined by surface integrity, particularly the absence of hole defects. Traditional manual inspection is subjective and inefficient, creating a demand for automated, high-precision detection systems. This research develops an automated framework to identify hole-based defects in pearls sourced from the Bangsring Underwater region using digital image processing and machine learning.  The methodology begins with preprocessing and thresholding to isolate pearl objects, followed by texture feature extraction using the Gray Level Co-Occurrence Matrix (GLCM). To handle high-dimensional data, this study utilizes Support Vector Machine - Recursive Feature Elimination (SVM-RFE) for feature selection, while classification is executed using an Artificial Neural Network (ANN).  The experimental dataset consists of 240 high-resolution pearl images (120 with hole defects and 120 defect-free), evaluated across training cycles ranging from 100 to 1000. Results show that the stand-alone ANN achieved a peak accuracy of 95.83% at 1000 cycles, whereas integrating SVM-RFE enhanced performance to 97.62% at 900 and 1000 cycles. These findings confirm that the combined SVM-RFE and ANN framework provides a robust solution for automated defect detection, offering a scalable approach for industrial quality inspection in the Bangsring Underwater region.
Co-Authors A. Hamdani Ach. Zubairi Ahmad Homaidi Hermanto Hermanto Jarot Dwi Prasetyo Lukman Fakih Lidimilah