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All Journal JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Journal of Artificial Intelligence and Engineering Applications (JAIEA)
Surianto, Stacyana Jesika
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Automotive Engineering Computer Science & IT Control & Systems Engineering

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Implementasi Algoritma Naive Bayes dalam Meningkatkan Akurasi Diagnosa Penyakit Tumor Otak Surianto, Stacyana Jesika; Putra, Samuel Anaya; Ananta, Willy Pramudia; Sitorus, Rizki Risdah; Ramadhani, Fanny
JATISI Vol 11 No 3 (2024): JATISI (Jurnal Teknik Informatika dan Sistem Informasi)
Publisher : Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/jatisi.v11i3.8113

Abstract

A brain tumor is an abnormal growth of cells in the brain that often requires an accurate diagnosis from a radiologist. This study aims to implement the Naive Bayes algorithm in improving the accuracy of brain tumor diagnosis. Naive Bayes is a popular classification algorithm in data mining that can provide accurate results even with limited datasets. The study used a dataset of MRI images of brain tumors from Kaggle consisting of 2044 image samples with three classes: meningioma tumors, pituitary tumors, and no tumors. The process starts with image preprocessing, then feature extraction using Local Binary Pattern (LBP), and classification using Naive Bayes algorithm. The test results showed the best parameters of LBP were radius 1 and neighborliness 8, while the Naive Bayes model achieved 68% accuracy, 67% precision, and 66% recall in classifying all three classes of brain tumors. The study expands knowledge of the potential of the Naive Bayes algorithm in brain tumor diagnosis and may form the basis for further research.
Comparison of Logistic Regression and XGBoost Model Performance in Predicting Credit Scores Surianto, Stacyana Jesika; Chairunisah
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 2 (2026): February 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i2.1877

Abstract

Credit Scoring is a mathematical approach used to assess the creditworthiness of individuals or companies by classifying debtors into certain categories based on their risk profiles. This study aims to compare the performance of the Logistic Regression and XGBoost machine learning algorithms in predicting credit scores (credit scoring) to reduce the risk of Non-Performing Loan (NPL) risk at PT Graha Mazindo Mandiri. The secondary dataset used contains 1,533 car loan debtor data with 17 variables, including 1dependent variable and 16 independent variables. The research process includes data preprocessing (cleaning, handling outliers, encoding, normalization, and class balancing with SMOTE), modeling, and evaluation using the Accuracy, Precision, Recall, F1-score, and ROC-AUC metrics. The results show that XGBoost excels with 96% accuracy and ROC-AUC of 0.99 compared to Logistic Regression with an accuracy of 88% and ROC-AUC0.94, due to XGBoost ability to capture non-linear patterns and handle data imbalance. This study provides insights into credit risk factors and supports more accurate credit decision-making, with recommendations for hyperparameter optimization and model integration into operational systems.
Co-Authors Ananta, Willy Pramudia Chairunisah Putra, Samuel Anaya Ramadhani, Fanny Sitorus, Rizki Risdah