Results 51 to 60 of about 2,231 (201)

Komparasi metode SMOTE dan ADASYN untuk penanganan data tidak seimbang MultiClass [PDF]

, 2023
Data Mining is an activity that combines various branches of science into one, consisting of database systems, statistics, machine learning, and visualization, to analyze a large dataset in order to obtain useful data characteristics.
Putri, Sephia Dwi Arma, Pamuji, Fandi Yulian   +1 more
core   +1 more source

Stacking of DT, RF, and Gradient Boosting Algorithms for Classification of Building Damage Due to Earthquakes

Journal of Applied Informatics and Computing
Classification of building damage levels due to earthquakes is an important aspect in disaster mitigation and post-disaster risk assessment. This study aims to improve classification accuracy on imbalanced data using an ensemble stacking method.
Nur Aqliah Ilmi, Nurul Anisa Sri Winarsih   +1 more
doaj   +1 more source

Machine Learning‐Based Loan Approval Automation: Enhancing Efficiency, Accuracy and Fairness in Credit Decision‐Making

Expert Systems, Volume 43, Issue 7, July 2026.
ABSTRACT Traditional loan approval processes are manual, time‐consuming and susceptible to human bias. This research develops a machine learning‐based system to automate loan eligibility assessment while enhancing efficiency, accuracy and fairness in credit decision‐making. We developed and compared multiple supervised ML models—including Random Forest,
Mani Ghahremani, Hellen Amimo Otieno, Mishanil Kazreen, Stavros Shiaeles   +3 more
wiley   +1 more source

The Impact of Extreme Data Imbalance on Evaluation Metrics of Deep Learning Models for Loan Default Prediction

Emitor: Jurnal Teknik Elektro
The growth of financial technology has made online loans more accessible, but it has also increased the risk of borrowers failing to repay. Developing a reliable system to predict loan defaults is therefore very important.
Irfan Budiyanto, Arief Hermawan, Donny Avianto, Muhammad Kusban   +3 more
doaj   +1 more source

Rockburst prediction based on data preprocessing and hyperband‐RNN‐DNN

Deep Underground Science and Engineering, Volume 5, Issue 2, Page 446-464, June 2026.
A data preprocessing workflow is proposed to address challenges in rockburst data analysis. Coupled algorithms preprocess the data set, and hyperband optimization is used to enhance RNN performance. Results show that preprocessing improves accuracy, while dense layers enhance model stability and prediction performance.
Yong Fan, Chenxi Yin, Guangdong Yang, Shengyong Ding, Bin Tian   +4 more
wiley   +1 more source

Integration of Adasyn Method with Decision Tree Algorithm in Handling Imbalance Class for Loan Status Prediction

Jurnal Riset Informatika
Determining the provision of credit is generally carried out based on measuring credibility using credit analysis principles (5C principles). However, this method requires quite a long processing time and is very susceptible to subjective judgments which
Ami Rahmawati, Ita Yulianti, Tati Mardiana, Denny Pribadi   +3 more
doaj   +1 more source

A Novel Wireless Network Intrusion Detection Method Based on Adaptive Synthetic Sampling and an Improved Convolutional Neural Network

IEEE Access, 2020
The diversity of network attacks poses severe challenges to intrusion detection systems (IDSs). Traditional attack recognition methods usually adopt mining data associations to identify anomalies, which has the disadvantages of a high false alarm rate ...
Zhiquan Hu, Liejun Wang, Lei Qi, Yongming Li, Wenzhong Yang   +4 more
doaj   +1 more source

COVEN: Providing a Variety of Threshold‐Based Forecasts for the Outer Radiation Belt

Space Weather, Volume 24, Issue 6, June 2026.
Abstract We present a suite of VAMPIRE (Van Allen belt Multi‐day Predictions by Implementing a Random Forest for Electrons) models capable of predicting if the outer radiation belt crosses set percentile thresholds. We use Random Forest classification models to predict if the daily ∼2 MeV electron flux level across the outer radiation belt exceeds ...
D. J. Weston, I. J. Rae, A. W. Smith, C. E. J. Watt   +3 more
wiley   +1 more source

Blood cancer prediction using leukemia microarray gene data and hybrid logistic vector trees model

Scientific Reports, 2022
Blood cancer has been a growing concern during the last decade and requires early diagnosis to start proper treatment. The diagnosis process is costly and time-consuming involving medical experts and several tests. Thus, an automatic diagnosis system for
Vaibhav Rupapara, Furqan Rustam, Wajdi Aljedaani, Hina Fatima Shahzad, Ernesto Lee, Imran Ashraf   +5 more
doaj   +1 more source

Imbalance‐Aware Credit Card Fraud Detection Using Multi‐Autoencoders and Generative Ensemble Learning

Expert Systems, Volume 43, Issue 5, May 2026.
ABSTRACT Credit card fraud detection remains a challenging research problem due to the class imbalance issue caused by the rarity of fraudulent transactions. Classical oversampling techniques such as SMOTE, ADASYN and their variants help balance data but do not reflect the nonlinear structure of real‐world fraud, leading to poor generalization.
Sultan Alharbi, Khalid Alahmadi, Xianzhi Wang   +2 more
wiley   +1 more source