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All Journal JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)
Dyah Irawati, Indrarini
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Computer Science & IT Engineering

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Feature Selection Using Pearson Correlation for Ultra-Wideband Ranging Classification Indah Hapsari, Gita; Munadi, Rendy; Erfianto, Bayu; Dyah Irawati, Indrarini
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 2 (2025): April 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i2.6281

Abstract

Indoor positioning plays a crucial role in various applications, including smart homes, healthcare, robotics, and asset tracking. However, achieving high positioning accuracy in indoor environments remains a significant challenge due to obstacles that introduce NLOS conditions and multipath effects. These conditions cause signal attenuation, reflection, and interference, leading to decreased localization precision. This research addresses these challenges by optimizing feature selection LOS, NLOS, and multipath classification within Ultra-Wideband (UWB) ranging systems. A systematic feature selection approach based on Pearson correlation is employed to identify the most relevant features from an open-source dataset, ensuring efficient classification while minimizing computational complexity. The selected features are used to train multiple machine-learning classifiers, including Random Forest, Ridge Classifier, Gradient Boosting, K-Nearest Neighbor, and Logistic Regression. Experimental results demonstrate that the proposed feature selection method significantly reduces model training and testing times without compromising accuracy. The Random Forest and Gradient Boosting models exhibit superior performance, maintaining classification accuracy above 90%. The reduction in computational overhead makes the proposed approach highly suitable for real-time applications, particularly in edge-computing environments where processing efficiency is critical. These findings highlight the effectiveness of Pearson correlation-based feature selection in improving UWB-based indoor positioning systems. The optimized feature set facilitates robust LOS, NLOS, and multipath classification while reducing resource consumption, making it a promising solution for scalable and real-time indoor localization applications.
Analysis of Pneumonia from Chest X-Ray Images Using an Optimized Ensemble Machine Learning Models with Voting Classifier Monita, Vivi; Hanan Lutfianto, Naufal; Dyah Irawati, Indrarini
JOIV : International Journal on Informatics Visualization Vol 9, No 5 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.5.3689

Abstract

Pneumonia is a pulmonary disease resulting from infections caused by bacteria, viruses, or fungi that invade the lungs. This condition leads to inflammation due to the accumulation of fluids, blood cells, and other substances in the alveoli. Common symptoms experienced by patients include fever, coughing, and production of phlegm. Although pneumonia can affect individuals of any age, those with weakened immune systems are particularly vulnerable. Children and elderly individuals are especially prone to contracting this illness. The present research employs an ensemble learning approach for pneumonia detection using chest x-ray images to address this issue, specifically integrating support vector machines (SVMs) and random forests (RFs). The primary aim is to evaluate the effectiveness of ensemble learning through a voting classifier in improving pneumonia detection accuracy compared to individual machine learning models. The methodology includes preprocessing the data with contrast-limited adaptive histogram equalization (CLAHE), which minimizes noise by defining a kernel matrix and substituting each pixel's intensity with the weighted average of its neighboring pixels and itself. The research also involves training models using SVM and RF algorithms with hyperparameter optimization. These individual models are then assessed and compared using performance metrics such as accuracy, area under the curve (AUC), specificity, sensitivity, confusion matrix, and computational efficiency. By harnessing the strengths of ensemble learning, this research aims to contribute to the development of reliable pneumonia detection systems, with potential applications in clinical environments where timely and accurate diagnosis is essential for patient management. This research achieved 99.40% and 96.32% accuracy, 99.97% and 96.52% AUC, and 0.0436% and 0.0451% loss. This method tackles others that use deep learning and single machine learning with all balanced datasets.
Co-Authors Bayu Erfianto Gita Indah Hapsari Hanan Lutfianto, Naufal Rendy Munadi Vivi Monita