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All Journal CAUCHY: Jurnal Matematika Murni dan Aplikasi eProceedings of Engineering
Aina, Brilliant Friezka
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mathematics

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Enhancing Binary Classification Performance in Biomedical Datasets: Regularized ELM with SMOTE and Quantile Transforms Focused on Breast Cancer Analysis Aina, Brilliant Friezka; Kallista, Meta; Wibawa, Ig. Prasetya Dwi; Nugroho, Ginaldi Ari; Meiska, Ivana; Naf’an, Syifa Melinda
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 9, No 2 (2024): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Universitas Islam Negeri Maulana Malik Ibrahim Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/ca.v9i2.28785

Abstract

Using microarray datasets, this research investigation addresses the problem of unbalanced data in binary classification tasks. The objective is to increase classification performance by adding Extreme Learning Machine (ELM) regularization, as well as Synthetic Minority Over-sampling Technique (SMOTE) for data over-sampling and Quantile Transformer for data scaling. The study began with gathering important biological datasets from reputable sources such as UCI and Kaggle, including Pima Indian Diabetes, Heart Disease, and Wisconsin Breast Cancer. SMOTE was employed to solve the difficulty of data imbalance in the preparation of the dataset. The data was then separated into training (80%) and testing (20%) sets before being scaled using Quantile Transformation. To boost accuracy, ELMs were employed with an emphasis on introducing regularization techniques. Quantile Transforms are used to generate a Gaussian or uniform probability distribution from numerical input variables. Regularized ELM (R-ELM) surpasses ELM in terms of AUC, despite ELM's faster calculation time. The final selection of the regularization parameter (C) in R-ELM influences the model's performance and calculation time. Overall, R-ELM with SMOTE produces encouraging results when it comes to effectively categorizing biological dataset properties. A subsequent investigation and validation of additional datasets, however, are necessary to establish its generalizability and robustness.
Natural Disaster Monitoring Information System From Social Media Data Using Naïve Bayes Algorithm Aina, Brilliant Friezka; Kallista, Meta; Wibawa, Ig. Prasetya Dwi
eProceedings of Engineering Vol. 11 No. 3 (2024): Juni 2024
Publisher : eProceedings of Engineering

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Abstract

In Indonesia, there have been several naturaldisasters, such as earthquakes, tsunamis, landslides, floods, andothers. Because Indonesia is situated where the Eurasian,Pacific, and Indo-Australian plates converge, this potentialnatural disaster is caused by this location. Social mediainformation is expanding quickly and becoming more useful.Social media helps to alert people of the disaster's locationduring a disaster like a flood. Twitter is used as a data searchengine in this work. Twitter has been utilized effectively toupdate the public on current events during emergencies. Inorder to learn more, we can conduct a search using pertinenthashtags to determining for the incident's location. The test'sresults will show a map of the Indonesian region, and thedisaster's epicenter will be determined using the geolocationprovided by the tweet data. The Naive Bayes approach will beused for classification. The clustering process occurs in real timeacross every region of Indonesia. In this investigation, theaccuracy value was 75% based on the k-fold cross-validationtest, utilizing a fold value of 3. Keywords—Natural disasters, Twitter, Naïve Baiyes.
Co-Authors Ig. Prasetya Dwi Wibawa Meiska, Ivana Meta Kallista Naf’an, Syifa Melinda Nugroho, Ginaldi Ari