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All Journal CAUCHY: Jurnal Matematika Murni dan Aplikasi eProceedings of Engineering
Meiska, Ivana
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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.
Pengembangan Sistem Klasifikasi Kualitas Air Minum Berbasis Web Menggunakan Algoritma K-Nearest Neighbors Meiska, Ivana; 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

Air memiliki peran penting sebagai kebutuhanprimer dalam kehidupan manusia, termasuk untuk konsumsi.Namun, sayangnya air mudah terkontaminasi sehingga dapatmembahayakan kesehatan tubuh. Oleh karena itu, penentuankelayakan air minum dengan metode manual seperti STORETdan Indeks Pencemaran memakan waktu lama dan biaya yangtinggi. Untuk mengatasi hal ini, penerapan machine learningdengan algoritma K-Nearest Neighbors dan teknik SMOTEuntuk mengatasi ketidakseimbangan pada kelas target menjadipilihan yang efisien dan tepat. Hasil penelitian menunjukkanbahwa model K-Nearest Neighbors dengan k=3 mampumencapai akurasi training sebesar 0.98928, akurasi testingsebesar 0.99434, serta ROC AUC mencapai 1.00 dengan losshanya 0.38618. Model yang optimal akan divisualisasikanhasilnya menggunakan Streamlit ssebagai alat untukmenyajikan informasi secara interaktif, memungkinkanpengguna untuk dengan mudah memahami dan menganalisiskualitas air minum. Kata kunci—Kelayakan Air Minum, K-Nearest Neighbors,Machine Learning, SMOTE, Streamlit
Co-Authors Aina, Brilliant Friezka Ig. Prasetya Dwi Wibawa Meta Kallista Naf’an, Syifa Melinda Nugroho, Ginaldi Ari Wibawa, Ig.Prasetya Dwi