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All Journal Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Buletin Ilmiah Sarjana Teknik Elektro
Phann, Raksmey
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Computer Science & IT Electrical & Electronics Engineering

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HNIHA: Hybrid Nature-Inspired Imbalance Handling Algorithm to Addressing Imbalanced Datasets for Improved Classification: In Case of Anemia Identification Saputra, Dimas Chaerul Ekty; Ratnaningsih, Tri; Futri, Irianna; Muryadi, Elvaro Islami; Phann, Raksmey; Tun, Su Sandi Hla; Caibigan, Ritchie Natuan
Buletin Ilmiah Sarjana Teknik Elektro Vol. 6 No. 3 (2024): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v6i3.11306

Abstract

This study presents a comprehensive evaluation of three ensemble models designed to handle imbalanced datasets. Each model incorporates the hybrid nature-inspired imbalance handling algorithm (HNIHA) with matthews correlation coefficient and synthetic minority oversampling technique in conjunction with different base classifiers: support vector machine, random forest, and LightGBM. Our focus is to address the challenges posed by imbalanced datasets, emphasizing the balance between sensitivity and specificity. The HNIHA algorithm-guided support vector machine ensemble demonstrated superior performance, achieving an impressive matthews correlation coefficient of 0.8739, showcasing its robustness in balancing true positives and true negatives. The f1-score, precision, and recall metrics further validated its accuracy, precision, and sensitivity, attaining values of 0.9767, 0.9545, and 1.0, respectively. The ensemble demonstrated its ability to minimize prediction errors by minimizing the mean squared error and root mean squared error to 0.0384 and 0.1961, respectively. The HNIHA-guided random forest ensemble and HNIHA-guided LightGBM ensemble also exhibited strong performances.
An Innovative Artificial Intelligence-Based Extreme Learning Machine Based on Random Forest Classifier for Diagnosed Diabetes Mellitus Saputra, Dimas Chaerul Ekty; Muryadi, Elvaro Islami; Phann, Raksmey; Futri, Irianna; Lismawati, Lismawati
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 10 No. 1 (2024): March
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v10i1.28690

Abstract

Since 2014, the World Health Organization has accumulated data indicating that 8.5% of 18-year-olds and older have been diagnosed with diabetes. In 2019, diabetes caused the lives of 1.5 million people worldwide, with those under the age of 70 accounting for 48% of all diabetes-related deaths. It is estimated that diabetes causes an additional 460,000 deaths each year due to renal failure and that hyperglycemia contributes to about 20% of all cardiovascular disease-related deaths. Diabetes may have contributed to a 3% rise in the age-adjusted death rate between the years 2000 and 2019. In recent years, the fatality rate attributable to diabetes has increased by 13% in low- and middle-income countries. Statistics collected by the World Health Organization indicate that the number of persons diagnosed with diabetes has increased from 108 million in 1980 to 422 million in 2014. The objective of this study is to construct a model capable of diagnosing persons with diabetes reliably, correctly, and consistently. This research used secondary data offered by Kaggle. The original data came from the National Institute of Diabetes and Digestive and Kidney Diseases. Each of the up to 768 data points consists of nine characteristics and two outputs, such as diabetes and non-diabetes in the provided example. In this study, a single algorithm is constructed by integrating two separate algorithms. Random forest algorithms, which are based on machine learning, and extreme learning machines, which are based on deep learning, have generated extraordinarily accurate results. When the confusion matrix is used, 98.05% accuracy is attained. Therefore, it is feasible to conclude that the suggested method was successful in completing an adequate analysis and classifying the data.
Co-Authors Caibigan, Ritchie Natuan Dimas Chaerul Ekty Saputra Futri, Irianna Lismawati, Lismawati Muryadi, Elvaro Islami Tri Ratnaningsih Tun, Su Sandi Hla