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All Journal Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI)
Fatma Indriani
Lambung Mangkurat University
Computer Science & IT Electrical & Electronics Engineering

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Prediction of Post-Operative Survival Expectancy in Thoracic Lung Cancer Surgery Using Extreme Learning Machine and SMOTE Ajwa Helisa; Triando Hamonangan Saragih; Irwan Budiman; Fatma Indriani; Dwi Kartini
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol 9, No 2 (2023): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i2.25973

Abstract

Lung cancer is the most common cause of cancer death globally. Thoracic surgery is a common treatment for patients with lung cancer. However, there are many risks and postoperative complications leading to death. In this study, we will predict life expectancy for lung cancer patients one year after thoracic surgery The data used is secondary data for lung cancer patients in 2007-2011. There are 470 data consisting of 70 death class data and 400 survival class data for one year after surgery. The algorithm used is Extreme learning machine (ELM) for classification, which tends to be fast in the learning process and has good generalization performance. Synthetic Minority Over-sampling (SMOTE) is used to solve the problem of imbalanced data. The proposed solution combines the benefits of using SMOTE for imbalanced data along with ELM. The results show ELM and SMOTE outperform other algorithms such as Naïve Bayes, Decision stump, J48, and Random Forest. The best results on ELM were obtained at 50 neurons with 89.1% accuracy, F-Measure 0.86, and ROC 0.794. In the combination of ELM and SMOTE, the accuracy is 85.22%, F-measure 0.864, and ROC 0.855 on neuron 45 using a data division proportion of 90:10. The test results show that the proposed method can significantly improve the performance of the ELM algorithm in overcoming class imbalance. The contribution of this study is to build a machine learning model with good performance so that it can be a support system for medical informatics experts and doctors in early detection to predict the life expectancy of lung cancer patients.
Automated Detection of COVID-19 Cough Sound using Mel-Spectrogram Images and Convolutional Neural Network Muhammad Fauzan Nafiz; Dwi Kartini; Mohammad Reza Faisal; Fatma Indriani; Triando Hamonangan Saragih
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol 9, No 3 (2023): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i3.26374

Abstract

COVID-19 disease is known as a new disease caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) variant. The initial symptoms of the disease commonly include fever (83-98%), fatigue or myalgia, dry cough (76-82%), and shortness of breath (31-55%). Given the prevalence of coughing as a symptom, artificial intelligence has been employed as a means of detecting COVID-19 based on cough sounds. This study aims to compare the performance of six different Convolutional Neural Network (CNN) models (VGG-16, VGG-19, LeNet-5, AlexNet, ResNet-50, and ResNet-152) in detecting COVID-19 using mel-spectrogram images derived from cough sounds. The training and validation of these CNN models were conducted using the Virufy dataset. Audio data was processed to generate mel-spectrogram images, which were subsequently employed as inputs for the CNN models. The AlexNet model, utilizing an input size of 227x227, exhibited the best performance with the highest Area Under the Curve (AUC) value of 0.930303. This study provides compelling evidence of the efficacy of CNN models in detecting COVID-19 based on cough sounds through the utilization of mel-spectrogram images. Furthermore, the study underscores the impact of input size on model performance. The primary contribution of this research lies in identifying the CNN model that demonstrates the best performance in COVID-19 detection based on cough sounds. Additionally, this study establishes the fundamental groundwork for selecting an appropriate CNN methodology for early detection of COVID-19.
Co-Authors Ajwa Helisa Dwi Kartini, Dwi Irwan Budiman Muhammad Fauzan Nafiz Muhammad Reza Faisal, Muhammad Reza Saragih, Triando Hamonangan