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All Journal PROCEEDING IC-ITECHS 2014 Journal of Data Analytics, Information, and Computer Science (JDAICS)
Maharani, Desy Khalida
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Computer Science & IT Decision Sciences, Operations Research & Management Education Mathematics Other

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PERFORMANCE COMPARISON OF MACHINE LEARNING MODELS TO REDUCE MISDIAGNOSIS RATES IN PSYCHIATRIC DISORDER USING EEG DATASET Munada, Wina; Maharani, Desy Khalida; Sofiana, Anis
Journal of Data Analytics, Information, and Computer Science Vol. 1 No. 3 (2024): Juli
Publisher : Yayasan Nuraini Ibrahim Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59407/jdaics.v1i3.967

Abstract

This paper aims at comparing the suitability of three machine learning models: LightGBM, CatBoost, and Logistic Regression, to lower misdiagnosis rates for psychiatric disorders. Misdiagnosis in mental health may mean improper treatment and, hence, poor outcomes for patients. Our research aims to determine the most accurate predictive model for mental health condition diagnosis that will lead to improved clinical outcomes. We trained and tested these models on an EEG dataset with patient records that have psychiatric diagnoses labeled. For all the models, evaluation and comparison are made using key performance metrics such as Accuracy, Precision, Recall, and F1-Score. Through the use of these methods, it was shown that LightGBM performed better than CatBoost and Logistic Regression, having achieved higher accuracy and F1 scores, indicating more power to make a difference among different psychiatric disorders. These results suggest that machine learning techniques, especially LightGBM, can greatly increase diagnostic accuracy and reduce misdiagnosis in psychiatric contextual systems. Keywords Machine Learning, Psychiatric Disorder, LightGBM, CatBoost, Logistic Regression.
Intelligent Waste Segregation System Using Convolutional Neural Networks for Deep Learning Applications Rahmawati, Siti Solehah Yunita; Maharani, Desy Khalida; Munada, Wina
IC-ITECHS Vol 5 No 1 (2024): IC-ITECHS
Publisher : LPPM STIKI Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/ic-itechs.v5i1.1579

Abstract

Efficient waste management is essential for environmental sustainability and reducing landfill burdens. This study proposes an Intelligent Waste Segregation System leveraging Convolutional Neural Networks (CNNs), specifically the VGG-16 model, to automate the classification of waste into recyclable and non-recyclable categories. The purpose of this research is to enhance waste sorting accuracy and efficiency using advanced deep learning techniques. The system employs VGG-16, pre-trained on a large dataset, and fine-tuned with a waste image dataset, enabling high precision in recognizing waste types. The methodology includes dataset preprocessing, model training, and performance evaluation using metrics such as accuracy, precision, and recall. Experimental results demonstrate that the proposed system achieves a classification accuracy of 96%, surpassing existing traditional methods. The implications of this research include improving recycling processes and reducing environmental pollution through accurate waste segregation. This system has practical applications in urban waste management and recycling facilities, providing a scalable solution to global waste challenges. The findings highlight the potential of CNN-based models, particularly VGG-16, in addressing critical environmental issues. In conclusion, the proposed system offers an effective approach to automated waste segregation, paving the way for sustainable waste management practices through deep learning applications.
Co-Authors Anis Sofiana Munada, Wina Rahmawati, Siti Solehah Yunita