Skripsi

PERBANDINGAN ALGORITMA XGBOOST, LIGHTGBM DAN RANDOM FOREST UNTUK KLASIFIKASI SINYAL EEG DALAM PENGENALAN EMOSI.

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Emotion recognition based on Electroencephalogram (EEG) signals has become an intriguing research field in human-computer interaction and medical applications with the potential to provide deep insights into patients' mental conditions. This study aimed to compare the performance of three machine learning algorithms, XGBoost, LightGBM, and Random Forest, in classifying EEG signals for emotion recognition. The research was conducted by testing these algorithms using parameter variations such as n_estimators, learning_rate, and max_depth, and different data splits. The evaluation method involved performance metrics including accuracy, precision, recall, and F1-score. Results showed that all three algorithms achieved maximum accuracy of 1.00, with unique characteristics: XGBoost excelled in performance stability, LightGBM stood out in computational efficiency, and Random Forest displayed balanced evaluation metrics. This research contributes to the development of EEG signal classification methods for emotion recognition using machine learning approaches, with recommendations for algorithm selection based on specific research needs while considering potential overfitting in large training datasets.

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Availability

Detail Information

Series Title

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Call Number

T1627472024

Publisher

Indralaya : Prodi Teknik Informatika, Fakultas Ilmu Komputer Universitas Sriwijaya., 2024

Collation

xix, 145 hlm.; ilus, 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

004.015 107

Content Type

Text

Media Type

unmediated

Carrier Type

other (computer)

Edition

-

Subject(s)

Prodi Teknik Informatika
Prinsip Matematika dalam Ilmu Komputer

Specific Detail Info

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Statement of Responsibility

MURZ

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