Skripsi

KLASIFIKASI SINYAL ELEKTROENSEFALOGRAM (EEG) UNTUK MENGENALI JENIS EMOSI MENGUNAKAN MACHINE LEARNING

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Penilaian

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Human emotion recognition can be done through facial expressions, voice, body posture, and physiological signals. However, these approaches tend to be subjective as individuals may consciously hide or manipulate their emotional expressions. As a more objective method, electroencephalogram signals can provide a more accurate understanding of a person's emotional state. In this regard, this research aims to develop a machine learning-based emotion classification model using electroencephalogram signals, which record brain electrical activity as a representation of emotional states. The dataset used consists of 2132 rows and 2549 features, including 2548 numerical data and one emotion label, namely positive, negative, and neutral. Three classification algorithms of Decision Tree, K-Nearest Neighbors, and Support Vector Machine are applied with data normalization method to improve the model performance. The experimental results show that Support Vector Machine with Quantile Transformer provides the highest accuracy of 99.76%, followed by Decision Tree 96.95% and K-Nearest Neighbors 96.01%. These findings suggest that machine learning approaches to EEG signals can be an effective method for human emotion classification.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2507003537	T175983	T1759832025	Central Library (Reference)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T1759832025

Publisher

Indralaya : Prodi Teknik Informatika, Fakultas Ilmu Komputer Universitas Sriwijaya., 2025

Collation

xiv, 129 hlm.; ilus.; tab.; 29 cm.

Language

Indonesia

ISBN/ISSN

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Classification

004.07

Content Type

Text

Media Type

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Carrier Type

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Edition

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Subject(s)

Teknik Informatika

Specific Detail Info

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Statement of Responsibility

MI

Other version/related

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