Melola Discovery

← Melola Discovery

Akses terbuka Jurnal SINTA 1

Alcoholism Classification Based on EEG Spectrograms Using Hybrid Convolutional Neural Network-Machine Learning Models

Journal of Robotics and Control (JRC) · Vol. 6 No. 6 (2025) · hlm. 2760–2773

DOI
10.18196/jrc.v6i6.28220
ISSN
2715-5072, 2715-5056
Terbit
2025-11-24
Penerbit
Universitas Muhammadiyah Yogyakarta
Bahasa
eng
Lisensi
creativecommons.org/licenses/by-sa/4.0

Abstrak

Alcoholism has a significant impact on brain activity, making early detection essential for effective intervention. Conventional diagnostic methods are often subjective and time-consuming, creating a need for automated approaches. This research presents a hybrid framework that integrates CNN for extracting features from EEG spectrograms with various machine learning classifiers to enhance classification accuracy. Its main contribution is the creation of a CNN–Machine Learning pipeline that merges deep feature representations with conventional classifiers such as SVM, KNN, RF, NN, SGD, DT, NB, and QDA for detecting alcoholism based on EEG data. This approach utilizes CNN’s ability to capture complex spatiotemporal patterns while systematically comparing different algorithms on the same extracted features. A total of 7,200 EEG spectrogram images were preprocessed through resizing, normalization, and labeling. CNN was used to extract 512-dimensional feature vectors, which were then classified using the selected machine learning models. Model performance was assessed through accuracy, precision, recall, F1-score, specificity, and ROC-AUC to ensure a thorough evaluation. Findings revealed that the CNN–SVM model delivered the highest results, attaining 82.41% accuracy, 78.04% precision, 90.21% recall, an F1-score of 83.69%, 74.62% specificity, and a ROC-AUC of 90.77%. QDA followed with the highest recall at 91.72% and a ROC-AUC of 89.77%, while RF delivered competitive stability. NN, SGD, and NB recorded lower metrics, showing limited ability to leverage CNN features. In conclusion, the hybrid CNN–Machine Learning framework effectively classifies alcoholic and non-alcoholic EEG spectrograms, demonstrating strong potential as an automated, accurate, and scalable tool for clinical screening.

Kata kunci

Jaringan kolaborasi para penulis

23 penulis · 84 tautan kepenulisan bersama · 57 lintas organisasi

Arahkan kursor atau ketuk sebuah nama untuk menyorot rekan penulisnya. Klik dua kali untuk membuka halaman penulis.

  • Institut Teknologi Sepuluh Nopember
  • Rajamangala University of Technology
  • Universiti of Malaysia Sabah
  • Hallym University
  • Guizhou University of Engineering Science
  • University of Newcastle Australia
  • Norwegian University of Science and Technology
  • Organisasi berbeda
  • Organisasi sama
  • Afiliasi tidak diketahui
  • Garis lebih tebal = lebih banyak artikel bersama · lingkaran lebih besar = lebih banyak artikel
Tampilkan sebagai tabel
Rekan penulisOrganisasiArtikel bersama
Yamin ThweRajamangala University of Technology5
M. N. A. UdaUniversiti of Malaysia Sabah3
Padma Nyoman CrisnapatiRajamangala University of Technology3
U. HashimUniversiti of Malaysia Sabah2
Stralen PratasikInstitut Teknologi Sepuluh Nopember2
Gao YulanGuizhou University of Engineering Science2
Abdul KarimHallym University2
Abdul KarimHallym University2
Abdul KarimHallym University1
Tegar Anugrah FirdausInstitut Teknologi Sepuluh Nopember1
Bryan RaisInstitut Teknologi Sepuluh Nopember1
Marcelinus Jonathan SalimInstitut Teknologi Sepuluh Nopember1
Andhika Rastra NegaraInstitut Teknologi Sepuluh Nopember1
Muhammad Qaiser Zaydan ZiaInstitut Teknologi Sepuluh Nopember1
Made KrisnandaUniversity of Newcastle Australia1
Diah Puspito WulandariInstitut Teknologi Sepuluh Nopember1
Adhi Dharma WibawaInstitut Teknologi Sepuluh Nopember1
Natan DerekNorwegian University of Science and Technology1
Yuri PamungkasInstitut Teknologi Sepuluh Nopember1
Uda HashimUniversiti of Malaysia Sabah1

Afiliasi berasal dari OpenAlex; metadata OAI-PMH jurnal tidak memuatnya. Penulis tanpa ID OpenAlex dicocokkan berdasarkan nama, sehingga dua orang bisa tergabung atau satu orang bisa terpisah.

Sumber metadata: OAI-PMH (metadata jurnal) https://journal.umy.ac.id/index.php/jrc/oai, pertama di-harvest 9 Nov 2026, terakhir terlihat 9 Nov 2026.
Metadata asli (oai_dc) 
<oai_dc:dc xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/  http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
	<dc:title xml:lang="en-US">Alcoholism Classification Based on EEG Spectrograms Using Hybrid Convolutional Neural Network-Machine Learning Models</dc:title>
	<dc:creator>Pamungkas, Yuri</dc:creator>
	<dc:creator>Aung, Myo Min</dc:creator>
	<dc:creator>Yulan, Gao</dc:creator>
	<dc:subject xml:lang="en-US">EEG Spectrogram</dc:subject>
	<dc:subject xml:lang="en-US">Alcoholism Detection</dc:subject>
	<dc:subject xml:lang="en-US">CNN</dc:subject>
	<dc:subject xml:lang="en-US">Machine Learning</dc:subject>
	<dc:subject xml:lang="en-US">Hybrid Models</dc:subject>
	<dc:description xml:lang="en-US">Alcoholism has a significant impact on brain activity, making early detection essential for effective intervention. Conventional diagnostic methods are often subjective and time-consuming, creating a need for automated approaches. This research presents a hybrid framework that integrates CNN for extracting features from EEG spectrograms with various machine learning classifiers to enhance classification accuracy. Its main contribution is the creation of a CNN–Machine Learning pipeline that merges deep feature representations with conventional classifiers such as SVM, KNN, RF, NN, SGD, DT, NB, and QDA for detecting alcoholism based on EEG data. This approach utilizes CNN’s ability to capture complex spatiotemporal patterns while systematically comparing different algorithms on the same extracted features. A total of 7,200 EEG spectrogram images were preprocessed through resizing, normalization, and labeling. CNN was used to extract 512-dimensional feature vectors, which were then classified using the selected machine learning models. Model performance was assessed through accuracy, precision, recall, F1-score, specificity, and ROC-AUC to ensure a thorough evaluation. Findings revealed that the CNN–SVM model delivered the highest results, attaining 82.41% accuracy, 78.04% precision, 90.21% recall, an F1-score of 83.69%, 74.62% specificity, and a ROC-AUC of 90.77%. QDA followed with the highest recall at 91.72% and a ROC-AUC of 89.77%, while RF delivered competitive stability. NN, SGD, and NB recorded lower metrics, showing limited ability to leverage CNN features. In conclusion, the hybrid CNN–Machine Learning framework effectively classifies alcoholic and non-alcoholic EEG spectrograms, demonstrating strong potential as an automated, accurate, and scalable tool for clinical screening.</dc:description>
	<dc:publisher xml:lang="en-US">Universitas Muhammadiyah Yogyakarta</dc:publisher>
	<dc:date>2025-11-24</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.umy.ac.id/index.php/jrc/article/view/28220</dc:identifier>
	<dc:identifier>10.18196/jrc.v6i6.28220</dc:identifier>
	<dc:source xml:lang="en-US">Journal of Robotics and Control (JRC); Vol. 6 No. 6 (2025); 2760-2773</dc:source>
	<dc:source>2715-5072</dc:source>
	<dc:source>2715-5056</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.umy.ac.id/index.php/jrc/article/view/28220/12033</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2025 Yuri Pamungkas, Myo Min Aung, Gao Yulan</dc:rights>
	<dc:rights xml:lang="en-US">https://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>