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All Journal Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Jurnal Komtika (Komputasi dan Informatika)
Rahmawati, Annida
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Health Professions Materials Science & Nanotechnology

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Acute effects of methadone on neural oscillations: an EEG study of theta, alpha, beta power, and frontal alpha asymmetry in opioid rehabilitation patients Nadiya, Ulfah; Simbolon, Artha Ivonita; Kusumandari, Dwi Esti; Rahmawati, Annida; Amri, M Faizal; Wibowo, Jony Winaryo; Danasasmita, Febrianti Santiardi; Sobana, Siti Aminah; Iskandar, Shelly; Turnip, Arjon
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 7 No. 2 (2025): May
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v7i2.64

Abstract

Methadone is a synthetic opioid that commonly employed in opioid substitution therapy (OST) to reduce withdrawal symptoms and suppress cravings in individuals with opioid use disorder. While its pharmacological effects are well-documented, the neurophysiological changes it induces especially during acute administration remain underexplored. This study aims to address that gap by investigating methadone-induced alterations in brain oscillatory activity through electroencephalography (EEG). Specifically, it examines changes in theta (4–8 Hz), alpha (8–12 Hz), and beta (12–30 Hz) frequency bands, along with frontal alpha asymmetry (FAA) for F4-F3 and F8-F7, a biomarker associated with emotional and cognitive processing. EEG data were collected from patients enrolled in opioid rehabilitation programs both prior to and one hour following oral methadone intake. The results revealed a significant global decrease in theta power, notably within the frontal, temporal, and occipital cortices. This reduction may reflect changes in executive functioning, emotional regulation, and increased sedation. In contrast, alpha power showed a marked increase, particularly in the central, parietal, and occipital regions, suggesting reduced sensory processing and heightened sedation or attentional disengagement. Meanwhile, beta power was consistently reduced across cortical regions, pointing toward decreased cortical arousal and cognitive alertness. FAA analysis revealed high variability among participants, indicating that methadone's influence on emotional valence and approach-avoidance behavior may differ significantly across individuals. These findings underscore methadone’s sedative and stabilizing effects on neural activity and support its clinical role in managing opioid dependence. Further research into inter-individual differences in EEG responses may inform more personalized and effective OST protocols.
Implementasi Support Vector Machine dan Resampling dalam Analisis Ulasan Pengguna Google Maps Khultsum, Umi; Rahmawati, Eka; Rahmawati, Annida; Annajib, Barra Rifki; Anggita, Christina Yuli
Jurnal Komtika (Komputasi dan Informatika) Vol 9 No 2 (2025)
Publisher : Universitas Muhammadiyah Magelang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31603/komtika.v9i2.14813

Abstract

The development of information technology has driven the increasing use of digital services such as Google Maps, which functions not only as a navigation tool but also as a platform for users to provide reviews. These reviews serve as an important data source for sentiment analysis; however, they are often unstructured and contain noise. This study aims to conduct sentiment analysis using the Support Vector Machine (SVM) model with the application of resampling techniques to address data imbalance issues in user reviews of the Google Maps application. A total of 1,000 recent reviews were collected through a scraping process, followed by data cleaning (lowercasing, stopwords removal, stemming, and lemmatization) and data preprocessing. The SVM model combined with resampling techniques was then implemented and evaluated using accuracy, precision, and recall metrics. The results indicate that the SVM model achieved an accuracy of 81%, with a weighted average precision of 0.79, recall of 0.81, and F1-score of 0.76. These findings demonstrate that applying resampling techniques to SVM yields good performance in sentiment classification. The study is expected to contribute to the development of sentiment analysis methods using the SVM model with resampling in the context of Google Maps reviews.
Co-Authors Amri, M Faizal Anggita, Christina Yuli Annajib, Barra Rifki Arjon Turnip Danasasmita, Febrianti Santiardi eka rahmawati Khultsum, Umi Kusumandari, Dwi Esti Nadiya, Ulfah Shelly Iskandar Simbolon, Artha Ivonita Siti Aminah Sobana Wibowo, Jony Winaryo