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All Journal SEMNASTERA (Seminar Nasional Teknologi dan Riset Terapan) Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics
Wibowo, Jony Winaryo
Unknown Affiliation
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Health Professions Materials Science & Nanotechnology Mechanical Engineering

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PELUANG DAN TANTANGAN TEKNOLOGI ARTIFICIAL INTELLIGENCE (AI) PADA PROSES ROASTING BIJI KOPI Wibowo, Jony Winaryo; Muhaemin, Mimin; Fakhrurroja, Hanif
SEMNASTERA (Seminar Nasional Teknologi dan Riset Terapan) Vol 6 (2024)
Publisher : SEMNASTERA (Seminar Nasional Teknologi dan Riset Terapan)

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Abstract

Kopi adalah salah satu minuman paling umum dan favorit yang dikonsumsi banyak orang di seluruh dunia. Proses roasting kopi memegang peranan penting dalam menentukan rasa kopi. Tahapan dalam proses roasting kopi terdiri dari pengeringan, penguningan, pecahan pertama, roast development, dan pecahan kedua. Dari kelima tahapan tersebut, proses pecahan pertama kopi merupakan awal mula terbentuknya karakteristik biji kopi. Pada tahap ini, seorang penyangrai biji kopi profesional harus memastikan suhu dan waktu yang sesuai agar biji kopi tidak hangus/gosong. Pada alat roasting kopi manual, hal ini menimbulkan ketidakonsistenan hasil roasting biji kopi karena sangat bergantung dari kemampuan dan pengalaman sang penyangrai biji kopi sedangkan pada smart coffee roaster menggunakan sensor dan control cerdas untuk mengoperasikan roaster dan mendapatkan kopi dengan konsistensi terbaik dan akurat. Makalah kali ini membahas tentang peluang dan tantangan yang diperlukan untuk membuat versi terbaik dari smart coffee roaster dari sisi system elektronik, desain, dan Artificial Intelligence (AI). Sistem elektronik terdiri dari sensor, control, dan aktuator. Desain yang Ergonomis, estetis, serta kenyamanan pengguna menjadi kunci utama yang diperlukan untuk membuat desain terbaik. Aplikasi AI mendeteksi kematangan biji kopi dan deteksi suara “retak” dengan memanfaatkan teknologi machine learning. Studi awal dilakukan dengan format audio hasil roasting dan dipisahkan antara audio yang mengandung suara retakan biji kopi dan audio yang tidak mengandung suara retakan biji kopi. Dataset audio tersebut kemudian diubah ke dalam format gambar menggunakan metode Mel-frequency cepstral coefficients (MFCC), untuk kemudian dilakukan pemodelan dengan menggunakan supervised learning convolutional neural network (CNN).
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.
Co-Authors Amri, M Faizal Arjon Turnip Danasasmita, Febrianti Santiardi Fakhrurroja, Hanif Kusumandari, Dwi Esti Mimin Muhaemin, Mimin Nadiya, Ulfah Rahmawati, Annida Shelly Iskandar Simbolon, Artha Ivonita Siti Aminah Sobana