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All Journal International Journal of Electrical and Computer Engineering TELKOMNIKA (Telecommunication Computing Electronics and Control) Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) SEMNASTERA (Seminar Nasional Teknologi dan Riset Terapan) Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics
Wibowo, Jony Winaryo
Research Center For Smart Mechatronics, National Research And Innovation Agency, Bandung, Indonesia
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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Implementation of Image Processing and CNN for Roasted-Coffee Level Classification Anto, Irfan Asfy Fakhry; Wibowo, Jony Winaryo; Salim, Taufik Ibnu; Munandar, Aris
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 12, No 4: December 2024
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v12i4.5531

Abstract

The roasting process of coffee beans plays a crucial role in the development of chemicals responsible for the rich color and complex flavors characteristic of well-roasted coffee. One approach to understanding this process involves assessing the roast level, which varies in color from light to dark, with intermediate levels in between. In this study, image processing was performed using Convolutional Neural Networks (CNNs), a widely used method for image classification. The objective was to utilize the LAB color model and the CNN framework to classify the roast levels of coffee beans based on images from files or video streams. The study also details the hardware and software tools employed. A user-friendly graphical interface was developed to ensure ease of use, requiring minimal training for efficient operation. The research successfully designed, developed, and implemented an application for classifying coffee bean roast levels using two methods: LAB color model image processing and the CNN model. Consequently, the system can recognize roast levels based on the outputs from both the LAB model and the CNN model. This research represents a preliminary effort and requires further development to support more extensive studies. Ultimately, it serves as a foundation for future exploration and the application of embedded system-based solutions for assessing coffee bean maturity levels in alignment with Agtron classification standards.
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.
Pengembangan Aplikasi Smart Coffee Roaster berbasis IoT untuk Pengendalian dan Monitoring Proses Penyangraian Kopi Aris Munandar; Oka Mahendra; Jony Winaryo Wibowo
Prosiding SISFOTEK Vol 9 No 1 (2025): SISFOTEK IX 2025
Publisher : Ikatan Ahli Informatika Indonesia

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Abstract

The coffee roasting process plays a crucial role in determining flavor quality; therefore, a system that ensures consistency and easy replication of roasting profiles is required. This study developed a smartphone-based Smart Coffee Roaster integrated with an ESP32 microcontroller to support manual and automatic roasting control and monitoring. The system was designed to record and store roasting profiles, synchronize parameters with the ESP32 device, and display temperature and roasting stages in real time. The hardware was designed using relay modules and an ESP32 microcontroller, while the software was developed using Kodular and Arduino IDE. The system was tested using black-box methods and successfully executed all key functions, including profile selection, actuator control, and real-time visualization. The application simplified the monitoring and control of the roasting process and provided structured profile data to maintain coffee quality consistency and support further analysis based on artificial intelligence
Adaptive Lyapunov-based control for underactuated nonlinear system using deep neural network Haiyunnisa, Triya; Wibowo, Jony Winaryo
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i2.pp717-728

Abstract

This paper proposes an adaptive Lyapunov-based control approach using deep neural networks (DNN) for underactuated nonlinear systems, with case studies on the Furuta pendulum and a wheeled path-following system. This approach combines simultaneous learning of the Lyapunov function V(x) to satisfy the positive-definite condition and the control law u(x) to satisfy negative definiteness of V ̇(x) thus ensuring the asymptotic stability of the system. The proposed model is validated using Python-based simulation. Results show that the proposed method significantly expands the region of attraction (RoA) compared to the linear quadratic regulator (LQR) method. In the Furuta pendulum, the RoA area in the [θ−θ˙] plane increased from 89.04% to 101.14% and in the [α−α˙] plane from 80.28% to 83.79%. Meanwhile, in the wheeled path-following system, the RoA within safety domain increased from 85.28% to 101.69%. Furthermore, robustness tests showed that the controller can maintain tracking performance on a sinusoidal path and reject short disturbances without excessive safety boundary violations. The resulting control signal remained smooth, non-oscillatory, and within the actuator saturation limits, ensuring safe and energy-efficient control. This approach offers a significant contribution by integrating Lyapunov stability theory, deep learning, and online adaptation, resulting a robust and practical for nonlinear underactuated systems.
Comparative performance analysis of convolutional neural network-architectures on coffee-bean roast classification Irfan Asfy Fakhry Anto; Jony Winaryo Wibowo; Aris Munandar; Taufik Ibnu Salim
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 23, No 6: December 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v23i6.27090

Abstract

The classification of coffee bean roast levels using Agtron standards has evolved from traditional subjective methods to technology-driven approaches employing advanced artificial intelligence. Recent advancements in computer vision have demonstrated the capability of convolutional neural networks (CNNs) in providing objective and consistent roast level classification compared to human visual assessment, which is prone to variability and subjectivity. This research presents a performance analysis of five CNN architectures (AlexNet, ResNet, MobileNet, VGGNet, and DenseNet) for classifying coffee beans into eight distinct Agtron roast levels. The comprehensive methodology encompasses four phases: i) data acquisition, ii) image preprocessing, iii) model training and validation, and iv) evaluation metric. During training-validation, DenseNet outperformed other models, achieving 99.702% training accuracy and 77.68% validation accuracy. In the testing evaluation, DenseNet also led with an average testing accuracy of 93.8%, followed by ResNet at 92.6%, VGGNet and AlexNet both at 92.4%, and MobileNet at 89.7%. The results show that the DenseNet shows promise in classifying Agtron coffee-bean roast classification.
Co-Authors Amri, M Faizal Anto, Irfan Asfy Fakhry Aris Munandar Aris Munandar Arjon Turnip Danasasmita, Febrianti Santiardi Fakhrurroja, Hanif Haiyunnisa, Triya Irfan Asfy Fakhry Anto Kusumandari, Dwi Esti Mimin Muhaemin Munandar, Aris Nadiya, Ulfah Oka Mahendra Rahmawati, Annida Salim, Taufik Ibnu Shelly Iskandar Simbolon, Artha Ivonita Siti Aminah Sobana Taufik Ibnu Salim