Trainer Kit for Aroma Classification Using Artificial Intelligence

Authors

Jazi Eko Istiyanto
Universitas Gadjah Mada image/svg+xml
Danang Lelono
Universitas Gadjah Mada image/svg+xml
Oskar Natan
Universitas Gadjah Mada image/svg+xml
Shafa Khamila
Universitas Gadjah Mada image/svg+xml
Hafizha Adhiyant
Universitas Gadjah Mada image/svg+xml
Ikhlasul Amal Abda’i
Universitas Gadjah Mada image/svg+xml
Ilyaz Raukhillah Adzaqi
Universitas Gadjah Mada image/svg+xml
DOI: https://doi.org/10.52549/ijeei.v13i2.6146
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Published: 22-04-2025 • Issue: Vol. 13 No. 2: June 2025 • Section: Regular paper

Abstract

This research focused on the development and evaluation of machine learning algorithms for aroma classification using sensor data, implemented within the e-Trainose system. Various algorithms, including Neural Network, Support Vector Machines, and Random Forest, were tested to determine their effectiveness in distinguishing between different aroma samples, namely alcohol, coffee, and tea. The study utilized an array of metal oxide semiconductor sensors to capture the volatile organic compounds associated with each aroma. The features tested included sensor responses such as resistance changes and Gaussian smoothing of sensor data. Among the algorithms tested, Neural Network demonstrated the highest accuracy (98.89%), precision (99.10%), recall (99.10%), and F1 score (99.10%), making it the most reliable model for this task. These results highlight the potential of using machine learning with e-Trainose for real-time aroma detection and classification. The research paves the way for future advancements in the field, including the development of hybrid models and further optimization of sensor-based classification systems.
Keywords: Aroma Classification; Machine Learning; Neural Network; Electronic Nose

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Author Biographies

Jazi Eko Istiyanto
Jazi Eko Istiyanto received his Bachelor's degree in Physics from Gadjah Mada University, Indonesia, in 1986, his Master's degree in Computer Science from the University of Essex, UK, in 1988, and his Doctoral degree in Electronic Systems Engineering from the University of Essex in 1995. He has been a lecturer at Gadjah Mada University since 1988, and served as Chairman of the Indonesia Nuclear Energy Regulatory Agency (BAPETEN) from 2014 to 2021. His research focuses on Cyber-Physical Systems, their security, and FPGA design automation. Dr. Istiyanto has been awarded the ASEAN Engineer title and is an active member of various professional societies, including IEEE and APTIKOM.
Danang Lelono
Danang Lelono received his Master’s degree in Electro/Technics from Gadjah Mada University, Indonesia, in 2000, and his undergraduate degree in Electronics & Instrumentation in 1994, also from Gadjah Mada University. He has been a lecturer in the Department of Computer Science and Electronics at Universitas Gadjah Mada since 1998. His research interests include Instrumentation & Control, Smart Sensors, Intelligent Systems, Embedded Systems, and Emergency Nursing. He is actively involved in both teaching and research, contributing to advancements in the fields of computer science, electronics, and control systems.
Oskar Natan
Oskar Natan received the BASc degree in electronics engineering and the MAEng degree in electrical engineering from Politeknik Elektronika Negeri Surabaya, Indonesia, in 2017 and 2019, respectively, and the PhD(Eng) degree in computer science and engineering from Toyohashi University of Technology, Japan, in 2023. Since January 2020, he has been affiliated with the Department of Computer Science and Electronics, Universitas Gadjah Mada, Indonesia, first as a Lecturer and currently an Assistant Professor. He has been serving as a reviewer/TPC for some reputable journals and conferences. His research interests include sensor fusion, deep learning, hardware acceleration, and end-to-end systems.
Shafa Khamila
Shafa Khamila is currently pursuing a Bachelor’s degree in Electronics and Instrumentation at Universitas Gadjah Mada, expected to graduate in 2026. He currently serve as intern project manager in PT. Stechoq Robotika Indonesia. Shafa is also the Manager of the Traditional Dancing Robot Team Alfan, where she leads technical coordination and team management.Shafa is actively learning and has a strong interest in hardware design, sensor integration, and machine learning algorithms, and is advancing her skills in data science, focusing on machine learning, Python programming, and data analysis.
Hafizha Adhiyant
Hafizha Adhiyant is currently pursuing a Bachelor's degree in Electronics and Instrumentation at Universitas Gadjah Mada, expected to graduate in 2026. He currently serve as intern hardware engineer in PT. Stechoq Robotika Indonesia. He is also involved in several technical roles, including as supervisor at Bimasakti Racing Team UGM, responsible for researching new electrical systems for FSAE cars. Hafizha has earned notable achievements, including 8th place in Formula SAE Italy 2023 and 4th place in KRBAI Regional 2023. He is also a recipient of the Pertamina Foundation Sobat Bumi Scholarship. Hafizha is quite skilled in areas such as programming, 3D design, PCB design, and electronics control systems.
Ikhlasul Amal Abda’i
Ikhlasul Amal Abda’i, a fifth-semester Electronics and Instrumentation student at Universitas Gadjah Mada, is an ambitious and versatile individual with expertise in control systems, programming, robotics, 3D design, and AI. He has demonstrated excellence in academic and competitive projects, including drone development and Chem-E-Car competitions. Known for his adaptability, creativity, and dedication, Ikhlas aspires to contribute innovative solutions in the fields of technology and engineering while continually expanding his knowledge and skills.
Ilyaz Raukhillah Adzaqi
Ilyaz Raukhillah Adzaqi is a creative and adaptable individual, passionate about learning and problem-solving with expertise in software programming, hardware programming and PCB Design. Currently pursuing a degree in Electronics and Instrumentation at Universitas Gadjah Mada, Ilyaz has a strong background in robotics and programming. He is involved with various organizations such as ORCA GMRT UGM, contributing to mechanical and programming divisions, and the ELINS Research Club, focusing on robotics research.

How to Cite

[1]
J. E. Istiyanto et al., “Trainer Kit for Aroma Classification Using Artificial Intelligence”, IJEEI, vol. 13, no. 2, pp. 294–308, Apr. 2025, doi: 10.52549/ijeei.v13i2.6146.