| Abstrak/Abstract |
Page Header User Username oskarnatan Password •••••••••••••• Remember me 1.5 2023 CiteScore 28th percentile Powered by Scopus SCImago Journal & Country Rank Quick Links Author Guideline Editorial Boards Reviewers Online Submissions Abstracting and Indexing Publication Ethics Visitor Statistics Contact Us Latex Template Journal Content Search All Browse By Issue By Author By Title Other Journals Information For Readers For Authors For Librarians Donations Home About Log In Register Search Current Archives Announcements Home > Vol 13, No 2 > Istiyanto DOI: 10.52549/ijeei.v13i2.6146 Trainer Kit for Aroma Classification Using Artificial Intelligence Jazi Eko Istiyanto, Danang Lelono, Oskar Natan, Shafa Khamila, Hafizha Adhiyant, Ikhlasul Amal Abda’i, Ilyaz Raukhillah Adzaqi 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. |
