ARIANTI SUNDARI Primary Author mixed material bibliography Banda Aceh Fakultas Teknologi Hasi Pertanian 2025

Skripsi

Gayo Arabica coffee is a leading Indonesian commodity known for its distinctive flavor, which is influenced by its roasting level (light, medium, and dark). The determination of roasting levels has traditionally relied on the subjectivity of the roaster and organoleptic methods, leading to variability in product quality. Analytical methods such as Gas Chromatography–Mass Spectrometry (GC-MS) can accurately identify volatile compounds but are costly, complex, and require specialized laboratories and trained personnel, making them inefficient for direct field applications. This study aims to design a roasting level classification device for Gayo Arabica coffee using gas sensors integrated with an Arduino microcontroller. The system utilizes four gas sensors (MQ-3, MQ-8, MQ-9, MQ-135) to detect volatile compounds from fine coffee powder samples in a sealed chamber. Sensor data are transmitted to a laptop and analyzed using Linear Discriminant Analysis (LDA) and Random Forest to develop a classification model, which is then implemented into the device. Testing results show a classification accuracy of 96.67%, with high precision, recall, and F1-score values across all three classes. This system has proven effective in distinguishing roasting levels of Gayo Arabica coffee and holds potential for application in small- to medium-scale industries as an affordable alternative to subjective methods and high-cost laboratory equipment such as GC-MS. 0 ELECTRONIC THESES AND DISSERTATION Universitas Syiah Kuala 158623 2025-07-05 15:50:23 2025-07-07 09:35:07 machine generated