Analisis Probabilitas Aroma Sugar browning Kopi Robusta pada Proses Roasting Tradisional Menggunakan Naive Bayes

Zuriah Anggun Nur Hikmah (Universitas Muhammadiyah Bengkulu, Bengkulu)
Muhammad Imanullah (Universitas Muhammadiyah Bengkulu, Bengkulu)

TIN: TERAPAN INFORMATIKA NUSANTARA
Vol 7 No 2 (2026): July 2026

The roasting process is one of the key stages in coffee processing because it greatly influences aroma and final flavor development. In Pasemah Air Keruh District, Robusta coffee is still roasted manually, and no clear guidelines exist for roasting conditions that consistently produce a sugar browning aroma. This study aimed to analyze the probability of sugar browning aroma formation in Robusta coffee and identify the dominant roasting category using the Naive Bayes algorithm. Data were collected from 150 Robusta coffee processors in Pasemah Air Keruh District. The observed variables included heat intensity, roasting duration, stirring, and cooling. Aroma classes were determined by baristas as the ground truth and categorized into Medium Roast, Medium Dark Roast, and Dark Roast. The data were cleaned, validated, converted into numerical form, and divided into 120 training and 30 testing samples. The results showed that the Naive Bayes algorithm achieved an accuracy of 76.67% and identified the relationship between roasting variables and sugar browning aroma. The Medium Dark Roast category had the highest probability of producing sugar browning aroma. This study provides an overview of the relationship between traditional roasting parameters and the probability of sugar browning aroma formation, which may serve as a reference for developing a more consistent coffee roasting process.