Pengaruh Penambahan Lactobacillus fermentum CK165 dan Lama Fermentasi terhadap Karakteristik Fisik Kopi Arabika (Coffea arabica) Asal Kintamani, Bangli
Bibliographic record
Abstract
The fermentation stage is considered to be one of the critical steps in coffee processing due to its impact on the final quality of the product. The aim of this study was to determined the effect of Lactobacillus fermentum CK165 addition and fermentation time on the physical characteristics of Arabica coffee Kintamani, Bangli, and knowing the right treatment to produce Arabica coffee with the best physical characteristics. This study used a completely randomized design (CRD) with treatment using Lactobacillus fermentum CK165 addition and duration of fermentation consisting of 0 hours, 12 hours, 24 hours, and 36 hours. Each treatment was repeated 2 times to obtain 16 experimental units. The physical characteristics of Arabica coffee were analyzed statistically by analysis of variance (ANOVA) and continued with Duncan multiple range test (DMRT), if there was an affect between treatments. The result showed that Lactobacillus fermentum CK165 addition and fermentation time significantly affected the bulk density, moisture content, bean number/10 g, weight of 100 beans, bean wide, and color (L* and b*). Lactobacillus fermentum CK165 addition and fermentation for 24 hours resulted Arabica coffee with the best physical characteristics with bulk density 0.637 g/ml, moisture content 8.507%, bean number/10 g 51.500 beans, weight of 100 beans 19.873 g, long 10.570 mm, wide 7.401 mm, thick 4.305 mm, L* 36.588, a* 1,670, b* 11.045, broken beans 0.533 bean number/100 g, brown beans 0.102 bean number/100 g, and partly black beans 1.766 bean number/100 g.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".