Determination Characteristic and Classification the Types of Orange Using UV-Vis Spectrophotometer by K-Nearest Neighbor Algorithm
Bibliographic record
Abstract
It has been researched at Laboratorioum Terpadu Faculty of Science and Mathematics.The purpose of this study is determining to distinguish in characteristic between sweet orange and sour orange, it makes sure wavelength and absorbance using UV-Vis Spectrophotometer.Furthermore, it is also determining classification between sweet orange and sour orange by predicting between actual data and predictive data.The method used is K-Nearest Neighbor algorithm using MATLAB 2015.Sweet orange and sour orange are researched for their characteristic to be analyzed whether they have significant differences in wavelength and absorbance.The KNN algorithm is used to see its ability in classification to predict sweet orange and sour orange data from actual data.Through this study, it is found that resulting characteristic of sweet oranges and acid oranges has a different, it can be seen based on forms wavelength and absorbance.Sweet oranges and acid oranges have they own characteristic.On the classification, prediction data results showing that 23 correct data from 40 actual data obtained a percentage of 67.5 % by using K-Nearest Neighbor.The finding of this research may serve as reference.The finding of this study may serve as reference for other researchers to be developed further.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".