Pengelompokan Data Kasus Keracunan Makanan Biologis Berdasarkan Faktor Penyebab Menggunakan Metode Clustering
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Cases of biological food poisoning can be caused by several causative factors, one of which is a food processing site that does not meet health requirements. According to the BPOM report (2016) cases of food poisoning in Indonesia in 2016 reached 1,068 cases. In 2016, 60 extraordinary events (KLB) of food poisoning were reported by 31 BB/BPOM throughout Indonesia. From the many cases of food poisoning that occur, it is necessary to take action in prevention by processing data on existing cases of poisoning to follow up on existing problems to reduce the number of cases of food poisoning by using a system on a computer so that the managed data can be processed quickly to obtain further information. Therefore the author wants to use a system with the clustering method to assist in processing data on biological poisoning cases grouping objects based on the characteristics of each object. Based on the research conducted, it can be seen that in cluster 2 in the dasta group of biological poisoning cases there are 11 data with centroid point age (x) 2, namely 12-16 years, centroid point on the type of poisoning (y) 6.36, namely sandwiches, and centroid point on the causative factor (z) 2.9, namely Gram-negative rod-shaped bacteria which are usually found in the intestines of humans and warm-blooded animals.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it