Pengelompokan Menggunakan Metode Clustering Pada Pola Hidup Pengguna KB
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
Healthy lifestyles are habits of doing something, be it food, healthy behavior so as to avoid the disturbance of all kinds of diseases, both physical and non-physical diseases, as well as birth control users must also strive for a healthy lifestyle, such as managing a healthy diet, rest, exercise, eating vegetables and fruits, doing optimal physical activity, not consuming alcohol, and maintaining a healthy body. In this problem, many family planning users do not pay attention to a healthy lifestyle because they think that the family planning tools used have no risk to health, but the use of family planning has side effects on health such as menstruation is not smooth, the body is obese, the body feels warm or feverish, there are blood clots, nausea, bloating, changes in vision, difficulty in getting back to normal, headaches, and others. To be able to attract the attention of the community in implementing a healthy lifestyle for family planning users, it is very necessary to have a system that can help people in changing their unhealthy lifestyle to a healthier one by grouping family planning user data based on variables that have been determined using the clustering method, to group data on healthy lifestyles for family planning users which later the results of this study can be used as input and guidance for a healthy lifestyle for family planning users, so that family planning users are more careful and have a healthy life. Of the 20 data, there are 3 groups, namely group 1 there are 4 data and group 2 there are 4 data and group 3 there are 12 data from the above results it can be seen that in cluster 3 is a group on family planning users based on a lot with a total of 12 data and is located in the contraceptive type group (X) is injectable birth control, and for the lifestyle group (Y), namely Frequent Night Baths and Risk (Z), namely Decreased Bone Strength.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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