Sistem Pendukung Keputusan Pemilihan Susu Formula pada Balita Menggunakan Metode Simple Additive Weighting (SAW)
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
Formula milk is packed with essential nutrients. It contains beneficial components such as carbohydrates, proteins, fats, vitamins, sodium, DHA, and more. High-quality formula milk should not lead to gastrointestinal issues such as diarrhea, vomiting, or problems with digestion, nor should it cause coughing, breathing difficulties, or skin reactions due to an incorrect formula choice. This research aims to explore how mothers select suitable formula milk for their babies. The study utilizes the SIMPLE ADDITIVE WEIGHTING (SAW) method to determine alternative options based on pre-assigned weights and criteria. Following this, the ranking method is applied to identify the best alternative. According to the findings, five alternatives were evaluated: MORINAGA CHIL KID, LACTOGEN, SGM, BEBELOVE, and NUTRIBABY ROYAL 1. Additionally, five criteria were considered: Milk Price, Safety (Bpom Certification, Halal, etc.), Nutritional Content (Protein, Calcium, Iron, Vitamins, etc.), Taste (Natural Sweetness, Vanilla, Honey), and Market Availability.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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 it