Sistem Pendukung Keputusan Pemilihan Susu Formula pada Balita Menggunakan Metode Simple Additive Weighting (SAW)
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".