Peramalan Jumlah Penerima Bantuan Pangan Non Tunai Menggunakan Metode Triple Exponential Smoothing Tipe Brown di Kota Pariaman
Bibliographic record
Abstract
The continuously increasing poverty rate in Pariaman City raises new questions about whether the government's poverty alleviations trategies are optimal. One of the government's poverty alleviation programs to meet food needs is the provision of Non-Cash Food Assistance (Bantuan Pangan Non Tunai or BPNT). This study aims to model and forecast the number of BPNT recipients in Pariaman City in the first quarter of 2025-2026 using Brown’s Triple Exponential Smoothing method. The type of data used is secondary data from the Pariaman City Social Service during the period 2019-2024. The results of the analysis show that the value of the smallest MSE is 403500.7661 using a parameter value of 0.32. Based on the model obtained, the forecast results for the number of recipients of BPNT in Pariaman City for the first quarter of 2025-first quarter of 2026 are 4231 family, 4225 family, 4219 family, 4212 family, and 4204 family.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".