Strategi Kebijakan Stabilisasi Harga Komoditas Pangan di Provinsi Sumatera Utara
Bibliographic record
Abstract
Food commodities are included in the volatile food category which is dominant in determininginflation because prices fluctuate so that they contribute to national and regional inflation. Thisstudy aims to analyze price stabilization and formulate policy recommendations to anticipatestrategic food commodity price fluctuations in North Sumatra province. Using weekly secondarydata, for the period January 2021-December 2022 (104 observations). Technical analysis using theARIMA model and the Coefficient of Variation. The results of the analysis with reference to pricestabilization indicators as a reference in stabilizing food supply and prices at the consumer levelindicate that there are seven commodities that need immediate intervention, namely Dry SeedSoybeans, Cooking Oil, Chicken Eggs, Pure Beef, Wheat Flour and Sugar. Sand. Alternative policiesin order to maintain the stability of food prices are (1) affordable prices, (2) availability of supplies,(3) smooth distribution and, (4) effective communication. These four strategies can be implementedin the form of (1) carrying out low-cost food market operations, (2) monitoring prices and dailysupplies carried out by the Food Officers Unit, (3) conducting food commodity bazaars before andduring the HBKN, (4) inter-governmental cooperation. Regional, (5) Efforts to provide food commodity supplies, (6) Active distribution of commodities to several markets in the North Sumatraregion, (7) Intensive coordination with TPID with the Ministry of Trade and the Economic Bureau ofthe North Sumatra Provincial Government, (8) High Level implementation TPID meetings ahead ofand during HBKN in several districts/cities of North Sumatra province, and (9) Use of RegionalIncentive Funds to increase food availability.Keywords: ARIMA, price fluctuation, coefficient of variation, price stabilization
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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 | 0.000 |
| 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) | 0.005 | 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".