FUZZY LOGIC MAMDANI PENERIMAAN SEMBAKO UNTUK KELUARGA MISKIN
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
Poverty is one of the fundamental problems that is the center of attention of the Indonesian government. To improve the coordination of poverty reduction, the Government issued Presidential Regulation Number 15 of 2010 concerning the Acceleration of Poverty Reduction which is a revision of Presidential Regulation Number 13 of 2009 concerning Poverty Reduction Coordination. Indonesia is an agricultural country, the average income derived from agriculture. One of the most advanced agricultural fields is rice, which produces rice as a staple food. The large number of Indonesian citizens causes the domestic rice harvest to be insufficient to meet the needs of its citizens, thus requiring additional supplies from abroad. This causes food shortages, especially for poor families. To improve the stability of Indonesia's economy, the Government is trying various ways by distributing basic food items one of the policies taken by the government is by issuing the Republic of Indonesia's presidential regulation number 63 of 2017 concerning the distribution of non-cash social assistance. Calculations on this decision support system are then used a method, the mamdani fuzzy logic method. Fuzzy logic mamdani method from the point of rule evaluation to determine the basic food recipients for poor families. In determining the nine basic food recipient parameters using fuzzy logic, the input variable is divided into 4 namely age, income, home conditions and number of children.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.024 |
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