Analisis Performance Fuzzy Tsukamoto Dalam Klasifikasi Bantuan Kemiskinan
Bibliographic record
Abstract
The Central Bureau of Statistics (BPS) showed that the poverty rate in Indonesia in September 2014 still high at about 27.7 million people, or about 10.96%. As a basis for policy countermeasures, understand the problem of poverty often demands the effort of defining, measuring, and identifying the root causes of poverty. This study wanted to use one of the methods that exist in fuzzy logic to classify beneficiaries of poverty that exist in Bantul. Fuzzy Inference System used in this study using Tsukamoto with 8 rule established by a group of poor criteria and types of poverty relief. There are three groups of criteria of poverty derived from 11 criteria of poverty in Bantul. While the types of assistance that are used are Raskin, BLT and KUR. The system is built using PHP. To see the performance Tsukamoto method in this study used 50 data poor people in Sub Districs Banguntapan. From the test results turned out to obtained an accuracy of 52%, meaning that there were 26 correct data according to the original data. It is necessary to modify the rules and membership functions to improve system accuracy results
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".