Aplikasi Sistem Pendukung Keputusan Menggunakan Algoritma C5 Untuk Menentukan Penerima Bantuan Sosial
Bibliographic record
Abstract
Poverty is one of the development problems in various fields which is characterized by high unemployment, underdevelopment and deterioration caused by change. Most of the residents of Setia village, Pahae Jae sub-district, still have many poor people, the poverty rate is still high and they have implemented a social assistance system for the poor or underprivileged to reduce poverty. However, it turns out that the selection of social assistance recipients in the Setia Village area, Pahae Jae District is still using a manual system. the selection process is carried out by observing the residents' files from the start of the process to who can receive assistance based on the criteria that have been determined in the social section. So that the settlement process in determining the prospective recipients of social assistance does not occur systematically and sometimes is not on target, the decision tree algorithm c5.0 method was chosen by the author to speed up and facilitate the selection of eligible citizens to receive social assistance. the criteria are processed so that and obtain a value that will be compared with the training data, this research is an application of the classification of eligible and unworthy social assistance recipients. building this program or application can help make it easier for the village to determine recipients of the social assistance program for underprivile ged families.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".