Sistem Pendukung Keputusan Penerima Zakat Menggunakan Metode Simple Multi Attribute Rating Technique (SMART) (STUDI KASUS: Kantor Baznas Kota Binjai)
Bibliographic record
Abstract
Banyak permasalahan yang dapat diselesaikan dengan menggunakan Sistem Pendukung Keputusan (SPK). Salah satunya adalah menentukan penerimaan zakat, ada beberapa metode yang dapat digunakan dalam membangun suatu Sistem Pendukung Keputusan (SPK) di antaranya Simple Multi Attribute Raiting Technique (SMART). Metode ini adalah metode penentuan Rangking, dimana proses rangking di berikan kepada masing-masing kriteria yaitu status pekerjaan, setatus tempat tinggal, kondisi kesehatan, pendapatan dan jumlah tanggungan keluarga . Jumlah alternatif sebanyak 15 (lima). Setelah semua nilai kriteria di masukkan maka hasil dari perhitungan dengan menggunakan metode SMART akan di rangking kan hingga di dapat sebuah rangking pembobotan dimana hasil dari perangkingan alternatif yang di gunakan bahwa yang sangat layak menerima zakat dengan nilai terbesar adalah Alternatif A10 dengan rangking 1 atas nama Lasmik dengan nilai 0,162
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".