Sistem Pendukung Keputusan Penerima Zakat Menggunakan Metode Simple Multi Attribute Rating Technique (SMART) (STUDI KASUS: Kantor Baznas Kota Binjai)
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

 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
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.006 | 0.000 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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