OPTIMALISASI PERKEMBANGAN ZAKAT INFAQ SHADAQAH MENGGUNAKAN PLATFROM E-COMMERCE
Bibliographic record
Abstract
This study aims to determine the development of zakat after using the E-Commerce Platform. The method used in this research is literature study. The results of this study indicate that the collection of ZIS funds in the donation, zakat maal, and zakat al-fitrah segments from April to May 2020 saw a significant increase. During the Covid-19 pandemic, the National Zakat Agency has always tried to make it easier for people to pay zakat without having to meet in person. Therefore, BAZNAS optimizes zakat payments online, namely using the E-Commerce Platform. In December 2020, the donation segment even grew by more than 50% compared to the previous year. As for accumulatively, the collection of donations in the fourth quarter of 2020 was recorded to grow up to 78% compared to donations during the fourth quarter of 2019. Furthermore, donation growth is expected to continue until 2021, along with the Covid-19 pandemic. Keywords: Zakat, Infaq, Sadaqah, E-Commerce Platform
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How this classification was reachedexpand
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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; both teacher heads agree on what is shown here.
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".