Decision Support System for Zakat Asnaf Selection among Uitm Melaka Students Using Artificial Neural Networks
Bibliographic record
Abstract
This study developed a Decision Support System for Zakat Asnaf Selection (DSSZAS) to address inefficiencies in the manual distribution of zakat among students at Universiti Teknologi MARA (UiTM) Cawangan Melaka. The current process faces challenges in accurately identifying eligible asnaf and distributing promptly. Therefore, to solve this, the DSSZAS leverages Artificial Neural Networks (ANN) to automate the classification of students into asnaf categories (faqr, miskin, and fisabilillah) based on socioeconomic data. The system was designed and trained with historical data using the Waterfall methodology. A comparison method was deployed between the generated result and human decision to test the result reliability. It achieves an accuracy rate of 1.0% with a minimized Mean Squared Error (MSE) of 0.06. The system significantly reduces human bias and enhances efficiency through automated decision-making and email notifications that inform students of their application status., DSSZAS strengthens the fairness and reliability of zakat distribution by providing a transparent and data-driven approach.
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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 | 0.010 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".