Recognition of Illegible Digits on Indonesian Election C1 Forms Using Convolutional Neural Network for Recapitulation Information System (SIREKAP)
Why this work is in the frame
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Bibliographic record
Abstract
The General Election (PEMILU) in Indonesia utilizes the Recapitulation Information System (SIREKAP) to accelerate and improve the accuracy of vote counting.However, the system often fails to recognize numbers on C1 sheets due to handwriting variations, low image quality, and visual disturbances.This study develops a Convolutional Neural Network (CNN) to classify digits 0-9 and the letter X, which are frequently misread.The dataset was collected from 300 respondents who rewrote numbers with seven variations: bold, right italic, left italic, crumpled paper, subscript, superscript, and upside down.A total of 3,850 images were generated and divided into 70% training, 15% validation, and 15% testing.Four CNN configurations were compared: standard, with L1 regularization, L2 regularization, and Elastic Net (L1+L2).The standard CNN achieved 94.92% training accuracy, 72.91% validation, and 69.88% testing.The L1 model showed overfitting with 91.99% training but only 59.72% testing accuracy.L2 regularization improved results with 92.47% training and 75.84% testing accuracy.Elastic Net achieved the best balance, reaching 95.51% training, 71.74% validation, and 77.89% testing accuracy.These findings highlight the effectiveness of Elastic Net in enhancing generalization and reducing misclassification, thereby supporting more reliable election vote recapitulation.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.013 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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