Constructing and Optimizing an Evaluation Model for the Implementation of Electronic Voting: An Indonesian Case Study
Bibliographic record
Abstract
In 2024, Indonesia is poised to conduct a significant national event -the simultaneous general election for both presidential and local leadership positions.Historically, manual voting has been the method of choice since the inaugural election in 1955.However, as Indonesia prepares for future electoral exercises, the potential adoption of electronic voting systems is a consideration that merits comprehensive investigation, given the nation's expansive geographical spread and substantial population, which presents considerable challenges in executing any election.Despite several countries previously implementing electronic voting systems in their general elections, these cases have often culminated in failure, primarily due to concerns surrounding data security, public trust, and technological preparedness.This study, employing the structural equation modelling-partial least squares (SEM-PLS) approach, endeavors to evaluate the multifarious factors that could influence the successful deployment of an electronic voting system in Indonesia.The findings reveal that dimensions such as trust in government, technology, and electoral commissions; technological infrastructure; human resources; and constitutional readiness all significantly contribute to the potential success of electronic voting system implementation.These results are anticipated not only to inform the development and application of electronic voting in Indonesia, but also to provide a foundational platform for future research efforts dedicated to constructing a robust and effective electronic voting framework.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".