Optimizing Village-Level Quick Count Accuracy and Efficiency via a Stratified Systematic Cluster Random Sampling Approach

Accurate and transparent election result reporting plays a vital role in preserving public confidence and reinforcing democratic legitimacy. This research evaluates the effectiveness of the Stratified Systematic Cluster Random Sampling (SSCRS) method in improving the accuracy and efficiency of village-level quick counts. Conducted in Panembangan Village, Cilongok District, Banyumas Regency, the study employs a quantitative descriptive approach to examine how the integration of stratification, clustering, and systematic selection techniques can generate statistically robust election estimates within limited operational constraints. The research population consisted of all valid ballots from the 2019 Village Head Election, distributed across ten polling stations (TPS). Applying the SSCRS design, five TPS were systematically selected following stratification, yielding a sample of 3,760 valid votes. Data were analyzed using statistical procedures to determine the Margin of Error (MoE) and the 95% Confidence Interval (CI). The findings show that Candidate Untung Sanyoto secured 59.16% of the votes, while Candidate Suprapto received 40.84%, with an MoE of ±0.69% and CI ranges of 58.47–59.84% and 40.16–41.53%, respectively. These outcomes demonstrate that the SSCRS method produces highly accurate and reliable estimates closely aligned with the official results, confirming both its statistical validity and field-level practicality. By combining three sampling techniques, the method ensures proportional representation, reduces sampling bias, and enhances data collection efficiency under constrained conditions. This research provides a methodological contribution to electoral statistics, presenting a replicable hybrid sampling model well-suited for small-scale electoral contexts. Future studies are encouraged to extend this framework to different regions and election types to further assess its flexibility and robustness across diverse demographic and logistical settings.