Optimizing Village-Level Quick Count Accuracy and Efficiency via a
Stratified Systematic Cluster Random Sampling Approach
Dublin Core
Title
Optimizing Village-Level Quick Count Accuracy and Efficiency via a
Stratified Systematic Cluster Random Sampling Approach
Stratified Systematic Cluster Random Sampling Approach
Subject
Quick Count; Stratified Systematic Cluster Random Sampling; Electoral Transparency; Sampling Accuracy; Village Head Election
Description
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
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
Creator
Rizqi Yoga Pratama1
, Abednego Dwi Septiadi2,*, Muhamad Awiet Wiedanto Prasetyo3
, Abednego Dwi Septiadi2,*, Muhamad Awiet Wiedanto Prasetyo3
Source
https://ijiis.org/index.php/IJIIS/article/view/220/149
Publisher
University Of Amikom Purwokerto, Indonesia
Date
desember 2024
Contributor
Fajar bagus W
Format
PDF
Language
English
Type
Text
Files
Collection
Citation
Rizqi Yoga Pratama1
, Abednego Dwi Septiadi2,*, Muhamad Awiet Wiedanto Prasetyo3
, “Optimizing Village-Level Quick Count Accuracy and Efficiency via a
Stratified Systematic Cluster Random Sampling Approach,” Repository Horizon University Indonesia, accessed January 2, 2026, https://repository.horizon.ac.id/items/show/9717.
Stratified Systematic Cluster Random Sampling Approach,” Repository Horizon University Indonesia, accessed January 2, 2026, https://repository.horizon.ac.id/items/show/9717.