An Efficient Sampling Approach for Village Elections: Quick Count Using
Stratified Systematic Cluster Sampling
Dublin Core
Title
An Efficient Sampling Approach for Village Elections: Quick Count Using
Stratified Systematic Cluster Sampling
Stratified Systematic Cluster Sampling
Subject
Quick Count, Sampling Method, Stratified Systematic Cluster Random Sampling, Village Head Election
Description
Quick counts are widely used to estimate election outcomes before official results are announced. However, their accuracy depends on the quality
of the sampling method used. This study applies the Stratified Systematic Cluster Random Sampling method in the 2019 village head election in
Panembangan Village, Indonesia, to provide an efficient and statistically sound quick count process suitable for rural contexts with limited
resources. The method integrates stratification (dividing voters by hamlet), clustering (grouping by polling stations), and systematic sampling
(selecting polling stations at fixed intervals). Out of 10 polling stations, 5 were systematically selected after stratification. All valid votes from
these polling stations were used for estimation. The results show that candidate Untung Sanyoto received 59.16% of the vote, while his opponent
received 40.84%. The margin of error was ±0.69% at a 95% confidence level, and the estimates closely matched the official election results,
demonstrating the method’s reliability. This study shows that combining statistical rigor with practical sampling strategies can produce fast,
accurate, and cost-effective results. It provides a scalable model for local elections, especially in regions facing geographic or logistical
challenges, and contributes to the development of transparent and trustworthy democratic practices
of the sampling method used. This study applies the Stratified Systematic Cluster Random Sampling method in the 2019 village head election in
Panembangan Village, Indonesia, to provide an efficient and statistically sound quick count process suitable for rural contexts with limited
resources. The method integrates stratification (dividing voters by hamlet), clustering (grouping by polling stations), and systematic sampling
(selecting polling stations at fixed intervals). Out of 10 polling stations, 5 were systematically selected after stratification. All valid votes from
these polling stations were used for estimation. The results show that candidate Untung Sanyoto received 59.16% of the vote, while his opponent
received 40.84%. The margin of error was ±0.69% at a 95% confidence level, and the estimates closely matched the official election results,
demonstrating the method’s reliability. This study shows that combining statistical rigor with practical sampling strategies can produce fast,
accurate, and cost-effective results. It provides a scalable model for local elections, especially in regions facing geographic or logistical
challenges, and contributes to the development of transparent and trustworthy democratic practices
Creator
Khabib Adi Nugroho1,*, Turino2
Source
https://ijiis.org/index.php/IJIIS/article/view/245/156
Publisher
Amikom Purwokerto University,
Date
januari 2025
Contributor
Fajar bagus W
Format
PDF
Language
English
Type
Text
Files
Collection
Citation
Khabib Adi Nugroho1,*, Turino2, “An Efficient Sampling Approach for Village Elections: Quick Count Using
Stratified Systematic Cluster Sampling,” Repository Horizon University Indonesia, accessed January 2, 2026, https://repository.horizon.ac.id/items/show/9724.
Stratified Systematic Cluster Sampling,” Repository Horizon University Indonesia, accessed January 2, 2026, https://repository.horizon.ac.id/items/show/9724.