K-Means Algorithm Analysis for Election Cluster Prediction
The general election is a democratic process that is carried out in every country whose system of government is presidential, including Indonesia, which conducts it every five years. In fact, some people abstain, leading to budget wasting and missing target. Thus, it is very important to identify cl...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Politeknik Negeri Padang
2023-01-01
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| Series: | JOIV: International Journal on Informatics Visualization |
| Subjects: | |
| Online Access: | https://joiv.org/index.php/joiv/article/view/1107 |
| _version_ | 1811159810938241024 |
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| author | Sri Ngudi Wahyuni Nazmun Nahar Khanom Yuli Astuti |
| author_facet | Sri Ngudi Wahyuni Nazmun Nahar Khanom Yuli Astuti |
| author_sort | Sri Ngudi Wahyuni |
| collection | DOAJ |
| description | The general election is a democratic process that is carried out in every country whose system of government is presidential, including Indonesia, which conducts it every five years. In fact, some people abstain, leading to budget wasting and missing target. Thus, it is very important to identify clusters of general election districts and map the number of voters to map the budget for the upcoming election. This process needs prediction to help reduce budgeting risk as an early warning. Based on the latest election data taken from Margokaton, Yogyakarta, Indonesia, many people voted in 2021, but the number of abstainers is high. In this case, cluster prediction is important to identify the election participants in each area. The K-Means algorithm could also predict abstainer areas in election activities to facilitate early mitigation in drafting election budgeting. Therefore, this study aimed to identify the pattern of voters in the election using the K-means algorithm. The data parameters comprised the list of voters, Unused ballot papers, and the sum of abstainers. This study is important because it contributes to reducing the election budget of each area. The data obtained from the Indonesia Ministry of Internal Affairs official website in 2021 were processed using the RapidMiner tool. The results showed more than 11% of the non-voters in cluster 1, 16% in Cluster 2, and 8% in cluster 3. The evaluation of clusters value is 2.04, indicating that the clustering using K-means is suitable, as shown by the DBI value close to 0. The results indicate that testing the cluster optimization of the K-Means algorithm using DBI is highly recommended. Based on this prediction result, the government needs special attention to clusters with many abstainers to decrease the number of abstainers and prevent overbudgeting. These results indicate the need to review the election participant data in 2024. Furthermore, there is a need for continuous socialization and education about election activities to reduce the number of abstainers and prevent overbudgeting. |
| first_indexed | 2024-04-10T05:48:32Z |
| format | Article |
| id | doaj.art-520b9cfc9df545c9ad30cd6c9a8aa05c |
| institution | Directory Open Access Journal |
| issn | 2549-9610 2549-9904 |
| language | English |
| last_indexed | 2024-04-10T05:48:32Z |
| publishDate | 2023-01-01 |
| publisher | Politeknik Negeri Padang |
| record_format | Article |
| series | JOIV: International Journal on Informatics Visualization |
| spelling | doaj.art-520b9cfc9df545c9ad30cd6c9a8aa05c2023-03-05T10:27:22ZengPoliteknik Negeri PadangJOIV: International Journal on Informatics Visualization2549-96102549-99042023-01-01711610.30630/joiv.7.1.1107447K-Means Algorithm Analysis for Election Cluster PredictionSri Ngudi Wahyuni0Nazmun Nahar Khanom1Yuli Astuti2Universitas Amikom Yogyakarta, Sleman, Yogyakarta 55283, IndonesiaUniversity of Professionals, Mirpur Cantonment, Dhaka, 1216, BangladeshUniversitas Amikom Yogyakarta, Sleman, Yogyakarta 55283, IndonesiaThe general election is a democratic process that is carried out in every country whose system of government is presidential, including Indonesia, which conducts it every five years. In fact, some people abstain, leading to budget wasting and missing target. Thus, it is very important to identify clusters of general election districts and map the number of voters to map the budget for the upcoming election. This process needs prediction to help reduce budgeting risk as an early warning. Based on the latest election data taken from Margokaton, Yogyakarta, Indonesia, many people voted in 2021, but the number of abstainers is high. In this case, cluster prediction is important to identify the election participants in each area. The K-Means algorithm could also predict abstainer areas in election activities to facilitate early mitigation in drafting election budgeting. Therefore, this study aimed to identify the pattern of voters in the election using the K-means algorithm. The data parameters comprised the list of voters, Unused ballot papers, and the sum of abstainers. This study is important because it contributes to reducing the election budget of each area. The data obtained from the Indonesia Ministry of Internal Affairs official website in 2021 were processed using the RapidMiner tool. The results showed more than 11% of the non-voters in cluster 1, 16% in Cluster 2, and 8% in cluster 3. The evaluation of clusters value is 2.04, indicating that the clustering using K-means is suitable, as shown by the DBI value close to 0. The results indicate that testing the cluster optimization of the K-Means algorithm using DBI is highly recommended. Based on this prediction result, the government needs special attention to clusters with many abstainers to decrease the number of abstainers and prevent overbudgeting. These results indicate the need to review the election participant data in 2024. Furthermore, there is a need for continuous socialization and education about election activities to reduce the number of abstainers and prevent overbudgeting.https://joiv.org/index.php/joiv/article/view/1107k-means algorithmclusterpredictionelectiondavies bouldin index. |
| spellingShingle | Sri Ngudi Wahyuni Nazmun Nahar Khanom Yuli Astuti K-Means Algorithm Analysis for Election Cluster Prediction JOIV: International Journal on Informatics Visualization k-means algorithm cluster prediction election davies bouldin index. |
| title | K-Means Algorithm Analysis for Election Cluster Prediction |
| title_full | K-Means Algorithm Analysis for Election Cluster Prediction |
| title_fullStr | K-Means Algorithm Analysis for Election Cluster Prediction |
| title_full_unstemmed | K-Means Algorithm Analysis for Election Cluster Prediction |
| title_short | K-Means Algorithm Analysis for Election Cluster Prediction |
| title_sort | k means algorithm analysis for election cluster prediction |
| topic | k-means algorithm cluster prediction election davies bouldin index. |
| url | https://joiv.org/index.php/joiv/article/view/1107 |
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