Penerapan Metode K-Means Untuk Pengelompokkan Data Pelaporan Di Kantor Urusan Agama

The process of reporting marriages at the Religious Affairs Office (KUA) in Laubaleng District is still done manually, which results in obstacles in the management, accuracy and efficiency of data access. This research aims to overcome this problem through developing a system based on the K-Means clustering method. This system is designed to group marriage reporting data based on attributes such as age, marital status, and month of marriage, so as to provide a more structured and informative data pattern. The Elbow method is used to determine the optimal number of clusters, while the K-Means algorithm is applied using Euclidean distance to calculate the closeness of the data to the centroid. The research process involves collecting reporting data from 2019 to 2024, data preprocessing, normalization, and evaluating clustering results using the Davies-Bouldin Index (DBI). The research results show that the K-Means method is effective in grouping data, providing clear visualization of the distribution of marriage patterns, and increasing the efficiency of data management at KUA. With this system, KUA can increase access speed, reduce the potential for errors, and support more accurate data-based decision making.

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Publication Details

Journal
Building of Informatics Technology and Science (BITS)
Published
2024-12-30
DOI
https://doi.org/10.47065/bits.v6i3.6316
Primary Topic
Data Mining and Machine Learning Applications
Type
article
Field-Weighted Citation Impact
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article

Penerapan Metode K-Means Untuk Pengelompokkan Data Pelaporan Di Kantor Urusan Agama

Nur Hayatin Nufus, Sriani Sriani
Building of Informatics Technology and Science (BITS)
Data Mining and Machine Learning Applications
article

Penerapan Metode K-Means Untuk Pengelompokkan Data Pelaporan Di Kantor Urusan Agama

Nur Hayatin Nufus, Sriani Sriani
article en

Abstract

The process of reporting marriages at the Religious Affairs Office (KUA) in Laubaleng District is still done manually, which results in obstacles in the management, accuracy and efficiency of data access. This research aims to overcome this problem through developing a system based on the K-Means clustering method. This system is designed to group marriage reporting data based on attributes such as age, marital status, and month of marriage, so as to provide a more structured and informative data pattern. The Elbow method is used to determine the optimal number of clusters, while the K-Means algorithm is applied using Euclidean distance to calculate the closeness of the data to the centroid. The research process involves collecting reporting data from 2019 to 2024, data preprocessing, normalization, and evaluating clustering results using the Davies-Bouldin Index (DBI). The research results show that the K-Means method is effective in grouping data, providing clear visualization of the distribution of marriage patterns, and increasing the efficiency of data management at KUA. With this system, KUA can increase access speed, reduce the potential for errors, and support more accurate data-based decision making.

Building of Informatics Technology and Science (BITS)Vol. 6(3)
Universitas Islam Negeri Sumatera Utara (ID)
Openalex Percentile: Top 53%
Data Mining and Machine Learning Applications
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