Analisis Data Judi Online di 5 Provinsi Indonesia Dengan Metode K-Means dan Decision Tree

This study aims to analyze online gambling data detected in five provinces in Indonesia using the K-Means and Decision Tree methods. The data includes player counts, transaction values, and geographical distribution in West Java, Jakarta, Central Java, Banten, and East Java. The K-Means method was applied to cluster provinces based on player counts and transaction values, while the Decision Tree was used to identify classification rules. The results reveal three main clusters with distinct characteristics: provinces with high player counts and high transactions, provinces with low player counts and moderate transactions, and provinces with moderate player counts but low transactions. These findings provide critical insights into the patterns of online gambling activities in Indonesia and serve as a foundation for more effective policies in managing its impacts.

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Publication Details

Journal
TAMIKA Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi
Published
2024-12-31
DOI
https://doi.org/10.46880/tamika.vol4no2.pp254-258
Primary Topic
Data Mining and Machine Learning Applications
Type
article
Field-Weighted Citation Impact
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article

Analisis Data Judi Online di 5 Provinsi Indonesia Dengan Metode K-Means dan Decision Tree

Nor Anisa, Muhammad Bayu Saputra
TAMIKA Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi
Data Mining and Machine Learning Applications
article

Analisis Data Judi Online di 5 Provinsi Indonesia Dengan Metode K-Means dan Decision Tree

Nor Anisa, Muhammad Bayu Saputra
article en

Abstract

This study aims to analyze online gambling data detected in five provinces in Indonesia using the K-Means and Decision Tree methods. The data includes player counts, transaction values, and geographical distribution in West Java, Jakarta, Central Java, Banten, and East Java. The K-Means method was applied to cluster provinces based on player counts and transaction values, while the Decision Tree was used to identify classification rules. The results reveal three main clusters with distinct characteristics: provinces with high player counts and high transactions, provinces with low player counts and moderate transactions, and provinces with moderate player counts but low transactions. These findings provide critical insights into the patterns of online gambling activities in Indonesia and serve as a foundation for more effective policies in managing its impacts.

TAMIKA Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi AkuntansiVol. 4(2)
Universitas Sari Mutiara Indonesia (ID)
Openalex Percentile: Top 54%
Data Mining and Machine Learning Applications
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