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Optimisation of Criminal Data Clustering Model using Information Gain Prih Diantono Abda’u; Ratih Hafsarah Maharrani; Muhammad Nur Faiz; Oman Somantri
Journal of Innovation Information Technology and Application (JINITA) Vol 7 No 1 (2025): JINITA, June 2025
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/jinita.v7i1.2741

Abstract

Crime is a phenomenon that significantly impacts society, necessitating mapping efforts that can be utilized for further analysis. Clustering, as a data analysis technique, groups objects based on similarities or differences in their characteristics. This approach enhances the understanding of data by identifying patterns and relationships between criminal events, such as crime type, time, and location. By clustering crime data based on similar characteristics, authorities can make more effective and efficient decisions in crime prevention and control. However, selecting too many attributes can negatively affect clustering performance. To address this issue, this study applies Information Gain reduction to reduce data dimensionality by eliminating attributes with low informational contribution. Additionally, three clustering methods K-Medoid, K-Means, and X-Means are compared to evaluate their performance. The concept of Information Gain is also integrated to optimize cluster formation, measuring how much an attribute contributes to distinguishing objects within a cluster. By leveraging Information Gain, this study aims to identify the most relevant and influential attributes in forming clusters that accurately represent crime data characteristics. Furthermore, the number of clusters generated is evaluated using the Davies-Bouldin Index (DBI). The results indicate that the K-Means algorithm outperforms the other two methods, achieving the best clustering quality with an optimal number of clusters (k = 6) and the lowest DBI value.
Co-Authors Abdul Rohman Supriyono Abdul Rohman Supriyono Agus Susanto Agus Susanto Ali Sofyan Amir Hamzah Andesita Prihantara Annisa Romadloni Ari Kristiningsih Arif Wirawan Muhammad Ayu Pramita Catur Supriyanto Dairoh Dairoh Dairoh Dairoh, Dairoh Dany Artha Widiyanto Dega Surono Wibowo Dega Surono Wibowo, Dega Surono Dodi Satriawan Dwi Wahyu Susanti Dyah Apriliani Dyah Apriliani Dyah Apriliani Edhy Sutanta (Jurusan Teknik Informatika IST AKPRIND Yogyakarta) Eka Tripustikasari Eko Nugroho, Wildani Erna Alimudin Evila Purwanti Sri Rahayu, Theresia Fadillah Fadillah Fadlilah, Ilma Faulin, Muhammad Husni Ganjar Ndaru Ikhtiagung Ginanjar Wiro Sasmito, Ginanjar Wiro Hety Dwi Astuti Ida Afriliana Ika Dewi Rozaurrohmah Iyat Ratna Komala Johanna, Anne Karyati, Titin Khoeruddin Wittriansyah Laura Sari Lina Puspitasari Linda Perdana Wanti Linda Perdana Wanti Linda Perdana Wanti Linda Perdana Wanti Lutfi Syafirullah Maharrani, Ratih Hafsarah Mohammad Khambali, Mohammad Muchamad Sobri Sungkar, Muchamad Sobri Muhammad Nur Faiz Muhammad Nur Faiz Musyafa Al Farizi Nur Wachid Adi Prasetya Nurlinda Ayu Triwuri Oto Prasadi Perdana Wanti, Linda Prih Diantono Abda'u Prih Diantono Abda’u Prih Diantono Abda`u Prihandoyo, Teguh Purwaningrum, Santi Ratih Hafsarah Maharrani Ratih Hafsarah Maharrani Ratih Hafsarah Maharrani Riyadi Purwanto Riyanto Riyanto Rohayah, Siti Santi Purwaningrum Santi Purwaningrum Sari, Laura Sasmito, Ginanjar Wiro Sena Wijayanto Sofyan, Ali Taufiq Abidin Taufiq Abidin Taufiq Abidin, Taufiq Teguh Prihandoyo Titin Kartiyani Titin Kartiyani Wanti, Linda Perdana Wildani Eko Nugroho Wildani Eko Nugroho, Wildani Eko Wiyono, Slamet Yeni Priatna Sari, Yeni Priatna