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All Journal Infotekmesin Journal of Innovation Information Technology and Application (JINITA)
Diantono Abda’u, Prih
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Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Mechanical Engineering

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Optimisation of Criminal Data Clustering Model using Information Gain Diantono Abda’u, Prih; Maharrani, Ratih Hafsarah; Nur Faiz, Muhammad; Somantri, Oman
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.
Implementasi Metode Waterfall dalam Sistem Informasi Knowledge Management untuk Digital Marketing Chasanah, Nur; Diantono Abda’u, Prih; Nur Faiz, Muhammad
Infotekmesin Vol 12 No 1 (2021): Infotekmesin: Januari 2021
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v12i1.363

Abstract

The development of digital technology directs organizations to be able to cultivate knowledge as an asset that can help its business activities. Knowledge management is considered necessary to be implemented in organizations involving many stakeholders. The implementation of annual management needs to be implemented by involving the utilization. This research was conducted to implement knowledge management in organizations that involve many knowledge assets in the business process, especially related to digital marketing activities. The purpose of this research is to identify organizational knowledge and produce knowledge sharing media systems. This research uses waterfall method in the development of its system so that the result of this research is a knowledge management information system that facilitates knowledge sharing activities that have been identified and can facilitate users in finding data, information and knowledge that is useful in fostering innovations related to digital marketing in the organization.
Co-Authors Maharrani, Ratih Hafsarah Nur Chasanah Nur Faiz, Muhammad Oman Somantri