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All Journal Infotekmesin Journal of Innovation Information Technology and Application (JINITA)
Nur Faiz, Muhammad
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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.
Metode Pengembangan Perangkat Lunak MDLC Pada Rancang Bangun Media Pembelajaran Planet Berbasis Teknologi Augmented Reality Supriyono, Abdul Rohman; Dwi Fatimah, Anggita; Bahroni, Isa; Perdana Wanti, Linda; Nur Faiz, Muhammad
Infotekmesin Vol 14 No 1 (2023): Infotekmesin: Januari, 2023
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v14i1.1689

Abstract

Along with the development of smartphones, Augmented Reality technology has begun to be used as a medium of interaction, although it has not been properly implemented and applied as a supporting medium. The use of still image objects in textbooks can make students tend to be more passive and less interactive because media images are unable to provide a reciprocal response. In science subjects, there is solar system material regarding planet recognition. Props are needed as learning media because the object of observation from the planet is too large. Several props are used as imitations of the planets, such as the use of drawing paper, audio, and video. The purpose of this research is to make a breakthrough in the use of Augmented Reality technology to support media for understanding planet recognition material by creating digital teaching aids that can be installed on smartphone devices. The MDLC method is an alternative method for developing multimedia applications that are easy to understand. The results of the test show that the application can function as expected, where each planetary marker that has been made can be recognized properly according to the intended planetary object.
Co-Authors Diantono Abda’u, Prih Dwi Fatimah, Anggita Isa Bahroni, Isa Maharrani, Ratih Hafsarah Nur Chasanah Oman Somantri Perdana Wanti, Linda Supriyono, Abdul Rohman