Article Per Year (5 Year)
p-Index From 2021 - 2026
0.659
P-Index
This Author published in this journals
All Journal JURIKOM (Jurnal Riset Komputer) Infotekmesin
Nur Faiz, Muhammad
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Mechanical Engineering

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
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.
Eksplorasi Teknik Pre-Processing Berbasis eXtreme Gradient Boosting (XGBoost) pada Serangan DDoS Nur Faiz, Muhammad; Sari, Laura; Imam Riadi; Arif Wirawan Muhammad; Sukma Aji
JURNAL RISET KOMPUTER (JURIKOM) Vol. 12 No. 6 (2025): Desember 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i6.9380

Abstract

Distributed Denial of Service (DDoS) attacks represent a critical threat to modern network security, particularly within Internet of Things (IoT) environments characterized by large-scale and heterogeneous traffic patterns. The primary challenges in detecting such attacks involve class imbalance, irrelevant features, and noise within the data, all of which can degrade the performance of machine learning-based detection models. This study evaluates the impact of a pre-processing pipeline—comprising the Synthetic Minority Over-sampling Technique (SMOTE), correlation-based feature selection, and advanced feature selection methods—on the performance of the XGBoost algorithm in detecting DDoS attacks using the CIC-IoT2023 dataset. Experimental results indicate that the XGBoost model trained on RAW data achieves exceptionally high performance, with an accuracy of 0.999983, precision of 0.985531, recall of 0.961390, and an F1-score of 0.999983. However, after applying the pre-processing techniques, all metrics experienced a decline, with accuracy decreasing to 0.958899, precision to 0.865729, recall to 0.748332, and the F1-score to 0.959158. The reduction in recall suggests a higher number of undetected attacks, whereas the drop in precision indicates an increase in false alarms. Nevertheless, the F1-score remaining above 0.95 demonstrates that the model continues to perform effectively overall. These findings reveal that pre-processing does not always lead to performance improvements, especially when the raw dataset is already relatively clean and balanced. This study provides deeper insights into how SMOTE, feature selection, and noise injection influence the generalization of XGBoost on IoT traffic, and emphasizes that the effectiveness of pre-processing is highly dependent on dataset characteristics and the intended application context of intrusion detection systems.
Co-Authors Arif Wirawan Muhammad Diantono Abda’u, Prih Dwi Fatimah, Anggita Imam Riadi Isa Bahroni, Isa Nur Chasanah Perdana Wanti, Linda Sari, Laura Sukma Aji Supriyono, Abdul Rohman