Article Per Year (5 Year)
p-Index From 2019 - 2024
0.61
P-Index
This Author published in this journals
All Journal Pixel : Jurnal Ilmiah Komputer Grafis Jurnal Ilmiah FIFO Jurnal Abdimas Prakasa Dakara
Zico Pratama Putra
Universitas Nusa Mandiri
Humanities Computer Science & IT Education Social Sciences

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

Found 3 Documents
Search

 1 
Analisis Desain Software Process Improvement Untuk Organisasi Pengembang Perangkat Lunak Skala Usaha Kecil Ade Priyatna; Kukuh Panggalih; Deny Robyanto; Zico Pratama Putra
Pixel :Jurnal Ilmiah Komputer Grafis Vol 15 No 1 (2022): Vol 15 No 1 (2022): Jurnal Ilmiah Komputer Grafis
Publisher : STEKOM PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/pixel.v15i1.759

Abstract

The development of software today in large and small organizations encourages every organization to develop and control in terms of development. Every organization in software development must grow and be able to improve itself, one of which is by handling advancement, if the company cannot do this. Therefore, the company will never be able to learn to take advantage of previous experience, so it will not be able to improve the quality of the existing process. Computer program Prepare Advancement can be done by referring to the CMMI (Capability Development Demonstrate Integration) made by SEI (Computer Program Designing Founded). SEI is a research center in the field of program designing, especially those related to procurement, engineering item lines, and prepare change. CMMI itself defines several levels of development, in order to go up to a higher level a number of processes must be carried out.
Penyuluhan Literasi Media untuk Bijak di Media Sosial dan Pemanfaatan Media Digital Dwiza Riana; Agus Subekti; Hilman F. Pardede; Zico Pratama Putra; Faruq Aziz
Jurnal Abdimas Prakasa Dakara Vol. 2 No. 2 (2022): Literasi Media dan Promosi Kreatif dalam Kegiatan Kemasyarakatan
Publisher : LPPM STKIP Kusuma Negara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37640/japd.v2i2.1522

Abstract

Understanding the power of media must be promoted at all levels. Efforts to develop media literacy, both in the form of thoughts and in conducting outreach activities, need to be carried out and supported by various stakeholders. Especially in the current era of digital media, people are used to and easily access social media. There is also growing concern about the negative impact of social media use on young people. Therefore, it is necessary to teach the younger generation media skills to use social media. This is the basis for making joint activities aimed at educating the younger generation to be wise in using social media and being able to use digital media well. This activity took place on April 3, 2022 with a total of 15 participants. Based on the results of the activities carried out, the application of positive communication resulted in positive changes in the insights, knowledge, skills, values, and attitudes of adolescents, and this activity has important benefits for community activities. to successfully achieve the goals and benefit the community, especially the partners of the SIGMA Foundation.
Evaluating the Performance of Classification Algorithms on the UNSW-NB15 Dataset for Network Intrusion Detection Zico Pratama Putra
Jurnal Ilmiah FIFO Vol 16, No 1 (2024)
Publisher : Fakultas Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/fifo.2024.v16i1.009

Abstract

 Network intrusion detection is a critical aspect of cybersecurity, aiming to distinguish between normal and malicious network activities. This study evaluates the performance of various machine learning algorithms on the UNSW-NB15 dataset for binary classification of network traffic into normal and attack categories. We employed several preprocessing steps, including handling missing values, encoding categorical features, and addressing class imbalance using a mix of Synthetic Minority Over-sampling Technique (SMOTE) and undersampling. The models evaluated include k-Nearest Neighbors (k-NN), Naive Bayes, Logistic Regression, Support Vector Machines (SVM), and Neural Networks. Our experimental results show that complex models like Neural Networks and SVMs significantly outperform simpler models. The Neural Network model achieved the highest accuracy of 92%, with a precision of 91%, recall of 93%, and an F1-score of 92%. SVM also performed robustly with an accuracy of 90%. Simpler models, while less effective, still achieved respectable performance, with Logistic Regression and k-NN reaching accuracies of 88% and 85%, respectively. The study highlights the importance of comprehensive preprocessing and the implementation of advanced machine learning techniques for effective network intrusion detection. The results suggest that while complex models offer superior detection capabilities, simpler models can still be valuable in resource-constrained environments. Future research should focus on applying these models to real-world data, exploring more advanced neural network architectures, and implementing cost-sensitive learning techniques to further enhance detection performance and efficiency.
Co-Authors Ade Priyatna Agus Subekti Deny Robyanto Dwiza Riana Faruq Aziz Hilman F. Pardede Kukuh Panggalih