Article Per Year (5 Year)
p-Index From 2020 - 2025
0.444
P-Index
This Author published in this journals
All Journal Jurnal Pseudocode Bulletin of Social Informatics Theory and Application
Herwanto, Guntur Budi
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Social Sciences

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

Found 3 Documents
Search

 1 
Recommendation system for web article based on association rules and topic modelling Herwanto, Guntur Budi; Ningtyas, Annisa Maulida
Bulletin of Social Informatics Theory and Application Vol. 1 No. 1 (2017)
Publisher : Association for Scientific Computing Electrical and Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/businta.v1i1.36

Abstract

The World Wide Web is now the primary source for information discovery. A user visits websites that provide information and browse on the particular information in accordance with their topic interest. Through the navigational process, visitors often had to jump over the menu to find the right content. Recommendation system can help the visitors to find the right content immediately. In this study, we propose a two-level recommendation system, based on association rule and topic similarity. We generate association rule by applying Apriori algorithm. The dataset for association rule mining is a session of topics that made by combining the result of sessionization and topic modeling. On the other hand, the topic similarity made by comparing the topic proportion of web article. This topic proportion inferred from the Latent Dirichlet Allocation (LDA). The results show that in our dataset there are not many interesting topic relations in one session. This result can be resolved, by utilizing the second level of recommendation by looking into the article that has the similar topic.
Concerns for Digital Privacy in Business and Management: An overview and Future Discourses Recommendation Linando, Jaya Addin; Herwanto, Guntur Budi
Bulletin of Social Informatics Theory and Application Vol. 6 No. 2 (2022)
Publisher : Association for Scientific Computing Electrical and Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/businta.v6i2.508

Abstract

This paper aims to highlight the developing awareness of concern for digital privacy from business and management viewpoint. The authors compile data privacy literature in management field and visualize the literature into 4 main clusters of concerns. The 4 main cluster of concerns in data privacy discourse on management field are: internet; roles-trust-security; locations; and consumer privacy. This paper contributes on the development of research and discourse in data privacy and management domain. Besides delivering the overviews of the digital privacy concerns in business and management fields, the paper also places suggestions for future researchers.
Pendekatan Ensemble Learning untuk klasifikasi serangan DDoS Nurfajri, Muhammad Oriza; Herwanto, Guntur Budi
Jurnal Pseudocode Vol 12 No 2 (2025): Volume 12 Nomor 2 September 2025
Publisher : UNIB Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33369/pseudocode.12.2.39-46

Abstract

This research proposes an ensemble learning approach for classifying Distributed Denial of Service (DDoS) attacks using the CIC-DDoS2019 dataset. DDoS attacks remain a significant threat to network security, necessitating efficient detection methods. We developed an ensemble model combining Random Forest, Gradient Boosting, and AdaBoost classifiers to enhance detection accuracy. Our methodology involves preprocessing the CIC-DDoS2019 dataset, extracting relevant features, and implementing both binary classification (benign vs. attack) and multiclass classification (attack type identification). The experimental results show that our ensemble model achieves an F1-score of 0.9967 for binary classification, with Gradient Boosting performing best among individual models. The multiclass classification reaches an accuracy of 0.8742 in distinguishing between different types of DDoS attacks. This research demonstrates that ensemble learning significantly improves the accuracy and reliability of DDoS attack detection compared to single-model approaches. Keywords: Ensemble learning; DDoS attack; network security; CIC-DDoS2019; machine learning.
Co-Authors Linando, Jaya Addin Ningtyas, Annisa Maulida Nurfajri, Muhammad Oriza