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Jurnal INFOTEL
Published by Universitas Telkom
ISSN : 20853688     EISSN : 24600997     DOI : https://doi.org/10.20895/infotel.v15i2
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering
Jurnal INFOTEL is a scientific journal published by Lembaga Penelitian dan Pengabdian Masyarakat (LPPM) of Institut Teknologi Telkom Purwokerto, Indonesia. Jurnal INFOTEL covers the field of informatics, telecommunication, and electronics. First published in 2009 for a printed version and published online in 2012. The aims of Jurnal INFOTEL are to disseminate research results and to improve the productivity of scientific publications. Jurnal INFOTEL is published quarterly in February, May, August, and November. Starting in 2018, Jurnal INFOTEL uses English as the primary language.
Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)
Articles 12 Documents
 1   2 
Search results for , issue "Vol 17 No 3 (2025): August" : 12 Documents clear
Memeriksa Mekanisme Perhatian dalam Hybrid Deep Learning untuk Analisis Sentimen di Seluruh Panjang Teks Livia Naura Aqilla; Yuliant Sibaroni
JURNAL INFOTEL Vol 17 No 3 (2025): August
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v17i3.1396

Abstract

Sentiment analysis is a key task in natural language processing (NLP) with applications in a wide range of domains. This study examines the impact of self-attention and global attention placement in CNN-BiLSTM and CNN-LSTM models, exploring their effectiveness when positioned before, after or both before and after BiLSTM/LSTM, particularly for texts of different lengths. Instead of applying attention mechanisms in a fixed position, this research explores the most suitable type and placement of attention to improve model understanding and adaptability across datasets with different text lengths. Experiments were conducted using the IMDB Movie Reviews Dataset and the Twitter US Airline Sentiment dataset. The results show that for long texts, CNN-BiLSTM with self-attention before and after BiLSTM achieves an F1 score of 93. 77% (+2. 72%), while for short texts, it reaches 82.70% (+2.24%). These findings highlight that optimal attention placement significantly improves sentiment classification accuracy. The study provides insights into designing more effective hybrid deep learning models. It contributes to future research on multilingual and multi-domain sentiment analysis, where attention mechanisms can be adapted to different textual structures.
Enhancing SDN Controller Resilience to DDoS Attacks with IAT-Based Detection on CICIoT2023 Muhammad Agung Nugroho; Rikie Kartadie
JURNAL INFOTEL Vol 17 No 3 (2025): August
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v17i3.1414

Abstract

This study addresses the vulnerability of Software-Defined Networking (SDN) controllers to Distributed Denial of Service (DDoS) attacks, a critical issue for secure smart city and e-government applications. Using the CICIoT2023 dataset. Methods: We employed Random Forest with Recursive Feature Elimination and Cross-Validation (RFECV) to identify critical features for DDoS detection, validated through simulations in a Mininet/ONOS environment. Results reveal Inter-Arrival Time (IAT) as the most significant feature (importance score: 0.3200), with Controller Resources being the most vulnerable component (vulnerability score: 0.9048). DDoS-ICMP_Flood was the most effective attack (vulnerability score: 1.00), while Controller Distribution achieved a mitigation effectiveness of 0.9048. This research introduces a novel temporal feature-based detection approach, outperforming volume-based methods, and proposes adaptive mitigation strategies for SDN resilience. These findings enhance secure SDN deployment in dynamic IoT-driven environments.

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