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All Journal Journal of Applied Data Sciences Journal of Community Service
Deshinta Arrova Dewi
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Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Industrial & Manufacturing Engineering Social Sciences

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Stacked LSTM with Multi Head Attention Based Model for Intrusion Detection S Phani Praveen; Padmavathi Panguluri; Uddagiri Sirisha; Deshinta Arrova Dewi; Tri Basuki Kurniawan; Lusiana Efrizoni
Journal of Applied Data Sciences Vol 7, No 1: January 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i1.764

Abstract

The rapid advancement of digital technologies, including the Internet of Things (IoT), cloud computing, and mobile communications, has intensified reliance on interconnected networks, thereby increasing exposure to diverse cyber threats. Intrusion Detection Systems (IDS) are essential for identifying and mitigating these threats; however, traditional signature-based and rule-based methods fail to detect unknown or complex attacks and often generate high false positive rates. Recent studies have explored machine learning (ML) and deep learning (DL) approaches for IDS development, yet many suffer from poor generalization, limited scalability, and an inability to capture both spatial and temporal dependencies in network traffic. To overcome these challenges, this study proposes a hybrid deep learning framework integrating Convolutional Neural Networks (CNN), Stacked Long Short-Term Memory (LSTM) networks, and a Multi-Head Self-Attention (MHSA) mechanism. CNN layers extract spatial features, stacked LSTM layers capture long-term temporal dependencies, and MHSA enhances focus on the most relevant time steps, improving accuracy and reducing false alarms. The proposed model was trained and evaluated on the UNSW-NB15 dataset, which represents modern attack vectors and realistic network behavior. Experimental results show that the model achieves state-of-the-art performance, attaining 99.99% accuracy and outperforming existing ML and DL-based intrusion detection systems in both precision and generalization capability.
USING AI TECHNOLOGY TO SUPPORT LEARNING AND BOOST STUDENT INDEPENDENCE AT SMA MUHAMMADIYAH PK SURAKARTA Muhammad Aliq Aulia; Kresno Ario Tri Wibowo; Irfan Nugraha; Deshinta Arrova Dewi
Journal of Community Service Vol 8 No 1 (2026): JCS, June 2026
Publisher : Ikatan Dosen Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56670/jcs.v8i1.399

Abstract

Background: The rapid development of Artificial Intelligence (AI) technology has significantly influenced the education sector, particularly at the senior high school level. However, preliminary observations at SMK Muhammadiyah PK Surakarta indicated that students’ understanding of AI was still limited prior to the implementation of this community service activity. Initial assessments showed that only about 20% of students had basic knowledge of AI concepts, including its functions, limitations, and ethical use in learning contexts. This gap between technology exposure and conceptual understanding highlights the importance of structured AI socialization to support learning and foster student independence. Objective: This community service activity aims to support learning processes and boost student independence by strengthening AI literacy through systematic socialization and educational activities. Method: The program was implemented through AI socialization sessions, interactive presentations, demonstrations of AI-based learning tools, and guided discussions. The activity was carried out through an international collaboration with INTI International University, with academic partnership support from Prof. Dr. Deshinta Arrova Dewi. The evaluation employed a one-group pre-test and post-test design to measure changes in students’ understanding of AI. Result: The post-test results indicated a substantial improvement in students’ understanding of Artificial Intelligence. The level of comprehension increased from approximately 20% before the socialization to 89% after the activity, reflecting an overall improvement of 69%. This improvement demonstrates that AI socialization effectively enhances students’ digital literacy, learning engagement, and capacity for independent learning. Conclusion: These findings indicate that AI socialization is essential and effective for senior high school students. The community service activity focusing on Using AI Technology to Support Learning and Boost Student Independence can be well accepted by the target participants and has strong potential to be implemented sustainably as an innovative learning support model in secondary education.
Co-Authors Irfan Nugraha Kresno Ario Tri Wibowo Lusiana Efrizoni Muhammad Aliq Aulia Padmavathi Panguluri S Phani Praveen Tri Basuki Kurniawan Uddagiri Sirisha