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All Journal Jurnal Pengabdian Masyarakat (ABDIRA) Julia Jurnal
Rheimanda Devin Emmanuel
Universitas An Nuur
Aerospace Engineering Computer Science & IT Languange, Linguistic, Communication & Media Nursing Social Sciences Other

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AI-BAHSI: Metode Hibrid Artificial Intelligence-Behavioral Analysis dan Hybrid Security Intelligence untuk Deteksi dan Mitigasi Ancaman Real-time pada Wireless Access Point Rheimanda Devin Emmanuel; Ani Anggraini; Agus Condro Wibowo; Kartika Imam Santoso; Eko Supriyadi
Julia: Jurnal Ilmu Komputer An Nuur Vol 5 No 2 (2025): julia.ejournal.unan.ac.id
Publisher : LPPM Universitas An Nuur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35720/julia.v5i2.30

Abstract

Wireless access point (AP) security faces significant challenges with the emergence of sophisticated attacks such as SSID Confusion (CVE-2023-52424), KRACK attacks, and advanced persistent threats. This research develops a hybrid AI-BAHSI (Artificial Intelligence-Behavioral Analysis and Hybrid Security Intelligence) method that integrates deep learning, ensemble machine learning, and federated learning for real-time threat detection and mitigation on wireless access points. The proposed method combines Convolutional Neural Network-Long Short Term Memory (CNN-LSTM) for pattern recognition, Random Forest-Support Vector Machine ensemble for threat classification, and federated learning for privacy-preserving security intelligence. Evaluation was conducted on a synthetic dataset that includes 15,000 normal traffic samples and 8,500 attack samples of various types. The results show that AI-BAHSI achieves a detection accuracy of 98.7%, a precision of 97.3%, a recall of 98.1%, and an F1-score of 97.7% with a false positive rate of only 1.2%. This method successfully detected zero-day attacks with a 94.6% confidence level and was able to automatically mitigate them in an average of 0.8 seconds. The main contribution of this research is the development of an adaptive security framework that can learn from new attack patterns in real time while preserving privacy through a federated learning architecture.
Implementasi Insinerator Rocket Stove untuk Pembakaran Limbah Rumah Tangga di Desa Ketitang Estuningtyas Ayu Hapsari; Rheimanda Devin Emmanuel; Risa Kurmawati; Sri Utami; Jaunatun Nafsi Nafisah; Nofia Ayu Aszhary; Rendi Ary Margyanto; Siti Nur Widyaningsih; Diva Putri Maharani; Imel Imel
Jurnal Pengabdian Masyarakat (ABDIRA) Vol 6, No 4 (2026): Abdira
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/abdira.v6i4.1901

Abstract

The problem of household waste management in Ketitang Village, Godong District, Grobogan Regency, is triggered by low public awareness, limited infrastructure, and the habit of littering and open burning which results in environmental pollution and health risks. An Nuur University's KKN Group 9 uses the Participatory Action Research (PAR) approach, conducting socialization, cooperative training to build a rocket stove incinerator from local materials (hebel bricks and iron), trial runs, and continued mentoring.  This technology burns dry waste at temperatures above 600°C efficiently, with a reduction in smoke emissions of up to 60-80% and a minimum volume of waste to ash. The results include increased awareness of the 3R principle (Reduce, Reuse, Recycle), community participation in RW 01-02, and the ability to manage independently sustainably. This innovation is an environmentally friendly model that deserves to be replicated with routine maintenance and support from the village government to create a clean and healthy environment.
Co-Authors Agus Condro Wibowo Ani Anggraini Diva Putri Maharani Eko Supriyadi Estuningtyas Ayu Hapsari Imel Imel Jaunatun Nafsi Nafisah Kartika Imam Santoso Nofia Ayu Aszhary Rendi Ary Margyanto Risa Kurmawati Siti Nur Widyaningsih Sri Utami