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All Journal Jurnal Pengabdian Masyarakat (ABDIRA) Edu Komputika Journal Julia Jurnal
Rheimanda Devin Emmanuel
Universitas An Nuur
Aerospace Engineering Computer Science & IT Education 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, Oktober
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.
E-Buyur Market: Fruit and Vegetable Quality Detection Platform Using On-Device Hybrid Artificial Intelligence Method to Reduce Food Loss in MSMEs Dita Prameswari; Rheimanda Devin Emmanuel; Mar Atus Solikhah; Erika Dwi Saputra; Sinta Susanti; Niqita Selviyanti; Rita Rahmawati
Edu Komputika Journal Vol. 13 No. 1 (2026): Edu Komputika Journal
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/edukom.v13i1.41313

Abstract

Indonesia produces large volumes of fruits and vegetables yet faces high food loss and waste (FLW), with studies estimating 23-48 million tons of food waste annually. Observations in Grobogan Regency show traders discarding around 30-40% of daily stock, often produce that is edible but visually imperfect (grade B/C). This study designs and evaluates E-Buyur Market, a digital marketplace embedding an on-device hybrid AI pipeline to automatically signal fruit-vegetable quality. The system combines a YOLO-TFLite commodity detector with a multitask MobileNetV3-Small TFLite grader to predict type, edibility, and a quality score derived from freshness probability. The model was trained on approximately 115,000 images compiled from publicly available Kaggle fruit-vegetable repositories, processed using data augmentation and post-training quantization for TensorFlow Lite deployment; a separate small set of real seller-captured photos was used to qualitatively test the deployed model under real usage conditions. On a held-out test set, the model achieves a type-classification accuracy of 0.91, freshness ROC-AUC of 0.94, PR-AUC of 0.92, and inference latency of tens of milliseconds on mid-range phones. Based on the system's design and signaling-theory rationale, on-device hybrid AI is expected to improve sell-through, reduce shrinkage, and shorten time-to-list for MSME sellers; empirical validation of these business-level outcomes through a live field trial remains future work.
Co-Authors Agus Condro Wibowo Ani Anggraini Dita Prameswari Diva Putri Maharani Eko Supriyadi Erika Dwi Saputra Estuningtyas Ayu Hapsari Imel Imel Jaunatun Nafsi Nafisah Kartika Imam Santoso Mar Atus Solikhah Niqita Selviyanti Nofia Ayu Aszhary Rendi Ary Margyanto Risa Kurmawati Rita Rahmawati Sinta Susanti Siti Nur Widyaningsih Sri Utami