Article Per Year (5 Year)
p-Index From 2021 - 2026
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This Author published in this journals
All Journal Journal of Computer Networks, Architecture and High Performance Computing Journal of Vocational, Informatics and Computer Education
Erni Marlina
Dipa Makassar University
Computer Science & IT Education

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The Design of Smart Prototype Pet Feeder Using Passive InfaRed (PIR) Sensors Erni Marlina
Journal of Computer Networks, Architecture and High Performance Computing Vol. 5 No. 1 (2023): Article Research Volume 5 Issue 1, January 2023
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v5i1.2237

Abstract

Food is necessary to support daily life and in pets like cats. Pet feeders are usually carried out routinely by pet owners by taking the time to stay at home and feed the pets. With activities requiring animal owners to be outside the home for long periods, it is necessary to design a prototype that can automatically assist the process of providing food, especially dry food, to pets. So pet feeders can be carried out even though the owner is not at home and does not have spare time. The system was built using the C language related to the Code Vision tools that support the hex file compiler into the microcontroller. The research method used is the experimental method, namely conducting trials (trial and error) directly on the research object and the comparative testing method for testing the built system. The auto pet feeder prototype uses the ATMega8535 IC by utilizing a PIR (Passive InfraRed) sensor to detect movement around the animal bowl. The PIR sensor detects well as expected, and it has an effective detection range of up to 5 meters. Not only detects humans, but the PIR sensor can also detect other living things. The PIR sensor will send a signal to the microcontroller to open the valve from the food measuring station, which is driven by a DC motor based on the detected motion to drop food into the animal's bowl. PhotoDioda controls the amount of food falling into the animal's bowl. The test results of this tool show that at a distance of 0–6 meters, the PIR sensor can provide feedback or detect animal movements well.
An Adaptive Attention-Based Multimodal Deep Fusion Network for Robust Biometric User Authentication Nurdin; Indo Intan; St. Aminah Dinayati Gani; Nur Salman; Erni Marlina
Journal of Vocational, Informatics and Computer Education Vol 4, No 2 (2026): June 2026
Publisher : Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/voice.v4i2.448

Abstract

Purpose - The global rise in identity fraud and credential-based breaches, with average costs reaching USD 4.88 million, highlights the limitations of unimodal biometric authentication systems, which are vulnerable to spoofing attacks and environmental degradation. Existing multimodal approaches also rely on static weighting strategies that lack adaptability to adversarial conditions or degraded data quality.This study proposes a Multimodal Deep Fusion Network (MDFN), an end-to-end deep learning architecture that integrates three biometric modalities: facial (visual), voice (audio), and keystroke dynamics (behavioral). The MDFN employs three independent feature extraction streams—ResNet-18 for visual data, 1D CNN for audio data, and Bi-LSTM for behavioral data—fused through an Attention-based Adaptive Weighted Fusion mechanism that dynamically adjusts to data quality. Methods - The evaluation was conducted using the VGGFace2, VoxCeleb1, and CMU Keystroke datasets under both normal and spoofing attack scenarios, using metrics such as EER, FAR, FRR, AUC, and APCER. Findings - The results show that the MDFN achieves an EER of 1.12% and an FAR of 1.12%, significantly outperforming the unimodal baselines (best EER: 4.15%) and static fusion models (EER: 1.95%). The system also demonstrated strong robustness against spoofing, achieving an APCER as low as 0.8%. Research Implications - MDFN is an effective authentication solution for high-security environments. Originality - Its key contribution lies in the attention-based adaptive fusion mechanism, which dynamically adjusts the modality weights based on a real-time quality assessment.
Co-Authors Indo Intan Nur Salman Nurdin St. Aminah Dinayati Gani