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All Journal Rekayasa Mesin International Journal of Advances in Intelligent Informatics Sebatik Edsence: Jurnal Pendidikan Multimedia Specta Journal of Technology Buletin Poltanesa Brilliance: Research of Artificial Intelligence Prisma Sains: Jurnal Pengkajian Ilmu dan Pembelajaran Matematika dan IPA IKIP Mataram Journal of Internet and Software Engineering Ininnawa: Jurnal Pengabdian Masyarakat
Bima Prihasto
Institut Teknologi Kalimantan
Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Environmental Science Mathematics Mechanical Engineering Physics Public Health Social Sciences Other

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Robust Voice Anti-Spoofing for Indonesian Datasets Using Spectro-Temporal Graph Attention Networks Bima Prihasto; Boby Mugi Pratama; Adinia Amaliah
Poltanesa Vol 27 No 1 (2026): June 2026
Publisher : P3KM Politeknik Pertanian Negeri Samarinda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51967/tanesa.v27i1.3841

Abstract

As voice biometric systems face escalating threats from deepfake audio, securing non-English languages remains a critical vulnerability. This exploratory study aims to develop a localized Indonesian-language spoofing detection baseline, acknowledging constraints of a small, imbalanced dataset. We propose an approach combining AASIST spectro-temporal graph attention networks with a SAMO multi-center one-class learning loss function. Initial evaluations revealed substantial class overlap without acoustic perturbation, yielding a baseline equal error rate of 48.9%, highlighting under-resourced language vulnerabilities. However, an ablation study injecting controlled Gaussian noise acted as an optimal stochastic regularizer, significantly clarifying the decision boundaries between genuine and spoofed speech. This minor perturbation reduced the equal error rate to 44.05%, whereas excessive noise predictably destroyed the fundamental acoustic structure. These findings demonstrate that regularized multi-center geometries isolate synthetic artifacts, establishing a foundational proof-of-concept and highlighting the need for expanded corpora when securing Indonesian voice authentication infrastructures against advanced generative attacks.
Ambulance Siren Audio Classification Using Convolutional Neural Network for Medical Emergency Detection Ramadhan Paninggalih; Bima Prihasto; Maryo Inri Pratama; Rizky Irswanda Ramadhana; Misbahuddin Misbahuddin; Buan Anshari; Lalu Ahmad Syamsul Irfan Akbar; Giri Wahyu Wiriasto
Prisma Sains : Jurnal Pengkajian Ilmu dan Pembelajaran Matematika dan IPA IKIP Mataram Vol. 14 No. 2: April 2026
Publisher : Universitas Pendidikan Mandalika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33394/j-ps.v14i2.20099

Abstract

The rapid detection of emergency vehicle sirens is critical for enhancing road safety and traffic management. This study proposes an automated classification system for ambulance sirens using a Convolutional Neural Network (CNN). The method utilizes Mel-Frequency Cepstral Coefficients (MFCC) to transform audio signals into 2D feature maps, allowing the model to capture distinct spectral and temporal patterns. The dataset was preprocessed using a stratified split to ensure balanced class distribution and prevent data leakage. Experimental results demonstrate that the CNN model achieves a high performance with an accuracy of 0.95, significantly outperforming baseline models such as Multi-Layer Perceptron (MLP) and XGBoost. Detailed evaluation through a confusion matrix indicates a consistent precision, recall, and F1-score of 0.95, proving the model’s robustness in distinguishing sirens from complex urban noise. The implementation of the Adam optimizer and early stopping mechanism ensured stable convergence and prevented overfitting. These findings suggest that the proposed CNN-MFCC framework provides a reliable solution for real-time emergency signal detection, offering a substantial contribution to intelligent transportation systems.
Pose-Based Suspicious Activity Detection Using YOLOv12n and MediaPipe in Smart Surveillance Systems Bowo Nugroho; Bima Prihasto; Aninditya Anggari Nuryono; Ahmad Rusdianto Andarina Syakbani; Nawfal Alhadi; Rionando Soeksin Putra; Ilham Al Basith
Journal of Internet and Software Engineering Vol 7 No 1 (2026): Journal of Internet and Software Engineering
Publisher : Department of Electrical Engineering and Informatics, Vocational College, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/jise.v7i1.31998

Abstract

Detecting suspicious activity in video surveillance is critical for public safety. Early identification of pre-incident behaviors, such as "looking around" nervously, can prevent crimes before they occur. However, traditional surveillance relies on continuous human monitoring, which is prone to error and resource-intensive. Existing automated methods struggle with real-time stability and produce flickering predictions. We present a two-stage pose-based detection system: YOLOv12n detects humans in each frame, and MediaPipe Holistic extracts pose features. A Support Vector Classifier (SVC) and Neural Network (NN) then classify 15-frame temporal segments as either "still" or "looking around." On our custom dataset, SVC achieves 97.35% accuracy while NN achieves 97.00%. Temporal smoothing eliminates prediction flickering, enabling stable real-time deployment. SVC provides better precision-recall balance, NN offers greater adaptability to complex pose variations.
Co-Authors Adinia Amaliah Ahmad Rusdianto Andarina Syakbani Al Khairy, Rafid Alwafi, Fauzan M. Alwafi, Fauzan Maftuh Anindita Septiarini, Anindita Aninditya Anggari Nuryono Anita Ahmad Kasim Askarin, Tsaqila B. Azhar, Nur Fajri Boby Mugi Pratama Bowo Nugroho Buan Anshari Chairunnisa Ardiansyah Lamasitudju Darmansyah Devy Setiorini Sa’adiyah Edia, Naflah Shafa Ekawati, Herliana Nur Fadhliana, Nisa Rizqiya Febriandini, Nanda Clariza Firman Bagja Juangsa Giri Wahyu Wiriasto Hariyani, Agustina Harry Tanni Pagiu Himawan, Kevin Ilham Al Basith Indira Anggriani Lalu Ahmad Syamsul Irfan Akbar Lestari , Ika Lukman Nadjamuddin Mamola, Randi Marianta, Arwin Muhammad Akbar Muhammad Bakri Muhammad Insan Kamil, Muhammad Insan Nadhira Rizqana Nur Salsabila Nawfal Alhadi Nur Fajri Azhar Nur Farid, Mifta Prahmana, Rico Aditia Pratama, Abdul Rizal Pratama, Maryo Inri Pratama, Raditya Hendra Puguh Budi Prakoso Rajab, Nur Ali Ramadhan Paninggalih Rionando Soeksin Putra Riyan Benny Sukmara Rizky Irswanda Ramadhana Samsu Dlukha N Sari, Yustika Perwita Suardi Suardi Syakbani, Ahmad Rusdianto Andarina Winarni Yuda, Dastin Pranata