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All Journal Specta Journal of Technology Prisma Sains: Jurnal Pengkajian Ilmu dan Pembelajaran Matematika dan IPA IKIP Mataram
Ramadhana, Rizky Irswanda
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Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Environmental Science Mathematics Physics Other

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EVOLUTION OF RESEARCH AND COMMUNITY SERVICE INFORMATION SYSTEM (SIMPAS LPPM) INSTITUT TEKNOLOGI KALIMANTANKALIMANTAN INSTITUTE OF TECHNOLOGY Azhar, Nur Fajri; Nugroho , Bowo; Alfani Putera, M. Ihsan; Kairupan, Muhammad Nasa'i; Ramadhana, Rizky Irswanda
SPECTA Journal of Technology Vol. 9 No. 2 (2025): Specta Journal of Technology
Publisher : LPPM ITK

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35718/specta.v9i2.1309

Abstract

The Research and Community Service Information System (SIMPAS) at the Institute for Research and Community Service (LPPM) of the Kalimantan Institute of Technology (ITK) has evolved since it was introduced in 2018 to improve efficiency and transparency in managing research and community service activities. This research focuses on the development of SIMPAS through technical updates and the addition of features to meet increasingly complex user needs. This evolution includes updating the system environment using the latest version of Laravel, database refactoring to simplify data processing, and adding features such as account registration using Google email and more flexible user role management. This research uses the Scrum methodology in system development, which allows for continuous improvement and focuses on collaboration between teams. The results of this research are expected to optimize the main business functions of LPPM ITK in the process of submitting proposals, selection, to reporting research and community service results, which in turn supports the achievement of the Tri Dharma of Higher Education and regional development. This research also discusses the integration of the system with other related platforms, as well as the potential use of modern technology to support operational security and efficiency.
Ambulance Siren Audio Classification Using Convolutional Neural Network for Medical Emergency Detection Paninggalih, Ramadhan; Prihasto, Bima; Pratama, Maryo Inri; Ramadhana, Rizky Irswanda; Misbahuddin, Misbahuddin; Anshari, Buan; Akbar, Lalu Ahmad Syamsul Irfan; Wiriasto, Giri Wahyu
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.
Co-Authors Alfani Putera, M. Ihsan Azhar, Nur Fajri Buan Anshari Giri Wahyu Wiriasto Irfan Akbar, L. Ahmad Syamsul Kairupan, Muhammad Nasa'i Misbahuddin, Misbahuddin Nugroho , Bowo Paninggalih, Ramadhan Pratama, Maryo Inri Prihasto, Bima