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All Journal Insect (Informatics and Security) : Jurnal Teknik Informatika Journal of Information Systems and Informatics PROGRES PENDIDIKAN Conten : Computer and Network Technology Jurnal Pengabdian Pancasila (JPP) ANOA: Jurnal Pengabdian Masyarakat Fakultas Teknik Journal of Information Systems and Technology (JISTech) Global Journal of Educational Technology (GJET)
Ilcham, Ilcham
Universitas Sembilanbelas November Kolaka
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Engineering Environmental Science Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Physics Social Sciences Other

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Pendekatan Teknik Sosial dalam Edukasi DAGUSIBU untuk Peningkatan Kesehatan Masyarakat Desa Donggala Akbar, Muhammad Hajar; Rianti; Ramadani; Cahya Wulandari, Devita Sari; Akbar; Ilcham
Anoa : Jurnal Pengabdian Masyarakat Fakultas Teknik Vol 4 No 01 (2025): Edisi Juni Tahun 2025 ANOA : Jurnal Pengabdian Masyarakat Fakultas Teknik
Publisher : FAKULTAS TEKNIK UMKENDARI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51454/anoa.v4i01.1237

Abstract

The DAGUSIBU (Obtain, Use, Store, Dispose) medication counseling and education program in Donggala Village, Wolo District, Kolaka Regency, aims to enhance community knowledge and awareness of proper medication use. This initiative is motivated by the low level of public understanding regarding medication management, which often negatively impacts individual and environmental health. The program employs a participatory approach, involving lectures, discussions, simulations, and Q&A sessions, supported by a mixed-method research design combining qualitative and quantitative approaches. The results showed a significant increase in participants' understanding after the counseling, particularly in obtaining medications from official sources and using medications according to medical guidance. Direct practice simulations proved effective in facilitating behavioral change. Despite challenges such as varying levels of health literacy, the program successfully encouraged positive changes in daily medication use and highlighted the importance of continuous education. The DAGUSIBU education model can be applied in other areas to foster healthier and more responsible medication management practices within communities.
Hybrid CNN-RNN Architecture with MFCC-LFCC Features for Audio Deepfake Detection Muh. Hajar Akbar; Nurfitria Ningsi; Aldi; Muhammad Na’im Al Jum’ah; Ilcham
Journal of Information System and Informatics Vol 8 No 4 (2026): August
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i4.1723

Abstract

The proliferation of sophisticated audio deepfake technology poses a significant threat to digital voice authentication and forensic verification systems. This research addresses this challenge by developing and evaluating a lightweight hybrid Convolutional Neural Network and Recurrent Neural Network (CNN-RNN) architecture for audio deepfake detection. The proposed model integrates a CNN for spatial feature extraction with a bidirectional RNN for temporal dependency modeling, utilizing an early vertically fused feature set of Mel-Frequency Cepstral Coefficients (MFCC) and Linear Frequency Cepstral Coefficients (LFCC) stabilized via utterance-level Cepstral Mean and Variance Normalization (CMVN). We assessed the proposed framework on the official ASVspoof 2019 Logical Access (LA) evaluation benchmark dataset (71,237 trials). Comprehensive evaluation on the official evaluation set demonstrated promising performance within the evaluated benchmark, achieving a Global Equal Error Rate (EER) of 8.24% and an Area Under the Receiver Operating Characteristic (ROC-AUC) of 0.9680, while maintaining an internal validation EER of 0.33% on known attacks. While showing high sensitivity to bona fide speech and robust resilience against advanced Neural Text-to-Speech synthesis (EER < 0.25% for A07–A10), the framework exhibits notable vulnerability to phase-preserving voice conversion attacks. Consequently, without real-world forensic operational testing, the model serves as an initial diagnostic screening approach rather than a fully operational forensic solution, and still requires further cross-repository and noisy-condition validation.
Co-Authors Akbar Akbar, Muhammad Hajar Al Jum'ah, Muhammad Na'im Aldi Budi Wijaya Rauf Cahya Wulandari, Devita Sari Dian Puteri Nurbaity Elma Elma, Elma Hajar Hajar Hendra Nelva Saputra Hermansa, Hermansa Iyan Nurdiyan Haris Jimsan Jimsan Mardianto Mardianto Muh. Dhion. R Muh. Hajar Akbar Muhammad Akbar Yasin Muhammad Akbar Yasin Muhammad Irwan Syahib Nasrullah Nasrullah Nurfitria Ningsi Ramadani Rauf, Budi Wijaya Rianti Rifai, Sitti Najmia Rosti, Rosti Sitti Najmia Rifai Sitti Najmia Rifai Yantika, Mega Juni Yasin, Muhammad Akbar Yudhi Dwi Hartono