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All Journal International Journal of Electrical and Computer Engineering Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Journal of Electronics, Electromedical Engineering, and Medical Informatics Jurnal Rekayasa elektrika PESARE: Science and Engineering Service Journal Jurnal Pengabdian Rekayasa dan Wirausaha
Yunida, Yunida
Department Of Electrical And Computer Engineering, Universitas Syiah Kuala, Indonesia
Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Environmental Science Public Health

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Improved Model of the Selection with Soft- and Hard-Combining Decoding Strategies for Multi-User Multi-Relay Cooperative Networks Nasaruddin Nasaruddin; Yunida Yunida; Khairul Munadi
International Journal of Electrical and Computer Engineering (IJECE) Vol 6, No 4: August 2016
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (367.895 KB) | DOI: 10.11591/ijece.v6i4.pp1766-1778

Abstract

In a wireless cooperative network, system reliability can be improved by introducing network coding (NC) for transmitting data packets from user to destination through relay nodes. At the destination, a decoding strategy is required to recover the original data packets. The use of NC in cooperative networks has been intensively studied in previous works in terms of the conventional model for two users and a single relay in a network. However, the network model cannot act as a virtual multiple-input multiple-output system, and a multi-user multi-relay network model could be used in a real system. Therefore, this paper proposes an improved model of two network decoding strategies, selection with soft combining (SSC) and selection with hard combining (SHC), for multi-user multi-relay cooperative networks. Users are classified based on their channel conditions, with better signal-to-noise (SNR) ratio sources being viewed as strong users, and others as weak or moderate users in the decoding strategies. To evaluate the performance of the proposed model, we first derive the bit error probability expressions for each strategy as a function of SNR and then evaluate the performance using numerical simulation for a Rayleigh fading channel. Simulation results show that SSC outperforms SHC. Furthermore, the improvement in network performance is achieved either by having a higher modulation level or using incremental relaying as the signal reception method at the destination.
Secrecy Capacity of Cooperative D2D Multi-relay Communication System with Multiple Protocols Based on Max-Min Relay Selection Nurul Maulida Fitri; Yunida Yunida; Melinda Melinda; Nasaruddin Nasaruddin
Jurnal Rekayasa Elektrika Vol 19, No 3 (2023)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17529/jre.v19i3.29486

Abstract

The utilization of other devices as relays in cooperative device-to-device (D2D) communication systems does not fully guarantee the security of confidential information from being intentionally or unintentionally accessed by eavesdroppers. Therefore, the implementation of a method to enhance the security performance of information is highly necessary. This paper proposes the application of relay selection mechanisms in a communication system with three relay protocols: Amplify-and-Forward (AF), Decode-and-Forward (DF), and Quantize-and-Forward (QF). The research method employs a mathematical modeling approach and simulations. The simulation results demonstrate an improvement in the level of information security in cooperative D2D communication systems using the proposed method in multiple relay protocols. The relay selection method has been evaluated and compared based on the Secrecy Outage Probability (SOP), which is one of the parameters for information security in the communication system. The SOP achieved is smaller with the implementation of the Max-Min relay selection technique in multi-relay cooperative communication networks. Considering the presence or absence of eavesdroppers, the SOP of the DF relay is smaller compared to other protocols. The impact of distance on secrecy capacity also indicates that the DF protocol utilizing multiple relays achieves higher results compared to other protocols, and the increased usage of relays also affects the simulation outcomes.
Improved Lung Sound Classification Model Using Combined Residual Attention Network and Vision Transformer for Limited Dataset Jurej, Muhammad; Roslidar, Roslidar; Yunida, Yunida
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 12, No 4: December 2024
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v12i4.5530

Abstract

According to WHO data, the prevalence of respiratory disorders is increasing, exacerbated by a shortage of skilled medical professionals. Consequently, there is an urgent need for an automated lung sound classification system. Current methods rely on deep learning, but limited lung sound data resulted in low model accuracy. The widely used ICBHI 2017 dataset has an imbalanced class distribution, with a normal class at 52.8%, wheezing at 27.0%, crackles at 12.8%, and combined wheeze and crackles at 7.3%. The imbalance of the dataset may affect the model's efficiency and performance in classifying lung sounds. Given these data limitations, we propose a hybrid model, combining residual attention network (RAN) and vision transformer (ViT), to construct an effective respiratory sound classification model with a small dataset. We employ feature fusion techniques between convolutional neural network (CNN) feature maps and image patches to enrich lung sound features. Additionally, our preprocessing involves bandpass filtering, resampling sounds to 16 kHz, and normalizing volume to 15 dB. Our model achieves impressive ICBHI scores with 97.28% specificity, 92.83% sensitivity, and an average score of 95.05%, marking a 10% improvement over state-of-the-art models in previous research.
Penerapan Komposter Pintar Berbasis IoT dan Energi Surya untuk Edukasi dan Reduksi Limbah Makanan di SMA Al-Mishbah Banda Aceh Utami, Rika Sri; Damanik, Yusuf Diva F; Habib, Muhammad; Rachmawati, Rachmawati; Yunida, Yunida
Jurnal Pengabdian Rekayasa dan Wirausaha Vol 2, No 1 (2025)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Pemborosan makanan merupakan tantangan lingkungan yang signifikan dan kerap terjadi di lingkungan sekolah akibat minimnya edukasi mengenai pengelolaan limbah organik. Berdasarkan data dari Bappenas, satu rumah tangga di Indonesia dapat menghasilkan hingga 1 kg limbah makanan per hari. SMA Al-Mishbah Banda Aceh menghadapi permasalahan serupa, yang ditangani melalui program pengabdian masyarakat bertajuk Nutri Revive: IoT Food Waste Composter. Program ini menerapkan komposter pintar berbasis IoT dan energi surya yang terhubung ke aplikasi mobile untuk memantau suhu, kelembapan, volume, dan status operasional secara real-time. Selama satu tahun, kegiatan meliputi perancangan alat, pelatihan kepada 40 siswa, pengumpulan sampah makanan, hingga integrasi sistem pemasaran berbasis dropshipping. Hasil pelaksanaan menunjukkan penurunan signifikan volume limbah, peningkatan keterampilan siswa dalam produksi dan distribusi kompos, serta terbukanya peluang kewirausahaan dengan skema insentif. Kolaborasi dengan pengusaha lokal seperti panglong kayu dan petani turut memperluas dampak lingkungan program. Monitoring dan evaluasi sistem menunjukkan efektivitas teknologi dalam meningkatkan efisiensi proses komposting. Program ini membuktikan bahwa integrasi teknologi hijau dan pemberdayaan pelajar dapat menjadi strategi berkelanjutan dalam membangun budaya sekolah yang ramah lingkungan.
Implementasi Rancang Bangun dan Instalasi Sistem PLTS untuk Penerangan pada Balai Pengajian Darul Hidayah Desa Deunong, Aceh Besar Tarmizi, Tarmizi; Fathurrahman, Fathurrahman; Yunida, Yunida
Jurnal Pengabdian Rekayasa dan Wirausaha Vol 1, No 2 (2024)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24815/jprw.v1i2.42917

Abstract

Desa Deunong yang terletak di Kecamatan Darul Imarah, Kabupaten Aceh Besar, merupakan salah satu desa binaan Universitas Syiah Kuala (USK) yang menghadapi tantangan signifikan dalam infrastruktur penerangan. Balai Pengajian Darul Hidayah, yang menjadi tempat utama kegiatan pengajian Al-Qur'an pada malam hari, tidak dapat difungsikan karena ketiadaan sistem penerangan. Untuk mengatasi permasalahan ini, dirancang dan dipasang sistem Pembangkit Listrik Tenaga Surya (PLTS) sebagai bagian dari program Pengabdian Kepada Masyarakat berbasis produk. Sistem ini dirancang di Laboratorium Sistem Tenaga Listrik USK dengan konfigurasi off-grid, terdiri dari 4 modul surya (100 Wp), 2 baterai seri dengan total kapasitas 200 Ah, dan inverter 3000 W, yang mampu memenuhi kebutuhan energi harian sebesar 1890 Wh. Proyek ini juga melibatkan pelatihan kepada mitra komunitas tentang instalasi dan pemeliharaan sistem untuk memastikan keberlanjutan. Sistem PLTS ini berhasil menyediakan penerangan bagi balai pengajian dan jalan akses menuju balai, sehingga kegiatan pengajian dapat kembali dilaksanakan dengan lancar. Inisiatif ini menunjukkan potensi solusi energi terbarukan dalam meningkatkan infrastruktur pedesaan dan mendukung pengembangan masyarakat.
Pembuatan dan Pemasangan Sistem Penerangan Panel Surya dalam Upaya Peningkatan Aktivitas Peternak Sapi di Desa Lampreh Kecamatan Ingin Jaya Kabupaten Aceh Besar Tarmizi, Tarmizi; Syahrial, Syahrial; Faturrahman, Faturrahman; Yunida, Yunida
PESARE: Jurnal Pengabdian Sains dan Rekayasa Vol 2, No 1 (2024): Februari 2024
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24815/pesare.v2i1.37736

Abstract

Lampreh Village is located in Lamteungoh settlement, Ingin Jaya District, Aceh Besar Regency, this village is one of the assisted villages of Syiah Kuala University. The main potential of this village is agriculture and cattle farming. Activities of cattle farmers such as feeding, cleaning cages, fumigating cows on the Krueng Aceh road in Lampreh Village are only done in the afternoon before the sun gets dark. The problem is that farmers along the Krueng Aceh road do not have lighting and there is no PLN electricity network. In this product-based Community Service, lighting from solar panels (PLTS) has been installed. This PLTS system has a solar panel capacity of 300 WP and a battery of 200 Ah. The total load is 84W, 80% efficiency and can be operated for 22 hours.
Ensemble Voting Method to Enhance the Performance of a Dental Caries Detection System using Convolutional Neural Network Putri Rizkiah; Maulisa Oktiana; Khairun Saddami; Maya Fitria; Fitri Arnia; Hubbul Walidainy; Yunida Yunida
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 8 No 2 (2026): April
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v8i2.1343

Abstract

Individual classification models for caries detection still face significant challenges, including limited accuracy and unstable predictions, which can hinder diagnosis, delay clinical decisions, and increase the risks associated with patient care. To overcome these limitations, this study proposes an ensemble voting method that combines five deep learning models, such as ResNet-152, MobileNetV2, InceptionV3, NASNetMobile, and EfficientNet-B5. This approach aims to enhance the accuracy and stability of caries detection by leveraging the complementary strengths of the individual models while mitigating their weaknesses. Each model was trained and tested on the same dataset of dental images, categorized into caries and regular classes. Their predictions were aggregated using hard and soft voting techniques. The ensemble's performance was evaluated using accuracy, precision, recall, and F1-score. The ensemble voting demonstrates a notable improvement in classification performance over individual models. Hard and soft voting have excellent classification performance and consistently outperform the best individual models. The accuracy increased from EfficientNetB5 0.8485 to 0.8864 and 0.8712, representing increases of 4.46% and 2.68%, respectively. The precision increased from MobileNetV2 0.8182 to 0.8493 and 0.8551, representing increases of 3.81% and 4.52%. For recall, EfficientNetB5 ranked highest among individual models with a score of 0.9242. Hard voting increased 1.64% to 0.9394, and soft voting decreased slightly by 3.28% to 0.8939. The F1 score of EfficientNetB5 is 0.8592. Hard and soft voting increased 3.83% and 1.73% to 0.8921 and 0.8741. The proposed ensemble improves the F1-score by 3.83 percentage points compared to the best individual model. The ensemble voting method effectively leverages the complementary strengths of each deep learning model to improve the stability and accuracy of fast, reliable dental caries early detection prediction.
Co-Authors Damanik, Yusuf Diva F Fathurrahman Fathurrahman Faturrahman Fitri Arnia Hubbul Walidainy Jurej, Muhammad Khairul Munadi Khairun Saddami Maulisa Oktiana Maya Fitria Melinda Melinda MUHAMMAD HABIB Nasaruddin Nasaruddin Nasaruddin Nasaruddin Nurul Maulida Fitri Putri Rizkiah Rachmawati Rachmawati Roslidar, Roslidar SYAHRIAL SYAHRIAL Tarmizi Tarmizi Utami, Rika Sri