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All Journal Buletin Ilmiah Sarjana Teknik Elektro
Chekol, Yenework Belayneh
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Electrical & Electronics Engineering

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Improving Dynamic Routing Protocol with Energy-aware Mechanism in Mobile Ad Hoc Network Mekonnen, Atinkut Molla; Munaye, Yirga Yayeh; Chekol, Yenework Belayneh; Bizuayehu, Getenesh Melie; Maghfiroh, Hari
Buletin Ilmiah Sarjana Teknik Elektro Vol. 6 No. 3 (2024): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v6i3.11994

Abstract

A Mobile Ad hoc Network (MANET) is designed for specific communication needs, where nodes dynamically interact. In a MANET, mobile nodes self-configure and frequently adapt to changes in topology due to their ability to move freely. Each node operates as a router, forwarding data to other designated nodes within the network. Since these mobile nodes rely on battery power, energy management becomes critical. This paper addresses the challenges of routing in MANETs by improving the Dynamic Source Routing (DSR) protocol. The proposed enhancement, termed energy-aware DSR, aims to mitigate and reduce packet loss and improve the packet delivery ratio, which often suffers due to node energy depletion. Simulations conducted with the NS-3.26 tool across varying node counts demonstrate that the energy-aware DSR protocol significantly outperforms the traditional DSR in terms of efficiency and reliability.
A Hybrid LSTM-CNN Approach Using Multilingual BERT for Sentiment Analysis of GERD Tweets Mekonnen, Atinkut Molla; Munaye, Yirga Yayeh; Chekol, Yenework Belayneh
Buletin Ilmiah Sarjana Teknik Elektro Vol. 7 No. 2 (2025): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v7i2.13281

Abstract

Analyzing public sentiment through platforms like Twitter is a common approach for understanding opinions on political matters. This study introduces a deep learning sentiment analysis model that integrates Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN) to assess attitudes toward the Grand Ethiopian Renaissance Dam (GERD). LSTM is utilized to capture long-range dependencies in text, while CNN identifies significant local patterns. An initial dataset of 30,000 unlabeled tweets was collected in 2024 G.C., out of which 17,064 were labeled as positive, negative, or neutral. The labeled tweets were divided into 13,112 for training and the remaining for testing. The hybrid LSTM-CNN model demonstrated superior performance compared to the standalone models, delivering more accurate and balanced sentiment classification. A major feature of this study is the analysis of tweets written in Amharic, Arabic, and English. The model was trained over 35 epochs with a batch size of 46 and a learning rate of 0.001. Using multilingual BERT (mBERT) embeddings notably enhanced the model’s performance, with training and testing accuracies reaching 95.3% and 92%, respectively. The hybrid model also achieved a precision, recall, and F1-score of 90%. In a focused analysis of Arabic tweets, 3,710 were negative, 9,793 positive, and 4,814 neutral. These results emphasize the influence of linguistic diversity and class distribution on classification performance. While mBERT showed strong results, addressing class imbalance and expanding language-specific features remains crucial for further improvements.
Co-Authors Bizuayehu, Getenesh Melie Maghfiroh, Hari Mekonnen, Atinkut Molla Munaye, Yirga Yayeh