Text Classification Using NLP by Comparing LSTM and Machine Learning Method Zaidan Muhammad Mahdi, Retno Fauziah Istiqomah, Alfarelzi, Sri Astuti, Ibnu Asror, et al. Proceeding of 2024 the 10th International Conference on Wireless and Telematics Icwt 2024, 2024 Natural Language Processing (NLP) has seen significant advancements recently, leading to various applications across different domains. This research focuses on text classification by comparing the performance of Long Short-Term Memory (LSTM) networks with several traditional machine learning algorithms, including Naive Bayes, Random Forest, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and XGBoost. The novelty of this study lies in its comprehensive comparative analysis using a balanced dataset. The data balancing was achieved through the Synthetic Minority Over-sampling Technique (SMOTE), ensuring robust model training. Experimental results reveal that the SVM algorithm achieves the highest accuracy of over 96%, surpassing other models in performance. This indicates that despite the advancements in deep learning, traditional algorithms like SVM remain highly effective for text classification tasks. The findings provide valuable insights into the strengths and weaknesses of different NLP approaches, contributing to the ongoing development of more accurate and efficient text classification models. Future work could explore hybrid models and it application to diverse datasets to further enhance classification accuracy.
Integration of Software-Defined Networking with Named Data Network for Implementing Forwarding Strategies in Wireless Networks Reza Maharani Susilo, Farraz Rizky Kusumaputra, Muhammad Hendrawan Adiwijaya, Ratna Mayasari, Ridha Muldina Negara, et al. 6th International Seminar on Research of Information Technology and Intelligent Systems Isriti 2023 Proceeding, 2023 Named Data Networking (NDN) represents a forward-looking networking concept that addresses various challenges within the current internet architecture, particularly the reliance on IP addresses for data transmission between devices. In response, Named Data Networking (NDN) and Software-Defined Networking (SDN) architectures introduce a novel approach to data delivery by shifting from a host-centric to a data-centric model. This transition not only enhances data distribution efficiency but also leverages SDN advantages stemming from its segregation of the data and control planes. To implement this convergence, we incorporated the SDN paradigm into the NDN environment. By doing so, we harnessed the capabilities of both SDN and NDN, enhancing network efficiency and reducing data retrieval time for consumers. We utilized Named Data Link State Routing (NLSR) as a routing protocol within the default NDN environment. However, NDN encompasses diverse forwarding strategies tailored to specific network conditions. This research specifically investigates Best-Route Forwarding, Multicast Forwarding, and Adaptive Smoothed RTT Forwarding strategies. The objective is to evaluate the disparities and appropriateness of these strategies within NLSR-NDN and SDN-NDN environments when applied to wireless networks. To assess the efficacy of these strategies, our analysis employs Quality of Service (QoS) parameters, encompassing Average Round Trip Time (RTT), Throughput, Packet Loss, and Satisfied Interest Ratio. These additional metrics provide a comprehensive evaluation of interest satisfaction. Our findings reveal that the SDN-NDN environment remarkably enhances network efficiency by approximately 50-70% compared to the NLSR-NDN environment.
Forwarding Strategy Analysis in Wireless Network Based Named Data Network (NDN) Reza Maharani Susilo, Farraz Rizky Kusumaputra, Muhammad Hendrawan Adiwijaya, Ratna Mayasari, Ridha Muldina Negara, et al. Proceedings Ieit 2023 2023 International Conference on Electrical and Information Technology, 2023 Along with the time, the development of the internet has grown so rapidly that it cannot be controlled. This has resulted in the current internet architecture that no longer being able to meet existing needs. The emergence of Named Data Networking (NDN) architecture can help to overcome previous problems. However, NDN architecture also has its own shortcomings or problems such as Broadcast Storm, which causes packets to be sent in the network to spin continuously that make requiring more energy and consuming considerable time. In this final project, the author implements a comparison of Forwarding Strategy methods focused on Analysis of Forwarding Strategies in Wireless Network Based Named Data Network to find the Best Strategies to prevent the Broadcast Storm and improve the efficiency of Network. Performance testing is carried out by experimenting with several different Forwarding Strategies. After experimenting with the Forwarding Strategies, the authors can find which is the best Forwarding Strategy to prevent the Broadcast Storm that occurs in Wireless Networks.
Optimizing Forwarding Strategies in Named Data Networking Using Reinforcement Learning Zhafirah Naghmah Ahmad, Fika Triana, Revita Rachel, Ridha Muldina Negara, Ratna Mayasari, et al. Proceeding of 2023 9th International Conference on Wireless and Telematics Icwt 2023, 2023 In the current network architecture, IP addresses are used, where data transmission uses the host address on each device. From this data delivery method, NDN emerges as a new paradigm in data transmission from being host-centric to becoming data-centric. There is a strategy used in research with the weakness of congestion in the forwarding strategy. Therefore, modeling the forwarding strategy using Reinforcement Learning is designed to overcome this problem. In the run simulation, an environment will be created in the Reinforcement Learning system with several scenarios in the NDN network. To measure the success of the system, testing is carried out to achieve maximum results, such as the Reinforcement Learning process, which is trial and error in nature, which means that several experiments are carried out, such as the exploration process carried out by the agent in the environment to achieve the goal and get the expected maximum reward. The components used in Reinforcement Learning in the training process are agents, actions, policies, and rewards. The tests aim to make NDN an efficient network system, simplify network performance automatically using Reinforcement Learning, and make NDN a network system that can overcome congestion for forwarding.
