Analisis Sentimen Netizen Twitter terhadap Pemberitaan PPN Sembako dan Jasa Pendidikan dengan Pendekatan Social Network Analysis dan Naive Bayes Classifier

Pandemi covid-19 yang terjadi memberikan dampak di berbagai bidang kehidupan. Salah satu dampaknya penerimaan negara semakin tertekan hebat. Padahal di sisi lain negara dalam proses pemulihan ekonomi nasional (PEN) yang membutuhkan dana sangat besar. Sehingga pemerintah ingin menggenjot pendapatan negara dari pajak pertambahan nilai (PPN). Jika pemungutan PPN dapat dilakukan dengan seoptimal mungkin, maka akan meningkatkan penerimaan negara. Rencana tersebut mengakibatkan maraknya pemberitaan mengenai pengenaan PPN sembako dan jasa pendidikan di Indonesia. Pemberitaan tersebut secara otomatis memicu opini di masyarakat. Salah satu cara untuk melihat opini masyarakat adalah melalui media sosial Twitter. Penelitian ini bertujuan untuk mengkaji lebih dalam tentang network dan sentimen netizen Twitter tentang PPN Sembako dan jasa pendidikan. Hasil Social Network Analisis (SNA) menghasilkan 5 klaster dengan record ke-90 merupakan bottleneck node yaitu aktor utama penyebaran informasi antar klaster. Model Naive Bayes Classifier memberikan hasil Recall Accuracy bahwa untuk Accuracy Classified sebesar 74.865 persen sementara persentase untuk Incorrectly Classified Instance sebesar 25.135 persen. Hasil klasifikasi berdasarkan emosi terbentuk 5 ekspresi fear, sadness, surprise, joy, dan anger dan emosi kata yang paling banyak adalah emosi anger (amarah), artinya mayoritas respon masyarakat terhadap kebijakan pengenaan PPN sembako dan jasa pendidikan diidentifikasikan oleh R Studio sebagai wujud keamarahan.

BOND

In a large-scale wireless sensor network, hundreds and thousands of sensors sample and forward data back to the sink periodically. In two real outdoor deployments GreenOrbs and CitySee, we observe that some bottleneck nodes strongly impact other nodes’ data collection and thus degrade the whole network performance. To figure out the importance of a node in the process of data collection, system manager is required to understand interactive behaviors among the parent and child nodes. So we present a management tool BOND (BOttleneck Node Detector), which explains the concept of Node Dependence to characterize how much a node relies on each of its parent nodes, and also models the routing process as a Hidden Markov Model and then uses a machine learning approach to learn the state transition probabilities in this model. Moreover, BOND can predict the network dataflow if some nodes are added or removed to avoid data loss and flow congestion in network redeployment. We implement BOND on real hardware and deploy it in an outdoor network system. The extensive experiments show that Node Dependence indeed help to explore the hidden bottleneck nodes in the network, and BOND infers the Node Dependence with an average accuracy of more than 85%.

Diameter-Aggregation Delay Tradeoff for Data Gathering Trees in Wireless Sensor Networks

We define the aggregation delay as the minimum number of time slots it takes for the data to be aggregated in a Data Gathering tree (DG tree) spanning all the nodes of the sensor network; the diameter of a DG tree is the maximum distance (number of hops) from a leaf node to the root node of the tree. We assume that intermediate nodes at the same level or different levels of a DG tree could simultaneously aggregate data from their respective child nodes using different CDMA (Code Division Multiple Access) codes; but, an intermediate node has to schedule non-overlapping time slots (one for each of its child nodes) to aggregate data from its own child nodes. We employ an algorithm to determine the minimum aggregation delay at every intermediate node of the Bottleneck Node Weight (BNW) and Bottleneck Link Weight (BLW)-based DG trees. We observe the BNW-DG trees to incur a smaller tree diameter, but a significantly larger aggregation delay; on the other hand, the BLW-DG trees incur a larger tree diameter and a relatively lower aggregation delay, especially with increase in node density.

Efficient Energy Routing Protocol based on Energy & Buffer Residual Status (EBRS) for Wireless Sensor Networks

Wireless networks consist of nodes, having the ability that, they can sense and collect the information from the nearby surroundings. It has the responsibility of designed protocol to send this collected information by data gathering and forward it to the outside network via a sink node. Furthermore, WSNs doesn’t need any predetermined network structure; all the nodes used in WSN can operate as a router as well as the host. It uses multiple hops to send information to the node outside the communication range through different neighbor nodes. All the sensor nodes in WSN have their range of communication and can send and collect messages straight to each other until they were in the communication range. Moreover, the Self-organizing property of nodes in the network made WSN outstanding amongst the major applications. Nevertheless, the wireless nodes there in the network have a battery with restricted energy and can’t be recharge or change once deployed. Hence, the node energy must be utilized efficiently for various functions as sensing the information, processing the sensed information, and transmitting the processed information to another node. With the enhancements of the innovation and cost-effective hardware, our visualization presents a tremendous life enhancement of WSN into several new applications. To modify following such background, the energy-efficient routing protocol is extremely desirable and can be achieved by clustering in WSN. In the literature survey, various energy-efficient routing techniques based on cluster have been given to attain the energy-efficiency and enhance the lifetime of the network. However, these protocols were suffering from the bottleneck node issue. It is the situation in the network where the router node subjected to heavy traffic due to its presence in energy-efficient routing path or high remaining energy. This paper aims to moderate the possibility of the node to become a bottleneck node throughout the application. Thus, we attain the objective by design and develop the cluster-based efficient-routing protocol by selecting the head nodes of the cluster based on their residual energy and buffer status. Performance outcome shows that the projected work out-performs in contrast with present cluster-based routing protocols.

Energy-Efficient Routing Protocol for Network Life Enhancement in Wireless Sensor Networks

The wireless sensor network (WSN) is composed of autonomous nodes consist of sensors to collect the status of the surrounding environment. These nodes are equipped with limited batteries. One cannot recharge or replace the batteries of the nodes during the mission, as the applications of WSNs include in underwater, forest driven and mountain based. Thus available energy must be utilized effectively. Energy efficient routing is one of the primary sources of energy management. Cluster-based routing in WSN is a prevalent method to achieve network performance and energy efficiency. In literature, the number of cluster-based energy efficient routing protocols and their route selection metric is designed based on the residual status of node energy. However, this metric causes some of the intermediate nodes to drain energy instantly. In wireless networks this situation roots intermediate nodes to turn into a bottleneck node, and thereby performance degradation in terms of efficiency and packet delivery caused. Thus our paper aims to design a cluster based routing protocol to prevent the creation of intermediate bottleneck node. We introduce a novel routing metric called “ranking status” for the bottleneck problem. Performances results indicate that the proposed routing protocol prevents the creation of intermediate bottleneck node, and improve the network's performance.

Heavy-traffic approx­imations for a layered network with limited resources

HEAVY-TRAFFIC APPROXIMATIONS FOR A LAYERED NETWORK WITH LIMITED RESOURCESMotivated by a web-server model, we present a queueing network consisting of two layers. The first layer incorporates the arrival of customers at a network of two single-server nodes. We assume that the interarrival and the service times have general distributions. Customers are served according to their arrival order at each node and after finishing their service they can re-enter at nodes several times for another service. At the second layer, active servers act as jobs that are served by a single server working at speed one in a processor-sharing fashion. We further assume that the degree of resource sharing is limited by choice, leading to a limited processor-sharing discipline. Our main result is a diffusion approximation for the process describing the number of customers in the system. Assuming a single bottleneck node and studying the system as it approaches heavy traffic, we prove a state-space collapse property.

Diameter-Aggregation Delay Tradeoff for Data Gathering Trees in Wireless Sensor Networks

We define the aggregation delay as the minimum number of time slots it takes for the data to be aggregated in a Data Gathering tree (DG tree) spanning all the nodes of the sensor network; the diameter of a DG tree is the maximum distance (number of hops) from a leaf node to the root node of the tree. We assume that intermediate nodes at the same level or different levels of a DG tree could simultaneously aggregate data from their respective child nodes using different CDMA (Code Division Multiple Access) codes; but, an intermediate node has to schedule non-overlapping time slots (one for each of its child nodes) to aggregate data from its own child nodes. We employ an algorithm to determine the minimum aggregation delay at every intermediate node of the Bottleneck Node Weight (BNW) and Bottleneck Link Weight (BLW)-based DG trees. We observe the BNW-DG trees to incur a smaller tree diameter, but a significantly larger aggregation delay; on the other hand, the BLW-DG trees incur a larger tree diameter and a relatively lower aggregation delay, especially with increase in node density.

Impact of the Structure of the Data Gathering Trees on Node Lifetime and Network Lifetime in Wireless Sensor Networks

We analyze the impact of the structure of the Data Gathering (DG) trees on node lifetime (round of first node failure) and network lifetime (minimum number of rounds by which the network gets either disconnected due to node failures or the fraction of coverage loss reaches a threshold) in wireless sensor networks through extensive simulations. The two categories of DG trees studied are: the Bottleneck Node Weight-Based (BNW-DG) trees and Bottleneck Link Weight-Based (BLW-DG) trees. The BNW-DG trees incur a smaller diameter and a significantly larger fraction of nodes as leaf nodes: thus, protecting a majority of the nodes in the network from simultaneously being exhausted of the energy resources (contributing to a significantly larger network lifetime); nevertheless the nodes that serve as intermediate nodes in the first few instances of the BNW-DG trees are bound to lose their energy more quickly than the other nodes, leading to a smaller node lifetime compared to that of the BLW-DG trees (that incur a larger diameter and a relatively lower fraction of nodes as leaf nodes).

Weighted-DESYNC and Its Application to End-to-End Throughput Fairness in Wireless Multihop Network

The end-to-end throughput of a routing path in wireless multihop network is restricted by a bottleneck node that has the smallest bandwidth among the nodes on the routing path. In this study, we propose a method for resolving the bottleneck-node problem in multihop networks, which is based on multihop DESYNC (MH-DESYNC) algorithm that is a bioinspired resource allocation method developed for use in multihop environments and enables fair resource allocation among nearby (up to two hops) neighbors. Based on MH-DESYNC, we newly propose weighted-DESYNC (W-DESYNC) as a tool artificially to control the amount of resource allocated to the specific user and thus to achieve throughput fairness over a routing path. Proposed W-DESYNC employs the weight factor of a link to determine the amount of bandwidth allocated to a node. By letting the weight factor be the link quality of a routing path and making it the same across a routing path via Cucker-Smale flocking model, we can obtain throughput fairness over a routing path. The simulation results show that the proposed algorithm achieves throughput fairness over a routing path and can increase total end-to-end throughput in wireless multihop networks.