Nowadays, multimedia data has been spreading throughout the world in a network, and the Internet of Things (IoT) also plays a role in it. The current technology can provide multimedia data sensing in an embedded device. The communication resource limitation is still going to be encountered, and it leads to choosing the proper protocol. Constrained Application Protocol (CoAP) is a suitable protocol for the embedded device since it works on top of User Datagram Protocol (UDP), which has a lower overhead compared to Transmission Control Protocol (TCP). CoAP protocol can communicate reliably with a large data payload through a confirmable block-wise approach. The evaluation has been conducted to the CoAP protocol in some research, but it is not yet evaluated in a wireless multimedia sensor network (WMSN) in multiple devices. This paper presents the evaluation of the CoAP protocol performance in multiple multimedia sensor devices connected to the edge server. Several different numbers of nodes are sending the multimedia data through resource-limited access point. The performance is measured by comparing the throughput value and the retransmission event number. The experiment result shows that the device addition affects the average throughput reduction in a linear pattern.
The development of the Internet that moves quickly makes the trend of online shopping in e-commerce and social commerce emerge. Currently, both e-commerce and social commerce come with a live streaming shopping feature that allows interaction between sellers and buyers even from a distance. However, the effectiveness of this strategy needs further validation. This study aims to find out the impact of live streaming shopping on customers' purchase intentions. The study was conducted using causal research and SmartPLS as a tool for data analysis. Data for this study was obtained by distributing questionnaires via Google Form with 300 respondents from all over Indonesia. This study reveals that immersion has an influence on purchase intention through customer trust (indicated by p-values of less than 0.05), while social presence and telepresence have no influence on purchase intention (indicated by p-values of greater than 0.05). It is hoped that this study could contribute to the advancement of social commerce and e-commerce research and practice, where it is advised that sellers across platforms make the best of live streaming shopping to increase their sales performance.
ISO 9001 is a standard for an organization that focuses on quality management. In ISO 9001, the organization must implement seven main clauses in achieving the certification or audit if the company has obtained ISO 9001 certification. One of the clauses is clause 6.2, which is related to quality objectives and focuses on process. The organization can achieve this clause if all SOPs in the organization are appropriately implemented. Therefore, if the SOP is well implemented, the process inside also runs well, and the quality objectives will be in line with the expectations. Manufacturing industry will be the case of the organization for this study. This study proposed a tracing system for the procedure in the SOP that used to achieve clause 6.2, quality objectives in ISO 9001. The database has an important role in system development because the system will store all information in it. Databases with conventional method have various weaknesses in data handling, such as centralized data storage and lack of security. Centralized data will make it difficult for each staff in the organization to access data if the main server is down. This study will focus on blockchain applied to traceability system as database technology because it can minimize the shortcomings of the database with conventional method. The results show that the data traceability system can trace procedures well and the data running well according to its function in storing data with blockchain. Based on these results, the traceability system in this study can trace procedures in achieving quality objectives in ISO 9001.
Some research uses the random forest model and sentiment analysis to detect COVID-19 fake news. However, there is still a research opportunity to apply the method to Indonesian Tweets and reevaluate the feature's performance. Our research aims to reevaluate synthesizing the sentiment analysis feature on detecting COVID-19 fake news on Indonesian Tweets by using the Spark Dataframe. We divide the stages of machine learning development into several steps, including collecting data using Tweepy and then applying sentiment polarity scores using Apache Spark. We apply random forest to classify fake news using the Spark MLlib. Further, we use model evaluation calculation through the level of Accuracy, Recall, Precision, and F1. The results show that applying the sentiment polarity calculation to our Tweet dataset labels 148 Tweets with positive sentiments, 118 Tweets with negative sentiments, and 99 Tweets with neutral sentiments. The Pearson correlation coefficient (PCC) feature score of Sentiment equals 0.056 and ranks fifth in the top feature correlation scores list. According to the experimental findings, the random forest model produces Accuracy = 0.787 for both models with sentiment analysis and without sentiment analysis. Which indicates that sentiment analysis provides no significance in the prediction model.
