Penerapan Text Network Analysis Dalam Menganalisis Pendapat Customer Product Patriot di Shopee

E-commerce has changed the buying and selling process and the way people interact via the internet. One company that uses e-commerce is PT Patriot Memory Indonesia. PT Patriot Memory Indonesia sells well-known computer peripherals, including the Solid State Drive (SSD). PT Patriot Memory Indonesia wants to analyze customer feedback regarding SSD products sold in e-commerce, namely Shopee by using Text Network Analysis (TNA) which is one part of social computing. Social computing is a science that focuses on social behavior and social contexts using computing systems. One of the tools of social computing, namely Text Network Analysis (TNA), is a research technique that focuses on identifying and comparing network relationships between words, sentences, and systems to model interactions that generate new knowledge or information. In this study, TextNetwork Analysis will show consumer perceptions through the feedback it provideson buyer reviews. The opinions expressed by consumers in buyer reviews can be analyzed so that they can connect each word and create associations of consumer perceptions of a product. Thus, it can be seen the aspects that must be addressed by the company to improve consumer perceptions. The problem analyzed is the development of social computing in analyzing big data. Can the company take advantage of this information so that they know the perceptions of their consumers through the information in the customer feedback at Shopee. Through Text Network Analysis in social computing, researchers will know the association of each word of consumer perception and can see the perception that has the highest degree of a product and see its relationship with other perceptions. This study looks at consumer perceptions of Patriot SSD products at Shopee. The results of this study can help provide customer feedback information to PT Patriot Memory Indonesia.

Efficient Key-Value Data Placement for ZNS SSD

Log-structured merge-tree (LSM-Tree)-based key–value stores are attracting attention for their high I/O (Input/Output) performance due to their sequential write characteristics. However, excessive writes caused by compaction shorten the lifespan of the Solid-state Drive (SSD). Therefore, there are several studies aimed at reducing garbage collection overhead by using Zoned Namespace ZNS; SSD in which the host can determine data placement. However, the existing studies have limitations in terms of performance improvement because the lifetime and hotness of key–value data are not considered. Therefore, in this paper, we propose a technique to minimize the space efficiency and garbage collection overhead of SSDs by arranging them according to the characteristics of key–value data. The proposed method was implemented by modifying ZenFS of RocksDB and, according to the result of the performance evaluation, the space efficiency could be improved by up to 75%.