The gaming industry is a huge industry that is based on creativity and the use of media as well as the latest technology. According to the Entertainment Software Association (ESA) in 2021 more than 227 million Americans playing video games, and 75% of USA households have at least one person who plays games and has a gaming device on their home. The facts mentioned before makes this industry very profitable to enter. In hardware aspect, some company innovates to make a specific device which is used special for gaming needs. This gaming device main selling points is high specification and ergonomics factor for improving gaming performance. Despite the successful sale of this product, many people still doubt the effectiveness of gaming peripheral products to improve gaming performance and whether the higher specification of gaming peripheral can truly improve player performance during gameplay. The Study is based on the effectiveness of peripheral on human perception sensor that can be used in the implementation of ergonomic science / physical engineering or HCI (Human Computer Interaction), namely vision, hearing, and touch. In that case, with qualitative research method (direct observation, interview & simulation) this study found as the result, that was true the gaming peripherals are able to improve the performance of the user, but not for all types of users. The competitive gamer who has high gameplay hours can benefit the most and use maximum potential performance of gaming peripherals.
The COVID-19 pandemic has caused many changes in people's daily lives. Various ways have been carried out in dealing with this pandemic, like use masks, diligently washing hands and, eating balanced nutrition. The pandemic condition has also raised many new entrepreneurs in various fields. Noona Juice, which is engaged in pure fruit juice, is a form of business that arises due to economic demands and opportunities for increasing public demand for nutritious food and drinks. The condition that occurs to the business of internal community service partners is the lack of an existing market because the marketing process is only carried out through relatives or family contacts and is produced according to the number of orders. Seeing the opportunities that exist for partners, from these conditions the purpose of this community service activity is to help partners in developing their business brands and also assist partners in utilizing financial technology. To achieve this goal, the service team will provide training and workshops in developing partner business branding, online marketing training, and training on the use of financial technology. From the evaluation of the activities that have been carried out, partners are satisfied with the activities that have been carried out.
The increasingly complex business environment necessitates businesses to design more effective and efficient strategies for company development, including market expansion. To understand customer behaviors, customer data analysis becomes crucial. One common approach used to group customers is segmentation based on RFM analysis (Recency, Frequency, and Monetary). This study aims to compare the performance of two clustering algorithms, namely DBSCAN and Affinity Propagation (AP), in providing customer profile segment recommendations using RFM analysis. DBSCAN algorithm is employed due to its ability to identify arbitrarily shaped clusters and handle data noise. On the other hand, Affinity Propagation (AP) algorithm is chosen for its capability to discover cluster centers without requiring a pre-defined number of clusters. The transaction dataset used in this research is obtained from one of the business incubator tenants at STIKOM Bali. The dataset undergoes preprocessing steps before being segmented using both DBSCAN and AP algorithms. Performance evaluation of the algorithms is conducted using the Silhouette Scores and Davies-Bouldin Index (DBI) matrices. The research findings indicate that the AP algorithm outperforms DBSCAN in this customer segmentation case. The AP algorithm yields Silhouette Scores of 0.699 and DBI of 0.429, along with recommendations for 4 customer segments. Furthermore, further analysis is performed on the AP results using a statistical approach based on the mean values of each segment for the RFM variables. The four customer segments generated by the AP algorithm, based on the mean values of the RFM variables, can be associated with the concept of customer relationship management.
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