Articles
Customer Behavior of Green Advertising: Confirmatory Factor Analysis
Doni Purnama Alamsyah, Norfaridatul Akmaliah Othman, Rudy Aryanto, Mulyani, Yogi Udjaja
Adv. Sci. Technol. Eng. Syst. J. 6(1), 833-841 (2021);
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The implementation of green advertising is relatively low for credibility but has an impact on green customer behavior. Based on the phenomenon, the purpose of this study is to examine factors affecting green advertising development, which is based on experienced customers towards products and advertisements and environmental issues. This research focuses on customers to create an implementation model for green advertising among companies. The study was conducted through a survey of 215 customers in West Java (Indonesia) who experienced green advertising and bought environmental-friendly products. Data were collected through a quantitative questionnaire and processed with SmartPLS to test and evaluate Confirmatory Factor Analysis (CFA). In emphasizing the study results, a fit test of the research model and research hypotheses were also being carried out by valuing the KMO. Research findings show several dimensions involved in developing green advertising, such as experience, theme, message, claim, emotion, interaction, and impact. The dimensions of green advertising were plotted in the CFA model so that the priority scale from the implementation of green advertising measurement can be detected. Customers assume green advertising as advertising that takes environmental issues of “global warming,” and this issue can adopt by companies in implementing the green marketing strategy.
The Impact of eLearning as a Knowledge Management Tool in Organizational Performance
Abdulla Alsharhan, Said Salloum, Khaled Shaalan
Adv. Sci. Technol. Eng. Syst. J. 6(1), 928-936 (2021);
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This paper aims to understand the impact of eLearning capabilities on organizational performance. It also addresses the obstacles of organizational learning using eLearning methods and highlighting some emerging trends and technologies that will impact the eLearning experience in organizations. It examines a brief history of knowledge management and how it is related to learning, organizational learning, and performance. It also explores different eLearning technologies and trends. A systematic literature review was used to examine previous papers between 2016–2020. Results show eLearning can impact organizational performance in many ways, and human factors can be one of the most challenging obstacles in deploying eLearning solutions in organizations, and many emerging eLearning trends were explored including open educational resources, gamification, flipped classrooms, and many others.
Comparison between Collaborative Filtering and Neural Collaborative Filtering in Music Recommendation System
Abba Suganda Girsang, Antoni Wibowo, Jason, Roslynlia
Adv. Sci. Technol. Eng. Syst. J. 6(1), 1215-1221 (2021);
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Music is one of the most popular entertainments, and the music industry continues to increase over time. There are many types of genres in music, and everyone has their own choice of the type of music they want to listen to. The recommendation system is an important function in the application, especially when there are a large number of choices for a particular item. With a good recommendation system, users will be able to get help from the suggestions given and can improve the user experience of the application. By using collaborative filtering (CF) methods to recommend products related to personal preference history, this feature can be better provided. However, the CF method still lacks in integrating complex user data. Hybrid technology may be a solution to perfect the CF method. The combination of neural network and CF also called NCF is better than using CF alone. The focus of this research is a CF method combined with neural networks or neural collaborative filtering. In this study, we use 20,000 users, 6,000 songs, and 470,000 records of ratings then predict the score using CF and NCF approach. We aim to compare the recommendation systems using CF and NCF. The study shows that NCF is better in gathering certain playlists according to one’s preferences, but it takes more time to build compared to user-based collaborative filtering.
Actual Traffic Based Load-Aware Dynamic Point Selection for LTE-Advanced System
Kittipong Nuanyai, Soamsiri Chantaraskul
Adv. Sci. Technol. Eng. Syst. J. 6(2), 776-783 (2021);
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Coordinated MultiPoint (CoMP) has been introduced for LTE-Advanced system to overcome the inter-cell interference problems and enhance the signal quality of cell-edge UEs (User Equipments). With such concept, the overall system performance should be improved considerably to support the significantly increasing amount of demand on data transmission via mobile communication that happens nowadays. Dynamic Point Selection (DPS) is one of the major CoMP techniques offering benefit through its practicality and low complexity. This work proposes the actual traffic-based load-aware DPS for LTE-Advanced system. The key important cell selection criterion employed in this work is based on the actual traffic load of the calls along with the UEs received signal indicator. The adapted Vienna downlink system level simulator has been used for the system evaluation. The video streaming traffic model was employed with the data rate of 512 kbps for the realistic use cases and four simulation scenarios including the uniformly distributed UEs case and different patterns of hotspots distribution use cases were deployed. The system performance evaluation includes the system throughput performance, the number of UEs achieving expected data rate, and eNBs’ traffic load. The results show that our proposed method offers a substantial improvement over the traditional system as well as the system embedded with the existing DPS mechanisms when the traffic loads are imbalanced such as in certain hotspot cases.
