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This study analyzes e-commerce platform preferences for digital marketing strategies among Generation Z students at the Institut Teknologi dan Bisnis Semarang using the Simple Additive Weighting (SAW) method. By evaluating four platforms (Tokopedia, Lazada, Shopee, TikTok) based on ease of use (25%), user experience (25%), promotions (15%), transaction security (15%), and product completeness (15%), the results show that Shopee leads (0.9333) with the best interface and features. TikTok (0.9080) excels in promotions but lacks security, Tokopedia (0.8820) remains consistent, while Lazada (0.8084) needs UX improvements. The implications suggest that platforms should optimize mobile interfaces, collaborate with influencers, and enhance visual search features to meet Gen Z’s demand for interactive experiences and economic value..

Pakistan is a developing country. Its transportation infrastructure mainly consists of road network. About 95% passengers and fright is transported using the road network. This high demand on road network is because of the unreliable railway system between the cities. Due to such high demand on road network the accident involvement risk of an individual is much high as compared to developed countries. This study uses a new modeling approach to estimate road safety risk for WTP.  A correlated random parameters Tobit model (heterogeneity-in-mean) is integrated with machine learning (Decision tree).  The decision tree categorizes higher-order interactions, while the model captures unobserved correlations and heterogeneity. The framework examines WTP determinants using a representative sample of 3178 road users from Pakistan. The model estimates WTP for different (fatal and severe injury) risk reductions to monetize road traffic crash costs. Results show maximum respondents are willing to support safety improvement policies. The model reveals significant WTP heterogeneity linked to perceptions of road safety and accident risk. Systematic preference heterogeneity emerges through higher-order interactions, offering insights into WTP relationships. Marginal effects highlight varying sensitivities to explanatory variables, quantifying their impact on WTP probability and magnitude. The framework provides two key contributions. It identifies public WTP determinants, emphasizing heterogeneous effects. It also helps in prioritization safety policies by understanding public sensitivity to WTP. The insights further emphasizing on the importance of road safety interventions to the specific socio-economic profiles of road users. This study offers a significant contribution to road safety improvement by providing valuable recommendations for policy makers. By integrating detailed socio-economic factors, it also addresses the urgent need for targeted traffic safety interventions in Pakistan. These findings are expected to aid policymakers and stakeholders in developing effective strategies to enhance road safety and reduce the accident involvement risk effectively.

This study develops an e-commerce application prototype with a User-Centered Design (UCD) approach that places user needs and preferences as the main focus. User interface (UI) and user experience (UX) design play an important role in creating intuitive interactions, which contribute to user satisfaction and retention. The UCD method allows testing and adjusting designs based on user feedback, thereby improving navigation comfort. The UCD stages in this study include planning, understanding the context, identifying user needs, creating design solutions, evaluating, and finalizing the design. The needs analysis revealed important features, such as digital product recording, displaying structured information through a dashboard, and a search feature. The developed prototype showed increased inventory management efficiency and reduced recording errors. The results of the study showed that the application of UCD in the development of the UI/UX e-commerce prototype created a user-friendly interface and improved the shopping experience. This application is expected to meet user expectations and support a comfortable and satisfying shopping experience, and emphasize the importance of user involvement in the product development process.

User satisfaction in information systems is a term used to describe how satisfied or dissatisfied users are when using a particular information system. User satisfaction includes how the information system meets the needs, expectations, and preferences of users in carrying out activities or achieving desired goals. User satisfaction is very important because it has a direct impact on the performance and success of information systems in organizations. The results of this study when viewed from Acceptability, Grade Scale and Adjective Rating are matched with the calculation of 50.5, namely the level of user acceptance is included in the marginal low category, the level of grade scale is included in the F category and the adjective rating is included in the ok-good category. According to the provisions of the conclusions that have been obtained, it can be concluded that users still experience difficulties when using SLiMS.

This qualitative literature review examines the impact of tax policies on cryptocurrency exchange preferences within the context of evolving regulations. With the rapid growth of the cryptocurrency market, understanding the relationship between tax regulations and user behavior has become crucial. This study synthesizes findings from various research articles, highlighting how clear and transparent tax policies can influence user engagement and compliance in cryptocurrency trading. The review reveals that uncertainty regarding tax obligations often leads to user avoidance of cryptocurrency exchanges, while a better understanding of tax responsibilities correlates with more proactive investment management. Furthermore, the analysis indicates that cryptocurrency platforms demonstrating transparency in tax handling are preferred by users. The findings emphasize the necessity for governments to develop supportive tax frameworks and educational initiatives to facilitate healthy growth in the cryptocurrency sector. Overall, the research underscores the importance of regulatory clarity in fostering user trust and participation in cryptocurrency exchanges, ultimately contributing to a more robust and sustainable market.

In this study, a website and UI/UX design ordering service system was developed. This system offers clients to make orders through a website that can make it easier for people to find service experts in the field of website and UI/UX design. The development of this system was carried out using the Extreme Programming (XP) method, this method is a software development that emphasizes flexibility, communication, and adaptability to changing requirements. Researchers will use short iterations to create, build, and test websites and UI/UX designs. To ensure a thorough understanding of client requests and preferences, developers and customers will communicate more frequently. The testing method on the system used in this study is User Acceptance Testing, which is a testing stage where the system or software is evaluated to ensure that the system meets user needs and requirements. Users perform this test to ensure that the system functions properly and meets their expectations. The results of this study, system development using the Extreme Programming (XP) method is very helpful for developers because the needs of each user are clearly defined, the use of the User Acceptance Testing method shows that each function of the application system operates according to user needs. Thus, the use of the Extreme Programming method can improve the system development.

In this modern era, people have various activities and to fulfill these activities, people need transportation as a supporting tool/assistant tool in carrying out their activities. Therefore, transportation service entrepreneurs are competing to attract the interest of their consumers by improving services, ease of ordering, fleet comfort, punctuality and so on. In Indonesia, there are many types of transportation that also function as public transportation, namely transporting people/goods and collecting agreed fees. The general public is currently familiar with two types of transportation, namely application-based online transportation and conventional transportation. Where application-based online transportation is the transfer of goods and people from place of origin to destination. The current flow of technological developments is something that must be understood and whether we like it or not, we must keep up with the flow of technological developments, so that the emergence of application-based online transportation is a transportation service that takes advantage of technological developments. Conventional transportation is passenger transportation carried out on a rental basis. Conventional transportation, for example, includes buses, public transportation, taxis, pedicabs and motorbike taxis. In line with current developments, the existence of conventional transportation is gradually being replaced by the choice of more sophisticated modes of transportation with the use of technological advances such as the emergence of application-based online transportation. Apart from being time efficient, online transportation itself can also be used at an affordable cost. However, this is different with conventional types of transportation, not all of which can be reached easily, sometimes you have to wait for a long time to be able to access or use these transportation services. Using Comparative Analysis The use of Comparative Analysis aims to compare the differences in services between two types of online and conventional transportation modes based on the type of transportation mode and the services provided by service providers to users such as the Academic Community located on the Cenderawasih University campus environment. By using Descriptive Statistical Analysis the data that has been collected can be analyzed by calculating the average perception of user respondents regarding service quality preferences for two types of online and conventional transportation modes from the Academic Community in the Cenderawasih University Campus environment.

Item-based collaborative filtering is a popular technique in recommendation systems that aims to provide suggestions for films to watch or services to users based on similarities between items. In this approach, the similarity between items is calculated using metrics such as cosine similarity, allowing the prediction of user preferences for items that have never been rated. This research implements Item-based collaborative filtering using datasets from Kaggle. Experimental results show that the resulting model is able to provide recommendations with significant improvements in evaluation metrics such as Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) of 3.05 and 3.26. This shows that the smaller the value, the better.

The Semarang Regency Regional Library has been supporting its users to increase their knowledge and knowledge informally. In order to continue to improve the reach and quality of its services, research needs to be carried out as a reference for developing Regional Library services so that they can be in line with the needs of users. This research aims to identify efforts that need to be made by the Semarang Regency Regional Library to develop its services based on the preferences of the library community as users of library services. This research was carried out using qualitative methods by extracting data from observations and surveys using purposive sampling by distributing open questionnaires to readers as respondents. The results of the research show that library services are very useful for respondents (60.0%), especially to increase knowledge (25.77%), reduce stress and mental pressure (22.70%), and help complete school or college assignments (21, 47%). The weakness of service at the Regional Library that was most felt by respondents was "the small number and variety of books" (42.37%). Locations for service development based on respondents' choice were Ambarawa (30.21%), West Ungaran (16.67%), Bandungan (13.54%), and Bawen (11.46%). The conclusion of this research is that the Semarang Regency Regional Library service must continue to be developed because it has provided benefits for readers which are realized through increasing the quantity and quality of books, digitizing collections, expanding and repairing buildings which is balanced with the addition of adequate indoor facilities as well as suggestions regarding further development locations. to pay attention to ease of accessibility for the community, especially ease of transportation access to the location.

The behavior of digital wallet users in Indonesia depends on individual preferences, user habits, and other factors. Consumers' decisions in using digital finance applications are influenced by various factors, including digital marketing, the quality of electronic services and the brand image of the digital finance applications they use. This research aims to determine the influence of digital marketing, electronic service quality and brand image on consumer decisions to use the OVO application in the Jabodetabek area. The method used in this research is a quantitative method that is associative. Data was collected using a questionnaire distributed via Google Form to 155 respondents who used the OVO application. The sampling technique uses purposive sampling. Data processing in this research used the IBM Statistical Package for the Social Sciences (SPSS) ver.25. Based on the results of hypothesis testing, it shows that: (1) digital marketing variables have a positive and significant effect on consumer decisions to use the OVO application (2) electronic service quality variables have a positive and significant effect on consumer decisions to use the OVO application (3) brand image variables have a positive and significant effect on consumer decisions to use the OVO application and (4) digital marketing variables, electronic service quality and brand image together have a significant influence on consumer decisions to use the OVO application.    

The behavior of digital wallet users in Indonesia depends on individual preferences, user habits, and other factors. Consumers' decisions in using digital finance applications are influenced by various factors, including digital marketing, the quality of electronic services and the brand image of the digital finance applications they use. This research aims to determine the influence of digital marketing, electronic service quality and brand image on consumer decisions to use the OVO application in the Jabodetabek area. The method used in this research is a quantitative method that is associative. Data was collected using a questionnaire distributed via Google Form to 155 respondents who used the OVO application. The sampling technique uses purposive sampling. Data processing in this research used the IBM Statistical Package for the Social Sciences (SPSS) ver.25. Based on the results of hypothesis testing, it shows that: (1) digital marketing variables have a positive and significant effect on consumer decisions to use the OVO application (2) electronic service quality variables have a positive and significant effect on consumer decisions to use the OVO application (3) brand image variables have a positive and significant effect on consumer decisions to use the OVO application and (4) digital marketing variables, electronic service quality and brand image together have a significant influence on consumer decisions to use the OVO application.    

This research aims to analyze the changes in digital consumer behavior in the context of e-commerce business until 2024. Through a qualitative approach with a focus on literature studies, the analysis identifies factors that influence changes in digital consumer behavior, including increased online shopping activities, shifting purchasing preferences, the use of mobile devices, digital payment trends, data security and privacy, and the influence of social, cultural, and new technological factors. The analysis shows that consumers are increasingly demanding better user experience, flexibility in payment methods, and guaranteed data security and privacy. Meanwhile, new technologies such as artificial intelligence and collaboration with influencers also play an important role in influencing consumer behavior. Thus, the proposed suggestions include improving user experience, flexibility in payment methods, investment in new technologies, collaboration with influencers, and prioritizing data security and privacy. It is hoped that the results of this study can help e-commerce businesses to better understand the changes in digital consumer behavior and develop effective strategies in dealing with them.    

The behavior of digital wallet users in Indonesia depends on individual preferences, user habits, and other factors. Consumers' decisions in using digital finance applications are influenced by various factors, including digital marketing, the quality of electronic services and the brand image of the digital finance applications they use. This research aims to determine the influence of digital marketing, electronic service quality and brand image on consumer decisions to use the OVO application in the Jabodetabek area. The method used in this research is a quantitative method that is associative. Data was collected using a questionnaire distributed via Google Form to 155 respondents who used the OVO application. The sampling technique uses purposive sampling. Data processing in this research used the IBM Statistical Package for the Social Sciences (SPSS) ver.25. Based on the results of hypothesis testing, it shows that: (1) digital marketing variables have a positive and significant effect on consumer decisions to use the OVO application (2) electronic service quality variables have a positive and significant effect on consumer decisions to use the OVO application (3) brand image variables have a positive and significant effect on consumer decisions to use the OVO application and (4) digital marketing variables, electronic service quality and brand image together have a significant influence on consumer decisions to use the OVO application.      

In the midst of internet advancement, the presence of marketplaces brings many positive impacts. Local marketplace users are predominantly coming from Generation Z, and the majority of ITS students, as observed during this research, is part of the group. The purpose of this study is to identify the most favorite marketplace and the factors influencing the preference of these favorite marketplaces among ITS students. The preference factors are based on 13 factors that affect shopping behavior. The method employed is a survey-descriptive using 55 respondents in answering the questionnaire. The research results indicate that advertising and communication, certification and security, and warranty are the three factors with the highest urgency in choosing a favorite local market. Additionally, discount, website, and review and complain are the three factors with the lowest urgency. This study reveals that consumers or users prioritize the value offered by marketplaces over factors such as price and discounts.    

This study investigates the benefits of augmented reality (AR) in the tourism industry and its impact on data security. The background of the research highlights that information technology is becoming increasingly important in the tourism industry, with augmented reality (AR) being a major focus for enhancing visitor experiences. In this context, concerns about user data protection arise since AR applications may require personal information such as user location and travel preferences. A comparative research method was used to compare two popular augmented reality (AR) applications: Smartify, Arts and Culture, Zome, Augmented Reality, Maps AR, Guideo, and AR Trial. The results indicate that most applications have adequate data security policies, including data transfer encryption and user control over their data. Zome was found to be the best in terms of data security and user privacy, scoring 90% in the assessment. This research underscores the importance of using AR applications to maintain data security and user privacy in the tourism industry and suggests further research to learn more about the data privacy practices of AR applications.

This study explores the design intricacies of a scalable RESTful Backend API tailored for a sophisticated food recipe recommendation application, with a primary focus on leveraging the Google Cloud Platform (GCP) for deployment. Employing a service-oriented paradigm, the API segregates Backend functionalities, fostering optimal scalability. The REST architecture ensures seamless integration, while GCP guarantees reliability and scalability. Utilizing Node.js and Express.js, the API efficiently manages culinary recipes and user preferences. Rigorous performance evaluations affirm its rapid responsiveness. This paper offers pragmatic guidelines for developers, emphasizing the significance of GCP for seamless and scalable deployments.  

Education is vital for human beings to develop and continue their lives. Although many of the tutor search apps are available, students in Eastern Sumba have difficulty enjoying this service because no private tutor is resident in East Sumba. A private lesson tutor is important for students who need more intensive learning outside of school, especially parents who are busy or do not have the opportunity or knowledge to help their children. The challenge often faced is the difficulty of finding a tutor that suits the needs of the student. The use of profile matching methods can help parents or student guardians find private tutors based on specific needs or preferences, such as subject, level of experience, location, or other criteria. Tutors that meet these criteria will appear as search results. Testing with the System Usability Scale (SUS) showed that the application is qualified for users, as it achieved an average score of 72.62.  

This study analyzes the impact of Facebook's algorithm on political polarization in Medan during the 2024 elections. Facebook's algorithm is designed to personalize content based on user interactions and preferences, which can create echo chambers and filter bubbles, reinforcing existing political beliefs and reducing openness to different viewpoints. Using a mixed-method approach that includes content analysis and user surveys, this research explores how content recommendation algorithms influence user interactions, the dissemination of political information, and the formation of polarized social groups. The findings show that Facebook's algorithm significantly contributes to political polarization in Medan during the 2024 elections. Recommendations to mitigate the negative effects of political polarization are also presented, including policies and practices for more mindful management of social media algorithms.

The increasing popularity of e-commerce has driven the development of more sophisticated product recommendation systems to enhance user experience. Within this framework, the incorporation of Artificial Intelligence (AI) has become essential for enhancing the caliber of product suggestions through the examination of user behavioral patterns and product attributes. This paper explores the incorporation of artificial intelligence into e-commerce product recommendation systems. This strategy integrates AI methodologies like machine learning, data mining, and natural language processing (NLP) to produce recommendation systems that are personalized and pertinent. Through in-depth analysis of user data and product information, the system can predict user preferences with higher accuracy, improve user engagement levels, and ultimately increase sales conversions. By combining domain knowledge, technical expertise, and the latest research methodologies, This research offers valuable insights for both e-commerce system developers and artificial intelligence researchers. The integration of artificial intelligence into product recommendation systems represents a critical measure in facilitating growth and fostering innovation within the ever-changing e-commerce landscape.  

The objective of this research is to devise a personalized recommendation system for a freelancing platform to optimize the freelancer project matching process. This enhancement is intended to improve user experience and increase the success rate of projects. The system will recommend projects to freelancers based on their skills and preferences by employing data analysis and machine learning methodologies. The research methodology adheres to the Cross Industry Standard Process for Data Mining (CRISP-DM), incorporating six stages: Business Understanding, Data Understanding, Data Preparation, Modeling, Evaluation, and Deployment. The proposed project employs a hybrid recommendation strategy, integrating Content-Based Filtering through KNearest Neighbors (K-NN) and Cosine Similarity, Collaborative Filtering via Singular Value Decomposition (SVD), and recommendations derived from Word2vec. Evaluation metrics such as precision, recall, F1 score, MAP, and MRR are utilized to assess model performance. The results, including precision scores of 0.80 for KNN and 0.728 for SVD, recall scores of 0.60 for KNN and 0.623 for SVD, and F1 scores of 0.69 for KNN and 0.671 for SVD, as well as a MAP of 0.75 and MRR of 0.80 for Word2vec, demonstrate the efficacy of the hybrid recommendation system in delivering accurate and varied project suggestions to freelancers, with a weighted average ensemble learning model emerging as the most effective solution.