Publication Search

This study aims to analyze how the TikTok and Instagram Reels algorithms play a role in the distribution of multimedia content and their implications for content visibility, user engagement, and digital marketing practices. The research method used is a qualitative approach through a Systematic Literature Review by analyzing articles from accredited national journals and reputable international journals published in the period 2020-2025. The literature search process was carried out systematically through openly accessible scientific databases, then selected using inclusion and exclusion criteria to ensure the relevance and quality of the sources. The research findings show that the TikTok and Instagram Reels algorithms both rely on analysis of user behavior, initial engagement levels, and the characteristics of short-form audiovisual content in determining content distribution. TikTok emphasizes an interest-based recommendation system that allows content from new creators to gain broad reach, while Instagram Reels combines algorithmic recommendations with established social networks. The implications of this study emphasize that understanding the mechanics of algorithms is a strategic factor for content creators, business actors, and digital marketing practitioners in designing effective, adaptive, and sustainable multimedia content distribution strategies.

The proliferation of cloud infrastructures has intensified concerns regarding data security, integrity, identity and access management, and user privacy. Despite recent advances, existing solutions often lack comprehensive integration of privacy-preserving mechanisms, dynamic trust management, and cross-provider interoperability. This study proposes an AI-enabled, zero-trust, blockchain-fused identity management framework for secure, privacy-preserving multi-cloud environments. The framework integrates homomorphic encryption with differential privacy for aggregate-level protection and secure multi-party computation for collaborative data processing. The proposed system was validated in a simulated multi-cloud environment using CloudSim, Ethereum blockchain, and AWS EC2. Experimental results indicate homomorphic encryption latency of approximately 450ms per operation and statistically significant security improvements (t(128) = 12.47, p < 0.001), privacy (t(95) = 8.93, p < 0.001), and throughput (t(156) = 15.21, p < 0.001). The framework achieved differential privacy with ε = 0.1 while retaining 99.2% data utility, and demonstrated a 34% improvement in processing speed over conventional differential privacy approaches. In addition, the implementation was observed to be 2.3× faster than BGV-based configurations, with 45% lower memory consumption than CKKS and a 67% reduction in ciphertext size relative to baseline implementations. From an operational perspective, the framework shows a 23% reduction in security management costs, a 31% improvement in resource utilization efficiency, and an 18% decrease in compliance audit expenses. The model further indicates a 27% reduction in total cost of ownership (TCO) compared with multi-vendor security solutions, a projected return on investment (ROI) within 14 months, and an 89% reduction in security incident response costs under the evaluated conditions.

PT Jasamarga Tollroad Operator (JMTO) ia trusted by the company to handle toll road operations which include transaction services at toll gates and toll road services. PT Jasamarga Tollroad Operator (JMTO) is responsible for smoothness, comfort, and safety on toll roads. Good coaching to all employees will improve employee performance. This study aims to determine the effect of compensation, motivation, and job involvement on the performance of employees of PT Jasamarga Tollroad Operator (JMTO) Semarang Branch. The issue raised in this study is the decline in employee performance at PT Jasamarga Tollroad Operator (JMTO) which is indicated by the existence of work aspects that do not meet minimum service standards, which will have an impact on decreasing the quality of service to toll road users. The type of research used in this research is descriptive quantitative with a census sampling method using a questionnaire with a population of 157 respondents, with a Likert scale to measure each variable. Data analysis technique used is primary data using correlation test, linear regression, multiple. Methods of data analysis using SPSS version 24.00. The results obtained from this study are that the t-value of -0.440 is smaller than the t-table of 1.975 and the significance value of 0.687 is greater than 0.005. This shows that compensation has no effect on the performance of employees of PT Jasamarga Tollroad Operator (JMTO). Motivation obtained t value 3.493 greater than t table 1.975 with a significance value of 0.001 less than 0.005. meaning that the motivational variable affects performance. variable job involvement obtained t value of 9.506 greater than t table 1.975 with a significance value <0.001 less than 0.005. shows that the variable job involvement affects the performance of employees of PT Jasamarga Tollroad Operator (JMTO). Simultaneously the compensation, motivation and job involvement variables obtained results 63.385 greater than table 2.66 with a significance level of 0.001 less than 0.005. this shows that the variables of compensation, motivation and job involvement simultaneously have a significant effect on the performance of employees of PT Jasamarga Tollroad Operator (JMTO).

This research aims to identify both the partial and simultaneous positive and significant effects of e-service quality, e-trust, and brand image on the customer loyalty of Byond by BSI users in Balikpapan City. The study adopts a quantitative correlational method. A total of 100 respondents were selected using a purposive sampling technique. Data were collected through online questionnaires distributed via a Google Form link. The analytical procedures utilized include correlational analysis, instrument tests, classical assumption tests, multiple linear regression analysis, and hypothesis testing using IBM SPSS Statistics version 24. The results of the study indicate that, partially, e-service quality and e-trust variables each have a positive and significant effect on customer loyalty among users of the Byond by BSI application in Balikpapan City. Meanwhile, brand image variable has a positive but not significant effect on customer loyalty among users of the Byond by BSI application in Balikpapan City. Furthermore, when examined simultaneously, the three variables collectively show a positive and significant effect on customer loyalty of Byond by BSI applications users in Balikpapan City.

This study aims to analyze the influence of customer experience and perceived value on tourist satisfaction among users of the Sidoarjo City Tour Bus as a form of public tourism transportation service. The Sidoarjo City Tour Bus represents a regional government service innovation intended to support tourism development while improving urban tourism accessibility. This study employs a quantitative approach using a survey method involving tourists who used the Sidoarjo City Tour Bus. Data were collected through questionnaires distributed to 99 respondents and analyzed using multiple linear regression with the assistance of SPSS software. The results indicate that customer experience does not have a significant effect on tourist satisfaction, suggesting that the experiences perceived by users are still subjective and not consistently felt by all service users. In contrast, perceived value has a positive and significant effect on tourist satisfaction, making it the most dominant variable in influencing satisfaction among Sidoarjo City Tour Bus users. These findings indicate that tourist satisfaction is more strongly determined by perceptions of benefits, service quality, and the suitability between sacrifices made and benefits received rather than by emotional experience alone. Therefore, this study recommends that the management of the Sidoarjo City Tour Bus and the local government enhance service value through improvements in facilities, information systems and registration processes, as well as optimization of comfort and schedule certainty. These efforts are important to increase tourist satisfaction and to support sustainable tourism development in Sidoarjo Regency.

The development of information technology has significantly improved the efficiency of application-based delivery services, including Shopee Partner Driver couriers. However, several issues such as delays in information updates, system errors, and inaccurate delivery data still affect courier performance. This study aims to analyze the effect of Information Quality and System Quality on the Performance of Shopee Partner Driver Couriers in Medan City. This research used a quantitative method with a questionnaire survey distributed to 100 active couriers. The instrument applied a fivepoint Likert scale, and data were analyzed using validity and reliability tests, descriptive statistics, and multiple linear regression analysis with SPSS software. The results indicate that both Information Quality and System Quality have a positive and significant effect on Courier Performance. The coefficient of determination (R²) of 0.625 shows that these two variables explain 62.5% of courier performance variance, while the remaining 37.5% is influenced by other factors. These findings support the DeLone and McLean (2003) model of information system success, emphasizing the importance of system and information quality in enhancing user performance.

This research presents the development of a transaction data protection mechanism for a bouquet sales application by utilizing the Advanced Encryption Standard (AES) algorithm. The rapid growth of digital commerce has led to an increase in online transactions, which in turn raises serious concerns regarding the security of sensitive transaction data. Information such as customer identities, order details, delivery addresses, and payment data are vulnerable to unauthorized access, data leakage, and manipulation if not properly secured. To address these issues, this study applies the AES-128 encryption algorithm using a 128-bit secret key to secure transaction data before it is stored in the system database. The encryption process follows the standard AES workflow, including key expansion, initial transformation, multiple encryption rounds, and a final transformation stage. Decryption is restricted exclusively to authorized users who possess the correct encryption key. The research methodology includes system analysis, AES integration into the application, and functional testing of the encryption and decryption processes. Data integrity is validated by comparing the original plaintext with the decrypted output, while system performance is evaluated based on processing time and decryption accuracy. Experimental results indicate that the average encryption and decryption time remains under 10 milliseconds per transaction, without affecting system performance. The findings confirm that AES-128 effectively enhances transaction data confidentiality and integrity in the bouquet sales application

This study aims to analyze the effect of product differentiation and product innovation on the competitive advantage of the Honda Beat Street motorcycle in Medan City. The research employed a quantitative method with an associative approach, involving 97 respondents who are users of Honda Beat Street. Data were collected through questionnaires and analyzed using validity tests, reliability tests, multiple linear regression, t-tests, and F-tests. The results show that product differentiation has a positive and significant effect on competitive advantage, with durability emerging as the most dominant indicator. Product innovation also has a positive and significant effect, with product design being the highest-rated indicator. Simultaneously, both differentiation and innovation significantly influence competitive advantage, as indicated by an Adjusted R Square value of 59.1%. These findings indicate that Honda’s differentiation and innovation strategies play an important role in strengthening the competitive position of Honda Beat Street in the Medan market. The study suggests that the company should enhance stylistic elements, enrich product variations, and consistently improve innovation to maintain relevance and competitiveness.  

Domestic wastewater management in densely populated areas remains challenging because conventional concrete/steel septic tanks can crack or corrode, increasing leakage risk and potentially degrading groundwater quality. This study aims to compare conventional septic tanks with Fiberglass Reinforced Plastic (FRP) septic tanks and to develop an FRP septic tank concept that improves durability, installation practicality, and treatment performance for residential use. A qualitative comparative approach was applied through purposive selection of households/public facilities, in-depth interviews (users, technicians, manufacturers), direct observations of tank conditions and maintenance, and documentation; findings were reduced, categorized (material, efficiency, cost, environmental impact), and thematically interpreted. The study produced an FRP septic tank design concept developed using an Objective Tree, Function Structure, and Morphology Chart, resulting in planning criteria and conceptual drawings. The FRP concept emphasizes lightweight strength, corrosion/chemical resistance, and shape stability under soil loads, while incorporating multi-partition internal processing and media (bioballs) to support staged treatment using gravity flow. The proposed design can reduce leakage risk, simplify installation and maintenance, extend service life, and support more sustainable domestic waste management in space-limited urban neighborhoods. The novelty lies in a residential FRP septic tank concept that integrates three partitions, bioballs, and gravity-based flow without relying on pumps/motors, supported by a structured product-engineering design framework.

One of the key instruments for advertising travel destinations is the growth of social media, particularly Instagram. Nevertheless, a large number of Instagram followers does not automatically translate into a large number of visitor visits. Rio By The Beach Beach in Kalianda, South Lampung had the most visitors during the 2025 holiday season, despite having fewer Instagram followers than other beach attractions. This condition suggests that the effectiveness of electronic word-of-mouth (E-WOM) and social media marketing may have a significant impact on travelers' decisions to visit. Thus, the purpose of this study is to ascertain how tourists' decisions to visit Rio By The Beach in Kalianda, South Lampung, are influenced by Instagram social media marketing and electronic word-of-mouth. This study examines 170 visitors to Rio By The Beach using a quantitative methodology and selective sampling strategies. Questionnaires were sent as part of the data collection process, and SPSS software was utilized for multiple linear regression data analysis. The study's findings show that electronic word-of-mouth and Instagram social media marketing significantly and favorably influence travelers' decisions to visit. Electronic word-of-mouth has a greater impact than the other one. The study's conclusions suggest that tourism destination managers should concentrate their marketing efforts more on user interaction, high-quality information, and developing travel experiences that can promote the creation of positive electronic word-of-mouth (E-WOM) in order to boost travelers' decisions to come.

Financial statements have an important role in facilitating all forms of economic activity in the private, public, and non-profit sectors. However, in reality, many companies commit financial statement fraud, causing losses to users. With the increasing attention to financial statement fraud, board diversity has become an important aspect in improving corporate financial reporting monitoring and reducing the possibility of fraud. However, previous studies on the effect of this factor on financial statement fraud still show mixed findings, particularly in the food and beverage manufacturing sub-sector in Indonesia. This study aims to analyze the effect of board diversity on financial statement fraud in food and beverage companies listed on the Indonesia Stock Exchange for the period 2021–2023. This study uses a quantitative approach with secondary data obtained from companies' annual reports and financial statements. Board diversity in this study includes board gender diversity, board age diversity, and board education diversity, all of which are measured using dummy variables. Financial statement fraud is measured using the Beneish m-score. Data analysis is performed using multiple linear regression with the Ordinary Least Squares approach. The results show that board age diversity has a significant negative effect on financial statement fraud. These findings indicate that board diversity is one of the factors that can reduce the likelihood of financial statement fraud.

This study aims to design, implement, and test a prototype that automates three functions, namely watering, fertilizing, and pest control based on Arduino Uno with the ability to directly monitor soil moisture and pH. This system is equipped with four main types of sensors. Soil condition monitoring involves an FC-28 soil moisture sensor and a soil pH sensor, water level measurement involves an HC-SR04 ultrasonic sensor, and pest detection in the plant area involves a RIP sensor. All data obtained from these sensors is then processed by the Arduino Uno microcontroller to automatically activate actuators such as water pumps, liquid fertilizer pumps, buzzers, and DC motors according to soil conditions and plant needs. Prototype testing was conducted on simulated land with various scenarios of moisture, soil pH, and pest activity. The test results revealed that the system was proven to be able to significantly optimize water and fertilizer utilization, as well as reduce pest disturbances that could potentially damage plants.  In addition, this system also displays the operational status directly through an LCD screen, making it easy for users to monitor. The advantage of this system is its multi-function integration in a single device that is cost-effective and easy to operate. In the future, the functionality of this system can be improved through integration with Internet of Things (IoT) technology, enabling remote monitoring and control with greater efficiency. More broadly, this study is expected to support increased production and sustainable agricultural practices in Indonesia.

This study explores the integration of deep learning based approaches in real time video content analysis for intelligent human computer interaction (HCI) in multimedia systems. Traditional video analysis techniques, such as rule-based methods and offline processing, struggle with real time performance and adaptability to complex video data. In contrast, the deep learning model used in this research, particularly Convolutional Neural Networks (CNNs), provides high accuracy in object detection, feature extraction, and real time processing. The integration of CNNs with interactive visualization modules enables dynamic adjustments to video content based on user interactions, ensuring a seamless and engaging user experience. The system was benchmarked in terms of its processing speed, accuracy, and responsiveness, showing significant improvements over traditional approaches in real time video analysis. Moreover, the study demonstrates that combining deep learning with real time visualization enhances the efficiency of interactive multimedia applications, making it suitable for dynamic environments such as surveillance, security monitoring, and interactive media. Despite the system's strong performance, challenges such as computational demands in high-resolution video processing were identified, highlighting the need for further optimization. Future work will focus on optimizing the system for different hardware platforms, incorporating multimodal inputs, and refining deep learning models to address computational bottlenecks. This research contributes to advancing HCI by providing insights into the integration of deep learning for real time video content analysis, which is pivotal for enhancing the interactivity and adaptability of intelligent multimedia systems.

This study explores the integration of adaptive streaming models with edge computing to optimize multimedia delivery, particularly in real-time applications such as video conferencing, live streaming, and virtual reality. The proposed model leverages adaptive compression techniques, including scalable video coding (SVC) and hybrid adaptive compression (HAC), which adjust video quality based on real-time network conditions. The use of edge computing further enhances the model by processing and delivering content closer to the user, reducing latency and optimizing bandwidth usage. The research demonstrates that the edge computing-based adaptive streaming model significantly improves latency by up to 30%, reduces bandwidth consumption, and ensures higher visual quality during video playback, even under fluctuating network conditions. This model addresses key challenges in multimedia streaming, such as maintaining video quality in bandwidth-constrained environments and minimizing buffering times. Furthermore, it enhances the overall Quality of Experience (QoE) for users by providing smoother interactions and real-time responsiveness. The study highlights the potential impact of this model on various fields, including remote education, entertainment, and interactive applications, where low-latency content delivery and high-quality streaming are critical. The findings suggest that integrating AI algorithms for even more efficient compression and expanding edge computing infrastructures will further optimize multimedia streaming in the future, ensuring reliable and high-quality user experiences in increasingly connected environments.

The integration of Augmented Reality (AR) technology into higher education has emerged as a promising approach to enhance collaborative learning experiences. This study aims to design and evaluate an AR multimedia framework that facilitates real time interaction and spatial visualization, creating immersive and engaging learning environments for students. The AR framework was developed with a focus on improving student engagement, collaboration, and learning outcomes through interactive 3D models and real time feedback. By leveraging AR technology, the study sought to address common challenges in traditional learning environments, such as limited student interaction and engagement, and lack of real time feedback. The experimental evaluation involved two student groups: one using the AR-based system and the other using conventional multimedia tools. Findings revealed that students using the AR framework showed significant improvements in engagement, interaction frequency, and collaborative task performance. Additionally, the AR framework contributed to better learning outcomes, including enhanced comprehension, retention of complex concepts, and improved problem-solving skills. The study also highlighted the importance of incorporating a user-centered design approach in developing AR applications to ensure that the system meets the needs and preferences of learners. Qualitative feedback from students indicated that the AR system provided an enriched learning experience, although challenges such as interface navigation were noted. Overall, the study demonstrates the effectiveness of AR in fostering collaborative learning and offers practical insights for its integration into higher education curricula. Future research should explore the integration of AR with other immersive technologies to further enhance collaborative learning experiences.

The development of information technology encourages organizations to adopt a more efficient, flexible, and secure data management system, especially in the field of financial management that requires high accuracy and reliability. One of the technologies that is widely used is cloud computing, which offers easy access to data and an integrated security system. This article aims to analyze the utilization of cloud technology in improving the security and accessibility of financial management data. The method used in this study is a literature study by examining various scientific sources, books, and online news relevant to the topic of cloud computing and financial data management. The results of the study show that cloud technology is able to improve data security through the implementation of encryption, multi-layered access control, user authentication, and a reliable data backup system. In addition, cloud technology also improves the accessibility of financial data because it allows users to access information in real-time, flexibly, and without location or device restrictions. Thus, the application of cloud technology can be a strategic solution for organizations in improving operational efficiency, data security, and the quality of decision-making in financial management.

User experience (UX) evaluation plays a crucial role in understanding how users interact with digital platforms and in improving product design. Traditional UX evaluation methods, such as surveys and interaction logs, often rely on a single data source, which limits the depth of analysis. This study explores the integration of multimodal data processing techniques in UX research, aiming to enhance the accuracy and comprehensiveness of UX evaluations. By combining interaction logs, visual attention data, and physiological measurements, this approach provides a more holistic understanding of user behavior, emotional responses, and satisfaction. Interaction logs offer objective data on user actions, while eye-tracking and physiological data capture users' emotional states, providing richer insights into usability and user experience. This study highlights the effectiveness of multimodal integration in identifying patterns that traditional methods overlook, such as emotional responses to interface elements and real-time feedback from users. The findings reveal that multimodal data processing improves the precision of UX assessment by combining objective behaviors with subjective emotional responses, offering a more complete view of user interactions. The study also discusses the challenges of data synchronization and the potential ethical concerns related to the use of physiological data. The integration of these data sources shows great potential for enhancing the design process, allowing designers to make informed decisions based on comprehensive insights. Finally, this research underscores the future potential of multimodal analytics in UX research, suggesting further exploration of additional data modalities and real-time applications in various digital environments.

This study explores the role of visual analytics in enhancing decision-making processes within creative industries, focusing on its application to large-scale multimedia datasets. Visual analytics integrates interactive visualization techniques with computational algorithms, enabling users to explore complex datasets intuitively and derive actionable insights. The research centers on the design and implementation of interactive dashboards tailored to the creative sector, particularly film, music, and advertising industries, to facilitate real-time data exploration. The study also investigates the usability of these tools through expert-based evaluations, aiming to assess their effectiveness in supporting informed and timely decision-making. The findings reveal that interactive visualizations significantly improve insight discovery and pattern recognition, enabling decision-makers to uncover hidden trends in large multimedia datasets. However, challenges related to scalability, user acceptance, and real-time processing were encountered during the implementation phase. The research highlights the practical benefits of integrating visual analytics into industry workflows, which include enhanced content creation, audience engagement, and strategic planning. Furthermore, the study identifies key visual analytics techniques such as dynamic dashboards, pattern recognition, data mining, and clustering, which are essential for analyzing multimedia data. The study concludes by emphasizing the potential for wider applications of visual analytics in other sectors, suggesting future research directions to improve tool performance, scalability, and user accessibility, as well as exploring the integration of emerging technologies like artificial intelligence and virtual reality.

Human–Robot Interaction (HRI) systems increasingly rely on data-driven approaches to interpret multimodal sensory inputs and support natural interaction. However, purely neural-based HRI models often suffer from limited interpretability and insufficient context-aware decision-making, which can reduce user trust and adaptability in dynamic interaction scenarios. To address these limitations, this study proposes a hybrid neural–symbolic HRI framework that integrates multimodal neural perception with explicit symbolic reasoning for adaptive and interpretable robot behavior. The proposed system combines deep neural networks for processing visual, speech, and gesture inputs with a rule-based symbolic reasoning layer that models interaction context, user states, and behavioral constraints. A loosely coupled integration strategy enables neural outputs to be transformed into symbolic representations, allowing logical inference to guide action selection while preserving perceptual accuracy. The framework was evaluated through controlled HRI experiments comparing a neural-only baseline with the proposed hybrid configuration across multiple interaction scenarios. Experimental results demonstrate that the hybrid neural–symbolic system significantly improves interaction accuracy, contextual responsiveness, and user satisfaction, while achieving substantial gains in interpretability. These findings indicate that symbolic reasoning effectively complements neural perception by enhancing transparency and context-aware adaptation without compromising performance. The study concludes that hybrid neural–symbolic architectures provide a promising foundation for developing trustworthy, adaptive, and human-centered HRI systems.

This research aims to develop a multiclass flower image classification system using the Convolutional Neural Network (CNN) algorithm with the EfficientNet architecture. The main problem addressed is the difficulty of manual identification of flower species that share high visual similarity. The research stages include collecting 17,299 flower images across 19 classes, performing data preprocessing such as image resizing, pixel normalization, and augmentation, followed by model training using the EfficientNet transfer learning approach. The model was trained for 10 epochs with an 80:20 training-validation data split. The evaluation results show that the model achieved a validation accuracy of 98.05% with a loss value of 0.0968, and an average precision, recall, and F1-score of 0.98. The trained model was then implemented into a web-based application built using the Next.js framework, enabling users to upload flower images and obtain real-time classification results via the Hugging Face API. The system successfully identified flower species with a confidence level of 99.87%. These findings demonstrate that combining a modern CNN architecture with transfer learning provides efficient and highly accurate flower classification performance, which can be effectively implemented for educational and digital conservation purposes.