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The rapid growth of social media has yielded vast digital traces with high potential for improving corporate forensic auditing. Their utilization, however, lags behind through technological reliability, privacy, and adherence to the law. The aim of this study is to explore effective utilization of social media digital traces in forensic auditing and develop a functional framework that lags neither behind through technological efficiency nor adherence to the law and ethics. A mixed-method design was utilized, combining quantitative machine learning analysis with qualitative document analysis and semi-structured interview insight. Quantitative data drawn from social media digital traces were processed using Random Forest algorithm with SMOTE for class balancing, while qualitative data were processed using thematic analysis. The results indicated high model performance with 91.3% accuracy and AUC-ROC of 0.94, together with three emergent themes: digital integration, ethics and privacy, and regulation and legality. The results demonstrate that digital footprints may serve as an effective early and reliable indicator for fraud detection, provided they are accompanied by clear regulatory and ethical frameworks. Its principal contribution lies in the development of an operational model that combines machine learning with legal and ethical perspectives, a new strategy which matures methodological refinement and practical application in today's forensic auditing.

This article explores the background, methods, results, and conclusions of digitalization in public services, focusing on its impact on the social administration ecology. It examines the rapid adoption of e- government and ICT (information and communication technology) by public administrations worldwide since 2020, investigating both opportunities and risks. Methods include a systematic literature review and qualitative case studies drawn primarily from peer-reviewed journals published between 2020 and 2024. The results show that digitalization in public services offers significant benefits: improved efficiency, transparency, citizen participation, reduced corruption, and enhanced environmental governance. However, it also presents risks, in particular widening digital divides, loss of human aspects in administrative interaction, ethical concerns (data privacy, algorithmic bias), regulatory and infrastructural challenges, and potential exclusion of marginalized groups. The discussion elaborates on how these opportunities and risks reshape the ecology of social administration defined here as the interplay of structures, actors, norms, technologies, and environment in public administration. In conclusion, the paper argues that digitalization must be managed with attention to equity, ethical governance, infrastructure readiness, and regulatory safeguards. Key recommendations include fostering digital literacy, inclusive design, transparency in data and algorithmic processes, and participatory governance.  

Shadow puppet craft training is a strategic intervention in preserving cultural heritage and strengthening the creative economy sector in Indonesia. To ensure the effectiveness and efficiency of training, a planning approach is needed that is not only conventional, but also based on quantitative analysis and intelligent systems. This community service proposes a training planning strategy using an interdisciplinary approach involving Operation Research, Design of Experiment (DoE), Simulation, Metaheuristic Algorithms, and Data Mining. This study begins with the identification of key training variables, such as duration, number of participants, initial competency level, teaching materials, and instructor resources. Through the DoE approach, various combinations of variables are systematically tested to identify the optimal training design. Next, Simulation is used to model the dynamics of training implementation and evaluate implementation scenarios. To predict training needs and participant behavior, Data Mining techniques are applied to historical data of arts community training. In the final stage, Metaheuristic algorithms such as Genetic Algorithm and Simulated Annealing are used to solve complex and large-scale scheduling and resource allocation problems. The results of the integration of these approaches show an increase in training efficiency of up to 27% as well as increased participant satisfaction and the quality of work results. This activity demonstrates that applying a quantitative, data-driven approach to traditional crafts training planning can provide significant added value. This model can be replicated in other training programs based on local wisdom and other creative industry sectors.

This study aims to explore the behavioral dynamics of Generation Z (Gen Z) toward digital culinary brands on TikTok, a platform increasingly central to youth consumer engagement. The research investigates how Gen Z interacts with, interprets, and forms relationships with culinary content in a digital environment shaped by algorithmic trends and participatory culture. Employing a qualitative phenomenological approach, data were collected through in-depth interviews with 12 Gen Z participants aged 18–24 years who actively engage with culinary brand content on TikTok. Additional data were obtained from observational analysis of brand-related content and user interactions on the platform. Thematic analysis revealed three core themes: emotional engagement with sensory-rich food content, digital consumption as identity performance, and expectations of brand authenticity and interactivity. Participants expressed strong affective responses to visually appealing and immersive content, linked their brand preferences to personal values and social identity, and favored brands that demonstrated responsiveness, humor, and human-like interaction. These findings highlight the role of TikTok not only as a marketing tool but as a cultural space where brand perception and loyalty are socially constructed. This study contributes to the theoretical understanding of consumer engagement by situating Gen Z’s behavior within a sociocultural and digital framework. Practically, it offers insights for culinary brands to develop more authentic, interactive, and value-driven content strategies. The findings also suggest implications for digital marketing policies and content regulation. Future research is recommended to explore cross-platform behaviors and cultural variations in digital brand engagement.

In the era of data-driven digital commerce, Artificial Intelligence (AI)-based product personalization has become a key strategy to enhance user experience and foster customer loyalty. However, in the Indonesian e-commerce landscape, there remains a lack of empirical understanding of how personalization systems influence long-term user engagement. This study investigates the impact of AI-driven product personalization on customer loyalty among Indonesian e-commerce users. Employing a mixed-methods approach, quantitative data were collected through an online survey of 150 active users, and qualitative insights were obtained from in-depth interviews with six informants. Statistical analysis using simple linear regression revealed that personalization significantly influences customer loyalty, with a beta coefficient of 0.653 (t = 8.241, p < 0.001) and an R² value of 0.567. Qualitative findings highlight user concerns over recommendation accuracy, interface overload, and repetitive suggestions, which affect emotional satisfaction and platform attachment. This research contributes to the growing body of knowledge on AI adoption in e-commerce by integrating behavioral and technological dimensions of loyalty formation. It also offers practical implications for designing more context-sensitive personalization systems that prioritize not only algorithmic precision but also user control and experience quality.

Advances in technology and computers are utilized in various fields of contemporary applications, especially in cryptography and data security in graph theory applications. By understanding the concepts of graph theory, researchers and developers can design cryptographic algorithms that are stronger, more efficient and resistant to attacks. One of the cryptographic applications that has been implemented is the XNOR algorithm. This algorithm has been applied to encryption and decryption as well as the use of stream ciphers. By using 64 bits, the XNOR algorithm can expand encryption and decryption capabilities and increase the security of encrypted data. In this research, an analysis was carried out regarding information encryption and decryption algorithms with the application and development of the XNOR gate logic circuit method in Boolean algebra and graph theory. This research uses the example of the word MATH, which can be changed into a code or password and vice versa to secure information that you want to keep secret. Apart from that, an analysis of the graph formation of each character in the word MATH was also carried out using Python which produced semi-Euler and Hamilton graphs  

Artificial Intelligence (AI) is quickly becoming a game changer in the way businesses build and manage their strategies. This article explores how AI is helping organizations make faster and smarter decisions, streamline operations, and spark innovation across various industries. With the ability to process massive amounts of data, AI tools can uncover valuable insights about market trends and customer behavior, allowing companies to respond more accurately and stay ahead of the competition. From machine learning and generative AI to natural language processing and digital twins, these technologies are transforming everything from internal workflows to how businesses connect with customers. The article also offers a practical roadmap for adopting AI in a business setting, covering steps like evaluating readiness, running pilot projects, and measuring success through return on investment (ROI). It emphasizes the need for strong data infrastructure, skilled teams, and a culture that supports innovation and data-driven thinking. Challenges such as algorithmic bias, data privacy, and internal resistance to change are also addressed. Real-world examples from banking, retail, and manufacturing show how AI can deliver real impact improving efficiency, increasing customer satisfaction, and driving business growth. Ultimately, embracing AI isn’t just about keeping up with technology it’s about shaping the future of smart, strategic, and ethical business.

TikTok's algorithm-based digital marketing strategy, especially through the #FYPMarketing approach, has become an effective method in building emotional connections and increasing Gen-Z consumer loyalty. Through the For You Page (FYP) feature, brands can spread content organically with a wide reach, as long as the content is emotionally relevant, authentic, and participatory. This study uses a qualitative method based on literature studies and analyzes the #ScarlettGlowUp campaign as a case study. The findings show that the success of marketing to Gen-Z is greatly influenced by the use of user-generated content, micro-influencer involvement, and storytelling that is consistent with the values ​​held by the audience. However, challenges such as algorithm manipulation, digital communication ethics, and data privacy issues are obstacles that require an approach based on transparency and social responsibility. This article concludes that the success of the #FYPMarketing strategy depends not only on technological and algorithmic capabilities, but also on trust, clarity of values, and alignment between the brand and its digital community.

Mining activities in the Raja Ampat area have sparked various public reactions, both supportive and critical, particularly on social media platforms such as Twitter. This study aims to analyze public sentiment regarding the mining operations by employing two classification algorithms. A total of 500 tweets related to Raja Ampat were collected from the X platform, and after data cleaning, 168 were identified as positive sentiments and 303 as negative. Sentiment analysis was conducted using text mining techniques by comparing two algorithms: Support Vector Machine (SVM) and Naïve Bayes. To address the issue of data imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) was applied. The analysis results showed that SVM achieved an accuracy of 80%, outperforming Naïve Bayes, which reached only 68%. This indicates that SVM performed better in classifying sentiment. Additionally, the application of SMOTE effectively enhanced both algorithms’ abilities to detect positive sentiment, as reflected in the precision, recall, and F1-score metrics. For SVM, precision reached 85%, recall 80%, and F1-score 80%, while Naïve Bayes recorded a precision and recall of 69%, and an F1-score of 68%.

Teachers are under increasing pressure to deliver personalized, standards-aligned instruction while managing time constraints and rising workloads. Traditional lesson planning often limits creativity and adaptability due to its complexity and repetitive demands. In response, Artificial Intelligence (AI) has emerged as a promising tool to support instructional planning. This study highlights how AI enhances teacher efficiency, simplifies administrative tasks, and supports differentiated, data-driven instruction. However, these benefits require thoughtful and responsible integration. AI adoption must include safeguards for data privacy, ensure algorithmic transparency so teachers understand the basis of system recommendations, and actively mitigate systemic bias that may disadvantage certain learner groups. Most importantly, teachers should remain actively involved in reviewing and adapting AI-generated content to preserve professional judgment and uphold pedagogical integrity.

This study uses a qualitative method to explore how social media listening, combined with a machine learning approach, can be utilized to predict consumer trends in modern marketing strategies. In today’s digital era, social media serves as a rich data source for capturing consumer preferences, needs, and behaviors in real time. With machine learning algorithms such as natural language processing (NLP) and sentiment analysis, data from platforms like Twitter, Instagram, and TikTok can be processed to identify patterns that indicate market trends. This approach not only enables companies to respond quickly to consumer dynamics but also allows them to craft more targeted and data-driven marketing strategies. This study examines five major brands that implement social media listening as part of their digital strategy by observing consumer conversations, dominant emotions, and viral issues. The findings show that the integration of social media listening and machine learning can serve as an effective predictive tool in developing adaptive and contextual marketing campaigns.

This study uses a qualitative method to analyze the dynamics of the business environment within the context of the digital economy, specifically focusing on the impact of platformization on industry competition. Rapid digital technological developments have given rise to various digital platforms that disrupt conventional business models and reshape market structures across multiple sectors. Platforms such as marketplaces, ride-sharing services, and fintech companies have become dominant actors influencing competitive patterns, value distribution, and relationships between large and small enterprises. This research aims to understand how digital platforms affect industry competition through vertical integration, network effects, and data control. The findings reveal that platformization enhances transaction efficiency and market access but also creates imbalances in market power, digital monopolies, and dependency on algorithms. In this context, government regulation and business adaptation strategies are essential to maintaining competitive balance and ensuring the sustainability of the digital ecosystem. The study recommends the adoption of adaptive regulatory approaches and multi-sector collaboration to foster healthy competition in the digital era.    

Artificial Intelligence (AI) has become an essential tool in the world of management for decision-making. This article examines the ways in which AI can be used to improve the quality and speed of decision-making, and how AI can improve the operational efficiency of companies. In addition, this article also examines the challenges and opportunities that companies face in adopting AI.In the rapidly evolving digital era, AI has become an essential component of modern business strategies. Today's managers are often faced with the challenge of analyzing very large and complex volumes of data. To make good and timely decisions, AI offers a potential solution with fast and precise data analysis capabilities.The use of AI in decision-making involves machine learning algorithms and models to efficiently process and analyze large amounts of data. This helps managers gain deeper and more accurate insights, enabling more effective decision-making.

The training on optimizing Instagram and TikTok social media content and video editing using the CapCut application was part of a community service program aimed at enhancing digital literacy and visual content production skills. This activity took place during the Education Exhibition commemorating the 63rd Anniversary of Universitas Negeri Makassar, involving participants from various backgrounds, including students and small business owners. Using a participatory method combining theoretical sessions, demonstrations, and hands-on practice, the training resulted in significant improvements in participants’ understanding of social media strategies, personal branding, and video editing skills. Evaluation results showed a 41% increase from pre-test to post-test scores, and 85% of participants grasped the basic concepts of reach and engagement in social media algorithms. This program successfully equipped participants with relevant digital competencies and contributed to social transformation through the strategic and creative use of social media.

Artificial Intelligence (AI) is increasingly used in financial accounting to improve decision-making effectiveness. This research analyzes the role of AI in supporting data-driven decision making and identifies challenges in its implementation. Using a qualitative approach with the Systematic Literature Review (SLR) method, this study reviewed 41 relevant articles from national and international journals. The results showed that 28 studies supported the effectiveness of AI in improving financial decision-making by automating transaction recording, enabling algorithm-based predictive analysis, and detecting financial anomalies. AI enables companies to respond faster to market changes, increase transparency of financial reports, and reduce human errors in accounting processes.However, 13 studies highlighted challenges such as technological complexity, limited transparency in decision-making, algorithmic bias, and organizational readiness. In addition, evolving regulations are an obstacle to ensuring optimal use of AI while minimizing ethical and legal risks. The success of AI in financial decision-making depends on infrastructure readiness, regulatory support, and human resource competencies. Without a well-planned strategy, AI may pose new challenges that hinder its effectiveness. Therefore, this study provides insights into the optimal AI implementation strategy to ensure that this technology improves the accuracy and transparency of decision making while maintaining financial accounting accountability.

YouTube is one of the leading video-based social media platforms with a complex algorithm for recommending content to users. Understanding this algorithm is crucial for content creators to increase visitor numbers and audience engagement. This socialization activity aims to educate beginner content creators on video optimization strategies to enhance discoverability and recommendation by YouTube’s system. The socialization was conducted online via Zoom, utilizing presentations, interactive discussions, and simulations of YouTube Studio features. The results of this activity indicate an improvement in participants’ understanding of key factors influencing the YouTube algorithm, such as watch duration, user interactions, and metadata optimization. Some participants who implemented the taught strategies experienced up to a 30% increase in video views within a month. However, challenges remain in maintaining content consistency and utilizing YouTube analytics effectively. Therefore, continuous mentoring is necessary to help participants refine more effective strategies. This socialization is expected to enable content creators to maximize the YouTube algorithm to enhance their channel visibility and growth.

This study aims to design and develop a web-based stock and sales transaction management system that can help admins manage accounts, stock, transactions, and sales analysis using the Apriori algorithm. This system is designed with main features such as automatic transaction recording, real-time stock monitoring, and customer purchasing pattern analysis reports. The methods used in this study include needs analysis, system design, implementation, and testing using the black box testing method. The test results show that the system runs according to the design and can increase efficiency in managing sales data. However, there are several limitations such as the need for periodic database maintenance and limitations in raw material management. With this system, it is expected that the process of recording transactions and sales analysis can be carried out faster and more accurately, thus helping in making business decisions.

The purpose of this research is to develop a data search system for thesis and internship reports at the Faculty of Engineering Library of Gajah Putih University Takengon (UGP). This search engine will be created and used to help students and library employees access thesis and internship report information. Analysis of user needs, system design, creation of effective search algorithms, and evaluation of system performance are all topics that will be discussed in this thesis. Interviews with potential users, satisfaction surveys, and historical data collection of library usage are the methods used. It is expected that the results of this research will help library users find and retrieve thesis and internship report data and improve the accessibility and availability of academic information at the UGP Faculty of Engineering. When search engine technology is used, it is expected that the time required for Information will increase productivity, improve efficiency, and support the academic development of students at UGP.

Technological advancements have brought significant transformations across various fields, including the application of machine learning in recommendation and classification systems. Machine learning leverages data processing, utilizes algorithms, and efficiently identifies patterns to produce accurate recommendations and predictions. This study aims to review machine learning-based recommendation system approaches, analyze model performance, and compare the algorithms used. A literature review was conducted by examining journals published in the past five years, focusing on algorithm implementation. The findings indicate that the Naïve Bayes algorithm delivers the best performance, achieving an accuracy of up to 97%. This algorithm is particularly well-suited for processing small to medium-sized datasets with high efficiency. The research provides comprehensive insights into the performance and limitations of various algorithms, serving as a valuable guide for future developments in the field.

In the era of artificial intelligence (AI), technology is growing rapidly and changing the way businesses are run. AI enables automation, big data analysis, and increased operational efficiency. However, this development also raises various ethical issues that companies need to pay attention to so as not to harm consumers, society, and the environment. This study explores the paradigm of artificial intelligence in the context of business ethics, focusing on its impacts, challenges, and potential benefits in the business world. This study uses a systematic literature review approach from various reputable sources. The article criteria include literature published from 2019 to 2024 with the theme of business ethics in the AI era, and other articles relevant to this study. Based on the search, 15 articles were determined that were relevant to the predetermined criteria. The results of the study found that in this study, AI is not only a technical tool to improve operational efficiency, but is also able to redefine business ethics in the modern era. AI offers great potential to overcome traditional ethical challenges and create new paradigms in business decision making. However, this opportunity is also accompanied by significant challenges, such as algorithmic bias, data privacy, and lack of regulation. This discussion will review the role of AI in business ethics, the benefits it brings, the challenges it faces, and the steps that can be taken to optimize the ethical use of AI.