Ninda Lutfiani

The rapid growth of e-commerce in Indonesia has enhanced the convenience and efficiency of digital transactions while simultaneously increasing the risk of fraud, thereby threatening consumer protection and trust in online commerce. This study aims to analyze the legal regulation of e-commerce fraud and eval- uate the effectiveness of consumer protection mechanisms in Indonesia. The research employs a normative juridical method using statutory, conceptual, and case-based approaches through the analysis of laws and regulations, judicial decisions, legal doctrines, and relevant literature. The findings indicate that e-commerce fraud commonly occurs through fictitious sales, misleading prod- uct information, identity misuse, phishing attacks, and the unauthorized use of personal data. Although Indonesia has established a legal framework through the Indonesian Criminal Code (KUHP), the Law on Electronic Information and Transactions (ITE Law), consumer protection regulations, and digital gover- nance policies, law enforcement continues to face challenges related to digital evidence, cybersecurity risks, cross-border transactions, and limited digital lit- eracy. The study also highlights the importance of platform accountability, digital identity verification, and technology-based fraud prevention mechanisms in strengthening consumer protection. This study concludes that the effective prevention of e-commerce fraud requires not only robust legal enforcement but also stronger digital governance, enhanced institutional coordination, effective cybersecurity measures, and greater public awareness to support a secure and sustainable digital commerce ecosystem in Indonesia.

The rapid growth of e-commerce in Indonesia has enhanced the convenience and efficiency of digital transactions while simultaneously increasing the risk of fraud, thereby threatening consumer protection and trust in online commerce. This study aims to analyze the legal regulation of e-commerce fraud and eval- uate the effectiveness of consumer protection mechanisms in Indonesia. The research employs a normative juridical method using statutory, conceptual, and case-based approaches through the analysis of laws and regulations, judicial decisions, legal doctrines, and relevant literature. The findings indicate that e-commerce fraud commonly occurs through fictitious sales, misleading prod- uct information, identity misuse, phishing attacks, and the unauthorized use of personal data. Although Indonesia has established a legal framework through the Indonesian Criminal Code (KUHP), the Law on Electronic Information and Transactions (ITE Law), consumer protection regulations, and digital gover- nance policies, law enforcement continues to face challenges related to digital evidence, cybersecurity risks, cross-border transactions, and limited digital lit- eracy. The study also highlights the importance of platform accountability, digital identity verification, and technology-based fraud prevention mechanisms in strengthening consumer protection. This study concludes that the effective prevention of e-commerce fraud requires not only robust legal enforcement but also stronger digital governance, enhanced institutional coordination, effective cybersecurity measures, and greater public awareness to support a secure and sustainable digital commerce ecosystem in Indonesia.

This study aims to analyze the optimization of Search Engine Optimization (SEO) strategies to increase journal visibility and author awareness in Technomedia Journal. In the era of digital publication, scientific journals are not only required to publish quality articles but also to be easily found through search engines and academic platforms. This study employed a quantitative approach by distributing questionnaires to 101 respondents, including authors, researchers, academics, and journal users familiar with Technomedia Journal. The data were analyzed using the Structural Equation Modeling (SEM-PLS) method with the help of SmartPLS software. The research model consists of SEO, Paper Quality (PQ), Open Journal Systems (OJS), and indexing and digital presence as independent variables, journal visibility as an intervening variable, and author awareness as a dependent variable. The results showed that indexing and digital presence have the strongest influence on journal visibility with a path coefficient value of 0.641. In addition, journal visibility also has a strong positive effect on author awareness with a path coefficient value of 0.670. The R-square value for journal visibility is 0.804, which means that 80.4% of the variation in journal visibility can be explained by the independent variables. Meanwhile, the R-square value for author awareness is 0.704, which means that 70.4% of the variation can be explained by journal visibility and SEO. These findings indicate that strengthening indexing, digital presence, article PQ, OJS management, and SEO strategies in an integrated manner can increase journal visibility and author awareness of Technomedia Journal.

The rapid development of digital technology has encouraged startupreneur footwear businesses to implement innovative digital branding strategies to attract and retain consumers. This study aims to analyze the influence of digital storytelling and visual branding on brand resonance in startupreneur footwear businesses. A quantitative approach was employed, with data collected through questionnaires, social media observation, and literature studies. A total of 100 respondents were selected using purposive sampling. Data analysis was conducted using SmartPLS to evaluate the measurement and structural models. The findings indicate that digital storytelling and visual branding have a positive and significant influence on brand resonance. Digital storytelling has a positive and significant effect on brand resonance (β = 0.49, t = 5.27), while visual branding also has a positive and significant effect (β = 0.38, t = 3.90). Together, digital storytelling and visual branding explain 65% of the variance in brand resonance (R² = 0.65). Digital storytelling through authentic and emotionally engaging content increases consumer engagement and emotional connection with the brand. Meanwhile, visual branding through consistent visual identity, color usage, logo, and social media design strengthens brand recognition and consumer trust. The integration of digital storytelling and visual branding can enhance customer loyalty, repeat purchase intention, and Electronic Word of Mouth (e- WOM). Furthermore, Instagram and TikTok play important roles in supporting digital branding strategies for footwear businesses. This study contributes to digital marketing and branding strategies while supporting the Sustainable Development Goals (SDGs), particularly SDG 8 and SDG 9.

Accelerating sustainable innovation is a critical mandate for emerging economies. However, the high failure rate of startups, primarily driven by inefficient founder-investor alignment and information asymmetry, remains a systemic obstacle. In Indonesia, this challenge is further exacerbated by traditional matchmaking processes that rely on social networks, often excluding high-potential Micro, Small, and Medium Enterprises (MSMEs). This study introduces and validates Artificial Intelligence Machine Learning Excellence Economy (AIMEE), a novel hybrid matchmaking framework designed to democratize access to capital and accelerate the twin transition of digitalization and sustainability. Utilizing a pilot dataset of 94 startup-investor pairs from five provinces in Java, we developed a policy-aware algorithm that integrates financial metrics with five entrepreneurial dimensions, including Sovereignty Scores based on alignment with the Sustainable Development Goals (SDGs). The framework employs a lightweight neural network optimized with SMOTE (Synthetic Minority Over-sampling Technique) to address data scarcity and class imbalance. Empirical validation shows that AIMEE achieves an F1-Score of 0.83, Recall of 0.82, and ROC-AUC of 0.89, significantly outperforming traditional Random Forest baselines by 12%. The model effectively identifies patterns of potential collaboration, overcoming the limitations of rigid networking. These findings confirm that AI-driven matchmaking can serve as a robust digital infrastructure to operationalize the Asta Cita vision and SDG 9, contributing to a more inclusive, resilient, and competitive innovation ecosystem in Indonesia.

Achieving Sustainable Competitive Advantage (SCA) for green agricultural products in demanding international markets, particularly Europe, requires strategic integration beyond conventional marketing approaches. Although green branding and digital marketing have been widely recognized, the synergistic mechanism through which digital strategies, green value propositions, and market penetration collectively strengthen SCA remains underexplored. This study aims to develop and validate an integrated model that explains how Digital Traffic Management, Green Product Value, and Market Penetration contribute to SCA while supporting sustainability-oriented business growth. This study employs Structural Equation Modeling (SEM) because it provides stronger analytical robustness than conventional regression through latent variable estimation, construct validation, and comprehensive goodness-of-fit evaluation. The \textbf{findings} show that Market Penetration is the strongest predictor of SCA (β = 0.44), indicating that market expansion through digital channels significantly enhances long-term competitiveness. The results also reveal a positive reinforcement cycle between Green Product Value and SCA, demonstrating a virtuous loop of continuous improvement, digital adaptability, and green competitiveness. Furthermore, Market Penetration and Digital Traffic Management support SDG 8, SDG 9, and SDG 17 by encouraging sustainable economic growth, technology-based innovation, collaborative business ecosystems, and stronger digital partnerships for SMEs. This study provides a robust empirical foundation and strategic blueprint for SMEs, policymakers, and digital marketers to enter and grow in international markets while strengthening sustainable competitiveness and contributing meaningfully to a greener future.

Heart attacks and brain strokes have become major global health concerns due to the increasing number of cases occurring among not only older adults but also middle-aged and younger populations. Unhealthy lifestyles, smoking habits, obesity, hypertension, and diabetes have significantly contributed to the rising risk of cardiovascular and neurological diseases. This study aimed to investigate heart attack and brain stroke risks across different age groups from a data science perspective while evaluating the effectiveness of machine learning techniques for disease prediction. The research employed quantitative experimental analysis using healthcare datasets containing clinical and lifestyle-related variables such as age, blood pressure, cholesterol level, glucose level, smoking behavior, body mass index, and diabetes condition. Two supervised machine learning algorithms, namely Support Vector Machine (SVM) and Extreme Gradient Boosting (XGBoost), were implemented to classify disease risks. The experimental results showed that the SVM model achieved superior predictive performance with an accuracy of 99.5%, while XGBoost achieved 94.3% accuracy. The novelty of this study lies in integrating multi-age cardiovascular and neurological risk analysis with artificial intelligence-based predictive modeling to support the development of an intelligent mobile healthcare alert system. The study concluded that machine learning technologies have strong potential to improve early disease prediction, preventive healthcare, and proactive patient monitoring in modern healthcare environments.

The strategic role of visual design and digital branding training in enhancing the competitiveness of micro, small, and medium enterprises (MSMEs) has become increasingly important in the digital economy era. Many MSMEs still face limitations in market reach and weak brand identity due to inadequate visual communication skills and minimal implementation of digital branding strategies. These challenges reduce their competitiveness in an increasingly competitive online marketplace. This community service program aims to analyze the impact of structured training in visual design and digital branding on improving brand perception, market visibility, and sales performance of MSMEs. This program employs a mixed-method approach by combining pre-test and post-test surveys, in-depth interviews, and direct observation. The participants consisted of 50 MSME actors who took part in a a three-month training program covering logo design, product packaging, social media branding, and digital marketing content creation. Quantitative data were analyzed using paired statistical tests, while qualitative data were examined through thematic analysis. The results indicate a significant improvement in brand consistency, visual attractiveness, digital engagement, and online sales growth after the participants completed the training program. Participants also reported increased confidence in managing brand assets and implementing digital communication strategies. It can be concluded that visual design and digital branding training represent an effective intervention to strengthen the competitiveness of MSMEs, support sustainable business growth, and promote the development of a resilient creative economy in the era of digital transformation.

Artificial intelligence (AI) deployment in critical domains requires machine learning systems that are not only accurate but also robust, interpretable, fair, and aligned with responsible governance principles. However, conventional machine learning evaluation approaches often prioritize predictive performance and computational efficiency while giving limited attention to ethical accountability, transparency, regulatory compliance, and sustainability. This study aims to develop a trustworthy machine learning evaluation framework for robust and interpretable AI systems. The focus of the study is the evaluation of intelligent systems across healthcare, finance, and transportation, where reliability and accountability are essential for real-world deployment. A qualitative case study approach was employed through expert interviews, literature analysis, document review, and cross-domain case comparisons to identify key evaluation dimensions. The findings show that trustworthy evaluation should integrate technical indicators, including accuracy, robustness, and interpretability, with broader dimensions such as fairness, accountability, governance compliance, and social responsibility. The proposed framework provides a structured model for assessing intelligent systems beyond conventional performance metrics. It also supports better consistency in interpretability assessment, stronger fairness evaluation, and improved alignment with international AI governance expectations. This study contributes to the development of responsible AI by offering a practi- cal evaluation framework that can guide researchers, developers, and institutions in designing machine learning systems that are reliable, transparent, and socially accountable. The framework has implications for sustainable and compliant AI implementation in high-impact sectors.

Modern enterprises increasingly rely on interconnected digital infrastructures, which significantly increase exposure to cyber threats and trust-related vulnerabilities. Traditional security mechanisms often face limitations in providing real-time detection, transparent data validation, and resilient protection across distributed environments. This study aims to develop and evaluate a Blockchain-Augmented AI Architecture to enhance cybersecurity performance and strengthen operational trust within digital business ecosystems. The research adopts a quantitative experimental approach by integrating machine learning–based anomaly detection with a private blockchain layer for secure event logging and tamper-proof verification. The system is tested using simulated enterprise network traffic, and performance is evaluated based on detection accuracy, latency, throughput, and integrity validation efficiency. A comparative analysis is also conducted against conventional centralized cybersecurity models. The results demonstrate that the proposed architecture significantly improves cybersecurity robustness, achieving higher anomaly detection accuracy, reduced false positives, and faster verification time compared to baseline models. Additionally, blockchain integration enhances operational trust by ensuring immutable audit trails, decentralized consensus, and transparent data prove- nance. Overall, the combined system exhibits superior reliability and resilience across simulated network scenarios. This study concludes that integrating AI- driven detection with blockchain technology provides a more secure, transparent, and trustworthy cybersecurity framework for modern enterprises, while of- fering strong potential to support scalable digital transformation and address emerging threats in distributed environments