Prabu Kresna Putra

In today's digital world, social media platforms such as X (formerly Twitter) are widely used for communication but also for illegal activities, such as selling illicit drugs. This study examines how posts from X related to illegal drug sales in Indonesia can be analyzed using machine learning techniques, with a focus on incorporating features related to personally identifiable information (PII) and web URLs. The main questions are how to use classification methods to detect these posts and which drugs are most often mentioned. A total of 11,777 posts from X were collected using web scraping. After cleaning and processing the data, five machine learning models were used. The support vector machine (SVM) model showed the best results with a weighted F1-score of 97.08%. The findings show that abortion pills are the most frequently mentioned drugs, followed by sexual enhancement and anesthetic drugs. This study also used visual tools to show the most common hashtags, posts distribution over time, and types of drugs discussed. This study underlines the importance of monitoring social media to protect public health and highlights how incorporating PII features can improve the detection of illegal activities. Future research should look at posts in other languages and from other platforms, considering PII features for enhanced analysis.

LLMs have new probabilistic safety problems that cannot be detected with the current benchmark measures of accuracy such as hallucination, automation bias, demographic unfairness, and privacy. In this chapter, we propose SafeMed, which is a multi-dimensional assessment framework consisting of ten dimensions for clinical safety evaluation in LLMs. SafeMed considers the following aspects: clinical accuracy, hallucination control, robustness, calibration, bias and fairness, ethics, transparency, privacy, safety of human-automation interaction, and operational feasibility. SafeMed is derived based on global standards such as WHO Guidelines for AI Ethics, EU AI Act, and FDA Good Machine Learning Practices (GMLP), and it uses Indonesian regulations as a case study involving ChatGPT-based clinical assistants, Med-PaLM, and RAG. Adversarial testing and human-in-the-loop oversight are proposed as minimum deployment standards. The chapter concludes with a practical evaluation pipeline and governance recommendations for clinicians, administrators, and policymakers.

Information on business conditions is an important concern for market players and regulators. Hard information relates to easily validated characteristics such as production levels and employment conditions. In contrast, soft information such as consumer and public perceptions—is subjective and difficult to verify. Although previous studies on hard and soft information mainly focus on microeconomics and banking, current developments in big data and machine learning enable broader applications in financial market analysis. This study combined VADER sentiment analysis and support vector machine (SVM) classification (accuracy=85%) to analyze economic news, followed by Granger causality and multiple linear regression to examine causal effects and predictive relationships. The findings reveal that negative news sentiment and the Indonesian Rupiah (IDR) exchange rate influence each other, while positive sentiment has no causal impact on the exchange rate. Both negative and positive sentiments affect gold prices, whereas gold price movements do not influence sentiment. Regression analysis shows that negative sentiment has a stronger effect in decreasing the IDR exchange rate than positive sentiment, with the model explaining approximately 20% of the variance. Integrating sentiment and exchange rate data enhances the predictive model for gold price forecasting and highlights the asymmetric roles of positive and negative news in financial dynamics.

Online reviews are becoming increasingly important for assessing tourist satisfaction, and although aspect-based sentiment analysis (ABSA) can offer in-depth insights, its use in the ASEAN tourism sector is still largely unexamined. Problem: This gap highlights a significant research issue, as there is insufficient knowledge re-garding the present condition, obstacles, and particular possibilities for applying advanced sentiment analysis methods in this distinctive, multicultural area . Objectives: The main aim of this study is to methodically identify worldwide research trends, highlight gaps, and describe the challenges of utilizing ABSA in the tour-ism industry, concentrating on their effects on ASEAN . Methods Used: To accomplish this, a systematic literature review (SLR) was performed on 61 chosen articles following the PRISMA methodology. Re-sults: The findings indicate a notable research gap marked by a narrow emphasis on the ASEAN region and the application of less sophisticated methods in current studies; the main challenges recognized include the varie-ty of languages, cultural differences, and a lack of datasets in local languages . Conclusion & Solution Offered: This organized review of literature concludes by providing suggestions for a plan-based approach that allows the region to use online reviews to improve its tourism competitiveness, emphasizing the necessity of creating local data resources, encouraging cooperation among different fields, and using data-driven tools to support small businesses .

Bilingual and multilingual education integrated with artificial intelligence (AI) has fundamentally altered conventional education paradigms, increasing the engagement of learning, individualized, and adaptive. This chapter examines AI technologies for instance Intelligent Tutoring Systems (ITS), adaptive learning algorithms, and Natural Language Processing (NLP) aid language acquisition by personalizing material delivery, offering real-time feedback, gamification, and promoting cultural competence. AI-driven curriculum caters to a variety of learning styles and boost understanding, motivation, and retention while addressing cognitive problems. However, issues such as algorithmic prejudice, data privacy, and the digital divide need fair technological access and ethical considerations. The balance of AI and human supervision is critical to developing effective learning experiences. Future breakthroughs in agentic AI will provide adaptive, context-aware interactions that improve language learning, but they must be carefully implemented to ensure inclusivity and ethical integrity.

In the digital age, mobile banking has become essential for facilitating efficient financial transactions, with the Wondr mobile banking application from Bank Negara Indonesia (BNI) emerging as a significant innovation in this sector. Designed to provide a secure and user-friendly experience, Wondr aims to meet the diverse needs of its customers. However, to enhance its service and ensure user satisfaction, BNI must actively engage with customer feedback. This study leverages user reviews from platforms like Google Play Store to gain insights into the strengths and weaknesses of the Wondr application. Employing text analysis techniques, we utilise topic modeling through Latent Dirichlet Allocation (LDA) to extract relevant themes from these reviews to identify key areas for improvement and generate targeted recommendations. The findings of this research are intended to inform the ongoing development of the Wondr application, ultimately enhancing user experience and reinforcing BNI’s position within the digital banking landscape.

The social movement #DaruratReformasi emerging in Indonesia since July 2024 has attracted widespread attention both nationally and internationally. This study aims to analyze the communication dynamics and interaction patterns within the social media network of the movement using text mining and network analytics approaches. Topic modeling identifies the dominant key issues in public discourse, while social network analysis reveals the main actors and influencers involved in information dissemination and public opinion formation. A modularity approach is employed to detect naturally formed discussion communities within the network, and temporal analysis illustrates the phases of the movement’s development from initiation to its peak in November 2024. The results indicate that social media serves as a strategic platform for social mobilization and political advocacy, with key actors distributed across interconnected communities. Additionally, the involvement of government institutions as central actors highlights the two-way communication dynamics within the digital public sphere. These findings underscore the urgency of understanding social network structures in the context of modern digital social movements and provide implications for public communication management and mass mobilization strategies in the digital era.