Sawali Wahyu

The application of AI in supply chain management brings significant changes to various critical functions, from demand forecasting to security and risk mitigation. In this article, each aspect will be discussed based on empirical evidence and the latest technology. AI technology enables companies to analyze large amounts of data in real time, detect demand patterns, optimize inventory, and make automated decisions that previously required lengthy manual processes. Additionally, AI can manage the complexity of highly dynamic global supply chains by enhancing visibility, resilience, and operational efficiency. Through the integration of machine learning, deep learning, and reinforcement learning, AI can adaptively adjust procurement strategies, logistics scheduling, and supplier evaluations. Even in terms of security and risk prevention, AI can proactively predict disruptions, analyze supply chain vulnerabilities, and detect anomalies and potential fraud. Therefore, AI is not just a tool but a strategic key to achieving a resilient, adaptive, and competitive supply chain.

Mental health disorders affect 970+ million people globally, with 75%+ treatment gaps in developing countries. This chapter examines AI's transformative potential through machine learning, natural language processing, computer vision, and wearable technologies enabling early detection, personalized interventions, and continuous monitoring. Current applications show promise: CBT chatbots like Woebot and Wysa reduce depression/anxiety symptoms; digital phenotyping platforms detect relapses days earlier; computer vision systems enhance PTSD screening. However, challenges exist including algorithmic bias against minorities, privacy concerns, system opacity, and over-reliance risks. The chapter explores future directions emphasizing human-AI collaborative frameworks, federated learning for privacy protection, and explainable AI for transparency. Successful implementation requires interdisciplinary collaboration between computer scientists, healthcare professionals, ethicists, and policymakers for equitable mental health care delivery.

As digital marketing becomes increasingly driven by algorithmic surveillance and behavioral targeting, consumers are developing resistance strategies to regain autonomy and control over their digital experiences. This chapter examines ad blockers, digital detox practices, and de-influencing movements as socio-technical responses to pervasive monitoring, persuasive design, and platform power. By analyzing these resistance strategies through ethical, political, and cultural lenses, the chapter explores how consumer trust, agency, and accountability are being renegotiated in data-driven marketing ecosystems and considers their implications for the future of responsible digital marketing.

Mental health disorders affect 970+ million people globally, with 75%+ treatment gaps in developing countries. This chapter examines AI's transformative potential through machine learning, natural language processing, computer vision, and wearable technologies enabling early detection, personalized interventions, and continuous monitoring. Current applications show promise: CBT chatbots like Woebot and Wysa reduce depression/anxiety symptoms; digital phenotyping platforms detect relapses days earlier; computer vision systems enhance PTSD screening. However, challenges exist including algorithmic bias against minorities, privacy concerns, system opacity, and over-reliance risks. The chapter explores future directions emphasizing human-AI collaborative frameworks, federated learning for privacy protection, and explainable AI for transparency. Successful implementation requires interdisciplinary collaboration between computer scientists, healthcare professionals, ethicists, and policymakers for equitable mental health care delivery.

The application of AI in supply chain management brings significant changes to various critical functions, from demand forecasting to security and risk mitigation. In this article, each aspect will be discussed based on empirical evidence and the latest technology. AI technology enables companies to analyze large amounts of data in real time, detect demand patterns, optimize inventory, and make automated decisions that previously required lengthy manual processes. Additionally, AI can manage the complexity of highly dynamic global supply chains by enhancing visibility, resilience, and operational efficiency. Through the integration of machine learning, deep learning, and reinforcement learning, AI can adaptively adjust procurement strategies, logistics scheduling, and supplier evaluations. Even in terms of security and risk prevention, AI can proactively predict disruptions, analyze supply chain vulnerabilities, and detect anomalies and potential fraud. Therefore, AI is not just a tool but a strategic key to achieving a resilient, adaptive, and competitive supply chain.

Artificial intelligence has become a key driver of personalization in the fashion industry, enabling targeted recommendations, dynamic content, and individualized shopping experiences. While AI powered personalization enhances relevance and consumer satisfaction, it also raises ethical concerns related to autonomy, privacy, and invisible influence. This chapter examines the tension between AI enabled personalization and consumers' freedom to make independent fashion choices. It explores the technical and psychological mechanisms behind recommendation systems and analyzes their impact on sustainable versus fast fashion consumption. Drawing on consumer psychology, digital ethics, and fashion technology, the chapter proposes a responsible personalization framework that prioritizes transparency, relevance, and consumer empowerment.

Cyber threats such as malware injection, web shell exploitation, phishing, and lateral movement increasingly challenge host-level security mechanisms. Conventional Host-Based Intrusion Detection Systems (HIDS) are commonly deployed in stand-alone configurations, resulting in limited cross-host visibility, delayed incident response, and lack of integration with external threat intelligence. This study proposes an agent-based HIDS architecture with centralized orchestration and real-time threat intelligence integration to improve detection accuracy and response efficiency. The system is developed using an experimental approach based on the NIST SP 800-61 Rev.2 incident handling framework, covering preparation, detection and analysis, containment and recovery, and post-incident evaluation. Each host deploys a lightweight agent that monitors file system activities, generates cryptographic hash values, and sends artifact metadata to a centralized orchestration server. The server performs parallel validation using external threat intelligence services and executes automated containment actions. Experimental results in a multi-host virtual environment show a False Positive Rate (FPR) of 3.2%, Mean Time to Detect (MTTD) of 4.8 seconds, and Mean Time to Respond (MTTR) of 6.5 seconds, with a 38% improvement compared to manual monitoring. These findings indicate that centralized orchestration combined with threat intelligence integration enhances detection precision, scalability, and incident response effectiveness in HIDS

 SMP Negeri 38 Bekasi faces low student learning outcomes in Science, particularly in human body anatomy material, as reflected in students' failure to meet the Minimum Mastery Criteria (KKM). This issue stems from the abstract nature of anatomy topics, which demand strong visualization skills, while current instruction still relies on conventional, text-based, two-dimensional media that are less interactive. To address this gap, this study develops a mobile-based learning application that integrates Augmented Reality (AR) and a gamification approach to present three-dimensional models of human body organs, complemented by interactive quizzes, a scoring system, and a leaderboard. The application was built using the Multimedia Development Life Cycle (MDLC) method. Usability testing with the System Usability Scale (SUS) showed that the AR Human Body Anatomy application achieved a score of 84.05 (Excellent), while the Management Dashboard achieved 80.5 (Excellent), indicating that both applications are highly usable and well-received by users. These findings suggest that AR-based learning media with gamification can support students' understanding of human body anatomy through three-dimensional visualization, and has the potential to increase learning motivation and engagement. This study contributes a practical AR-gamification model for science education that can serve as an alternative to conventional visualization methods in anatomy instruction, particularly for the achievement of the KKM standard.

Sistem pelaporan penunjang medis pada Sistem Informasi Manajemen Rumah Sakit (SIMRS) berperan penting dalam mendukung proses operasional dan pengambilan keputusan. Namun, pada implementasi sistem yang berjalan, proses pelaporan sering mengalami waktu respons yang sangat lama akibat eksekusi query kompleks secara langsung pada basis data operasional. Pada kondisi awal, modul Laboratorium dengan volume data sekitar 100.000 baris dan modul Radiologi dengan sekitar 5.000 baris data memerlukan waktu pemrosesan lebih dari 20 menit, sedangkan modul Patologi Anatomi dengan sekitar 200 baris data membutuhkan waktu sekitar 6 detik. Penelitian ini bertujuan mengoptimalkan kinerja sistem pelaporan penunjang medis melalui penerapan strategi optimalisasi pada level basis data dan backend. Metode yang digunakan meliputi penerapan indexing dan materialized view untuk meningkatkan efisiensi query, pemrosesan asinkron pada backend, serta integrasi Redis dan MinIO sebagai mekanisme caching, pelacakan progres pembuatan laporan, dan penyimpanan hasil laporan dalam format JSON. Penelitian ini bertujuan mengoptimalkan kinerja sistem pelaporan penunjang medis melalui penerapan strategi optimalisasi pada level basis data dan backend. Metode yang digunakan meliputi penerapan indexing dan materialized view untuk meningkatkan efisiensi query, pemrosesan asinkron pada backend, serta integrasi Redis dan MinIO sebagai mekanisme caching, pelacakan progres pembuatan laporan, dan penyimpanan hasil laporan dalam format JSON.

The increasing adoption of electronic certificates in academic and professional environments raises critical challenges related to authenticity, data integrity, and verification reliability. Conventional certificate management systems commonly rely on centralized architectures and manual validation procedures, which are vulnerable to manipulation, duplication, and single points of failure (SPoF). This study proposes a blockchain-based electronic certificate verification system implemented on a private Hyperledger Fabric network using smart contracts. The system records certificate verification metadata on a distributed ledger to ensure integrity and traceability while maintaining storage efficiency. Smart contracts automate the issuance and validation lifecycle, enabling transparent and tamper-resistant certificate management. The verification process is conducted by comparing document authentication data with records stored on the blockchain. Experimental evaluation demonstrates that the proposed system can accurately identify document alterations and consistently distinguish between valid and invalid certificates. The results indicate that the integration of blockchain and smart contracts as an active validation mechanism enhances transparency, reduces dependence on centralized authorities, and improves trust in mobile-based digital credential systems. Therefore, the proposed approach provides a secure and reliable framework for electronic certificate verification in academic environments.