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This study aims to design and develop a waste complaint information system at the Environmental Agency of West Sumba Regency to improve the effectiveness of public services. The current problem lies in the manual complaint process, which leads to delays in handling reports, poor data documentation, and limited service transparency. This research employs a qualitative descriptive approach with data collection techniques including observation, interviews, and documentation studies. The system development adopts the Waterfall method, which consists of requirement analysis, system design, implementation, testing, and maintenance stages. The system is modeled using Unified Modeling Language (UML), including use case diagrams, activity diagrams, and sequence diagrams to provide a structured representation of the system. This approach is considered effective as it ensures a systematic and well-organized development process. The results indicate that the developed system facilitates the public in submitting complaints online and assists the agency in managing complaint data in an integrated manner. Furthermore, the system enhances response time, transparency, and service efficiency. Therefore, this waste complaint information system can serve as a technological solution to improve the quality of public services.

This research aims to analyze the implementation of the "Jalak Wadul Mas" (Jawa Timur Layanan Pengaduan Warga dan Dukungan Masyarakat/East Java Citizen Complaint Service and Community Support) innovation program in improving the welfare of people with social welfare problems (PMKS) in East Java Province. The Social Service of East Java Province developed this program as an integrated digital platform for complaint handling, social assistance distribution, and empowerment of vulnerable groups. Using the policy implementation theory from Edward III, this study examines four critical factors: communication, resources, disposition, and bureaucratic structure. This qualitative research employs a descriptive approach, with data collected through in-depth interviews, observation, and documentation at the Social Service of East Java Province during June-August 2025. Informants include program managers, field social workers, PMKS beneficiaries, and community stakeholders. The results indicate that the Jalak Wadul Mas program has successfully served 45,678 PMKS across 38 districts/cities in East Java, with a 78% complaint resolution rate and average response time of 3 working days. The program integrates multiple services, including emergency assistance, rehabilitation referrals, skills training, and economic empowerment. Key success factors include strong leadership commitment, adequate technology infrastructure, and collaborative networks with community organizations. Challenges remain in human resource capacity, internet connectivity in remote areas, and cross-sectoral coordination. This study recommends strengthening digital literacy training for beneficiaries, expanding mobile service units, developing real-time monitoring dashboards, and establishing sustainable funding mechanisms.

The increasing complexity and scale of modern cybersecurity threats necessitate the development of advanced systems capable of efficiently detecting, analyzing, and mitigating incidents in real time. This paper proposes an automated framework for digital forensics and incident response that leverages big data analytics and real time network traffic profiling. The framework integrates cutting-edge technologies, including Apache Spark for real time data processing and Hadoop for scalable data storage, combined with machine learning models like LSTM and Autoencoders to detect anomalies and threats in network traffic. By automating the process of incident detection and response, this framework significantly reduces the time required to identify threats and improves the accuracy of forensic evidence correlation across heterogeneous network environments. The study highlights the advantages of using machine learning models and big data tools to address the limitations of traditional manual and semi-automated systems, which often struggle to keep pace with large-scale data generation. Testing results demonstrate that the proposed framework can handle large data volumes efficiently, providing real time, actionable insights with significantly reduced response times. Additionally, the framework improves forensic analysis by enabling the correlation of evidence from different devices and protocols, making it more effective than traditional methods in identifying the root cause of security incidents. However, challenges related to data heterogeneity, scalability, and system integration were encountered during testing. The proposed framework holds promise for significantly enhancing the efficiency and effectiveness of cybersecurity operations, with future work focusing on further integration of advanced AI techniques and machine learning models for dynamic and adaptive incident response.

The increasing sophistication of cyber threats has rendered traditional cybersecurity models insufficient in safeguarding enterprise networks. This study introduces a risk aware cybersecurity governance model that integrates real time threat intelligence with predictive anomaly detection to proactively mitigate potential threats. By leveraging advanced machine learning and AI techniques, the model enhances the ability to identify and address cyber threats before they can escalate into significant incidents. The model’s ability to predict anomalies, analyze real time threat intelligence feeds, and provide early warnings allows for faster response times and reduced risk exposure compared to traditional reactive models. Through simulations and real-world use cases, the proposed model demonstrated a 30% reduction in response time and a 25% decrease in overall risk exposure, showing its potential to improve security decision-making and resilience in dynamic threat environments. Unlike traditional models that rely on static rules and periodic policies, the proposed model uses predictive analytics to stay ahead of evolving threats, ensuring continuous monitoring and rapid adaptation. This proactive approach enhances organizational resilience, particularly in handling sophisticated cyber threats such as ransomware, malware, and phishing attacks. Despite its effectiveness, challenges such as data overload, scalability, and the need for interpretability in AI models remain. Future research will focus on refining predictive models, improving scalability for larger networks, and enhancing the explainability of machine learning models to foster greater trust in automated cybersecurity systems. This study contributes to the ongoing evolution of cybersecurity governance by demonstrating the value of integrating predictive and real time monitoring technologies for enhanced threat detection and mitigation.