TechVista Journal of Emerging Information Systems

Harnessing Artificial Intelligence for Automation Efficiency and Innovation

Abdul Rahman Rahim — 2024-09-25

Artificial intelligence (AI) and automation are transforming organizational operations by enhancing efficiency, accuracy, and decision-making across multiple sectors. This study investigates how AI-driven automation is implemented and experienced within manufacturing, logistics, finance, healthcare, education, and digital service industries in Southeast Asia. The objective is to analyze productivity outcomes and human responses to rapid technological change. Using a qualitative multi-case study approach, data were collected through semi-structured interviews with managers and employees and analyzed using thematic coding to identify patterns in efficiency, skills, and governance. Results show that AI reduces manual workload, accelerates processing time, and improves service quality, confirming its role in driving digital innovation. However, technology adoption also raises challenges, including anxiety over job displacement, uneven digital literacy, and trust issues in automated systems. Organizations that provide structured upskilling, communication transparency, and ethical oversight report higher acceptance and better transformation outcomes. This study contributes to AI management literature by offering empirical insights from emerging economies while emphasizing the need for human-centered strategies. The findings suggest that successful automation requires balancing innovation with workforce empowerment to ensure equitable and sustainable digital progress.

Enhancing Data Privacy and Security in Cloud-Based Cybersecurity Frameworks: A Study on Homomorphic Encryption and Privacy-Preserving Mechanisms

Muhammad El-Rumi Ghazali — 2024-08-16

The rapid expansion of multi-cloud adoption has increased the urgency of ensuring cybersecurity and privacy during distributed data processing. This study proposes and evaluates a hybrid framework that combines homomorphic encryption, privacy-preserving analytics, and AI-driven anomaly detection to secure sensitive information while maintaining functional analytics in multi-cloud environments. The architecture was implemented using container-based orchestration and evaluated under simulated enterprise workloads involving encrypted computation and adversarial threat attempts. Experimental results show that the integrated system effectively protected data-in-use and significantly reduced observability of access patterns, thereby limiting opportunities for inference-based attacks. AI-driven monitoring achieved strong anomaly detection performance with low false-positive rates, demonstrating its value in identifying malicious behaviors beyond what cryptography alone can prevent. Although encryption introduced latency and additional resource demands, the overhead remained within acceptable boundaries for typical enterprise applications, especially when supported by elastic scaling and selective application of computationally intensive operations. Compliance assessment further revealed that operational controls such as key automation and tamper-evident logging are essential to meeting privacy regulations within distributed infrastructures. The findings indicate that robust cybersecurity and privacy in multi-cloud systems can be achieved through thoughtful integration of cryptographic safeguards and intelligent monitoring. This work contributes an empirically validated reference model for secure and privacy-aware cloud analytics and offers practical insights for future optimization and real-world adoption.

Challenges and Opportunities of Artificial Intelligence in Industrial Automation

Muh. Rochman — 2024-09-25

Artificial intelligence (AI) has become a key driver of industrial automation, reshaping production environments and redefining operational capabilities across global sectors. This study aims to examine the challenges and opportunities associated with AI implementation in industrial automation, focusing on efficiency gains, workforce implications, and governance requirements. A mixed qualitative synthesis and technology performance review were employed to evaluate AI readiness, adoption outcomes, and strategic enablers based on recent industrial case evidence and established scholarly frameworks. The results show that AI significantly enhances productivity, minimizes operational errors, and supports intelligent decision-making through automation of complex tasks. Moreover, the integration of AI fosters predictive maintenance, production flexibility, and accelerated innovation cycles. However, adoption is constrained by high initial investment, talent shortages, resistance to organizational change, ethical concerns, and cybersecurity risks. These challenges suggest that successful AI-driven automation depends not only on advanced technical infrastructures but also on coordinated policy support, workforce development, and strong digital governance. The study concludes that industrial transformation through AI represents both a technological opportunity and a socio-economic challenge, requiring holistic strategies to ensure its responsible and equitable advancement. The implications provide a foundation for future research exploring scalable models of AI adoption and long-term sustainability in industry.

A Teaching-Oriented Approach to Post-Cooling Mechanical Evaluation of Welded Joints by Students

M. Jaya Ramadhan — 2024-09-23

This study explores a teaching-oriented approach to post-cooling mechanical evaluation of welded joints, aiming to integrate quantitative metallurgical analysis with experiential learning in engineering education. The research sought to enhance students’ understanding of the relationship between thermal processes and mechanical behavior through direct involvement in experimental procedures. A quantitative method was employed using welded steel specimens subjected to two cooling conditionsnatural air cooling and controlled air jet cooling. Mechanical properties, including tensile strength, hardness, and grain size, were measured, and the data were analyzed statistically using analysis of variance and correlation tests to determine the influence of cooling rate on material performance. The results showed that controlled cooling produced finer grain structures and led to a 6–8% improvement in tensile strength and hardness compared to natural cooling. These findings confirm classical metallurgical theories regarding the Hall–Petch relationship and microstructural strengthening mechanisms. Beyond material analysis, the study demonstrated that student participation in quantitative experimentation fosters critical thinking, data literacy, and scientific reasoning. This integrated model effectively bridges the gap between theoretical instruction and professional engineering practice. The study concludes that combining quantitative evaluation with a teaching-oriented framework provides a replicable model for modern engineering curricula, promoting both technical competence and cognitive development. Future studies are encouraged to expand this model to diverse materials and thermal conditions to strengthen its generalizability.