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All Journal JIK Jurnal Ilmu Komputer Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal Minfo Polgan (JMP) Digitus : Journal of Computer Science Applications
Era Sari Munthe
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Computer Science & IT Decision Sciences, Operations Research & Management Library & Information Science Mathematics Social Sciences

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Journal : Digitus : Journal of Computer Science Applications

 1 
Cloud-Native Transformations: Microservices, Kubernetes, and Security Frameworks in Practice Munthe, Era Sari
Digitus : Journal of Computer Science Applications Vol. 3 No. 2 (2025): April 2025
Publisher : Indonesian Scientific Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61978/digitus.v3i2.880

Abstract

Cloud-native application development is reshaping how modern organizations build, deploy, and manage software. This narrative review aims to synthesize recent literature on the adoption of cloud-native paradigms, particularly focusing on microservices architecture, containerization, orchestration tools, security frameworks, and AI-driven resource management. Using Scopus, IEEE Xplore, ACM Digital Library, SpringerLink, and Google Scholar as primary databases, the review applies Boolean keyword combinations to identify relevant peer-reviewed publications. Studies were selected based on their alignment with defined inclusion criteria, emphasizing empirical insights on cloud-native technologies. The findings reveal that microservices enhance system scalability and business agility, while containerization offers portability and efficient resource utilization. Orchestration tools, especially Kubernetes, enable automated deployment and management across complex environments. Security integration through DevSecOps and Policy-as-Code frameworks strengthens defense mechanisms against cyber threats. Furthermore, AI-supported orchestration improves efficiency in resource allocation and system responsiveness. The discussion underscores the necessity of systemic support, including organizational policies, talent development, and cross-functional collaboration, in ensuring successful adoption. This review concludes that cloud-native success demands more than technical innovation; it requires strategic alignment between technology, human capital, and governance. Policymakers and organizational leaders must invest in comprehensive frameworks that support security, adaptability, and continuous learning. Future studies should expand the scope by evaluating cloud-native transformations across industries and developing scalable best practices for AI integration and policy deployment.
Generalizable and Energy Efficient Deep Reinforcement Learning for Urban Delivery Robot Navigation Samroh; Munthe, Era Sari
Digitus : Journal of Computer Science Applications Vol. 3 No. 2 (2025): April 2025
Publisher : Indonesian Scientific Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61978/digitus.v3i2.954

Abstract

The increasing demand for contactless urban logistics has driven the integration of autonomous delivery robots into real world operations. This study investigates the application of Deep Reinforcement Learning (DRL) to enhance robot navigation in complex urban environments, focusing on three advanced models: MODSRL, SOAR RL, and NavDP. MODSRL employs a multi objective framework to balance safety, efficiency, and success rate. SOAR RL is designed to handle high obstacle densities using anticipatory decision making. NavDP addresses the sim to real gap through domain adaptation and few shot learning. The models were trained and evaluated in simulation environments (CARLA, nuScenes, Argoverse) and validated using real world deployment data. Evaluation metrics included success rate, collision frequency, and energy efficiency. MODSRL achieved a 91.3% success rate with only 4.2% collision, outperforming baseline methods. SOAR RL showed robust performance in obstacle rich scenarios but highlighted a safety efficiency trade off. NavDP improved real world success rates from 50% to 80% with minimal adaptation data, demonstrating the feasibility of sim to real transfer. The results confirm the effectiveness of DRL in advancing autonomous delivery navigation. Integrating domain generalization, hybrid learning, and real time adaptation strategies will be essential to support large scale urban deployment. Future research should prioritize explainability, continual learning, and user centric navigation policies.
Balancing Performance, Cost, and Sustainability in Software Engineering Munthe, Era Sari; Marthalia, Lia
Digitus : Journal of Computer Science Applications Vol. 3 No. 3 (2025): July 2025
Publisher : Indonesian Scientific Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61978/digitus.v3i3.1075

Abstract

The environmental impact of Information and Communication Technology (ICT) has become a global concern, especially with the increasing energy consumption of data centers, artificial intelligence, and software systems. This narrative review explores how green computing and sustainable software engineering practices can address these environmental challenges. Using a systematic search across Scopus, IEEE Xplore, Web of Science, and Google Scholar, the review identifies best practices in integrating sustainability across the software lifecycle. Key findings reveal that energy-efficient coding, optimized database systems, and green AI strategies can significantly reduce energy use and carbon emissions. Cloud and serverless architectures offer additional sustainability potential when paired with proper energy monitoring tools. The review also highlights how educational reforms and organizational governance play essential roles in promoting eco-conscious practices. However, challenges persist. These include limited awareness among practitioners, lack of standardized metrics for software sustainability, and weak cross-disciplinary collaboration. Regional disparities also influence adoption, with Europe leading due to stronger policy frameworks, while Asia and North America show mixed trends. This study concludes that integrating sustainability into software engineering requires both technical innovations and systemic reforms. Future research should focus on empirical validation of sustainability frameworks, development of standard evaluation metrics, and promotion of interdisciplinary approaches. Sustainable ICT practices are not only an environmental necessity but also a strategic imperative for the future of digital innovation.
Internet of Things-Based Home Trash Capacity Tracking System with Instant Notifications Munthe, Era Sari; Diantoro, Karno; Herwanto, Agus
Digitus : Journal of Computer Science Applications Vol. 2 No. 3 (2024): July 2024
Publisher : Indonesian Scientific Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61978/digitus.v2i3.257

Abstract

Garbage created from routine household activities is collected and stored in household garbage cans. Location This garbage makes rubbish collection easier to live in and helps to maintain a clean household environment. Household garbage cans are often designed to fit specific demands and feature a tight-fitting cover to keep sickness and animals out and to minimize unwanted odors. The layout To stop the spread of bacteria or fungus, something must be easy to clean. Lack of technology to monitor garbage bin fullness and inability to precisely monitor fill capacity, which can lead to trash overflow, offensive odors, and animal nuisances. Thus, volume sensorization techniques and Internet of Things (IoT) technologies are the answers to this challenge. To enable real-time waste capacity volume monitoring and to give users level information about trash charging through the Blynk platform, the system will deliver When the garbage can is full, an alarm sensor-equipped warning will ring. The Arduino IDE and the C programming language are the software used. The findings of the study demonstrate that the garbage can capacity monitoring system The information about waste filling levels that are provided in real-time by this IoT-based system is effective. By using this approach, homeowners can easily keep a clean and healthy home environment by knowing when it's time to remove the trash.
Co-Authors Azis, Nur Diantoro, Karno Erwin Gunawan Herwanto, Agus Irwanto, Dola Istianingsih, Nanik Marthalia, Lia Samroh Sucipto, Purwo Agus