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Digitus : Journal of Computer Science Applications
Published by PT Penerbit Ilmiah Indonesia
ISSN : -     EISSN : 30313244     DOI : https://doi.org/10.61978/digitus
Core Subject : Science,
Computer Science & IT Decision Sciences, Operations Research & Management
Digitus : Journal of Computer Science Applications with ISSN Number 3031-3244 (Online) published by Indonesian Scientific Publication, is a leading peer-reviewed open-access journal. Since its establishment, Digitus has been dedicated to publishing high-quality research articles, technical papers, conceptual works, and case studies that undergo a rigorous peer-review process, ensuring the highest standards of academic integrity. Published with a focus on advancing knowledge and innovation in computer science applications, Digitus highlights the practical implementation of computer science theories to solve real-world problems. The journal provides a platform for academics, researchers, practitioners, and technology professionals to share insights, discoveries, and advancements in the field of computer science. With a commitment to fostering interdisciplinary approaches and technology-driven solutions, the journal aligns itself with global challenges and contemporary technological trends.
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 5 Documents
 1 
Search results for , issue "Vol. 3 No. 4 (2025): October 2025" : 5 Documents clear
Enhancing Software Quality Through Automated Code Review Tools: An Empirical Synthesis Across CI/CD Pipelines Gunawan, Budi; Sitorus, Anwar T
Digitus : Journal of Computer Science Applications Vol. 3 No. 4 (2025): October 2025
Publisher : Indonesian Scientific Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61978/digitus.v3i4.956

Abstract

Automated Code Review Tools (ACRT) have become increasingly integral to modern software development workflows, particularly within continuous integration and deployment (CI/CD) environments. This study aims to evaluate the effectiveness of ACRT in improving software quality, accelerating vulnerability remediation, and enhancing developer productivity. Using a combination of empirical analysis, industry case studies, and academic benchmarks, we examine how tools such as SonarQube, CodeQL, Copilot Autofix, and secret scanners impact key quality metrics including defect density, Mean Time to Repair (MTTR), and pull request (PR) throughput. A quasi experimental design was employed using Interrupted Time Series (ITS) and Regression Discontinuity Design (RDD) to measure longitudinal outcomes across six open source and enterprise projects. Results indicate that defect density decreased by 15–30% following ACRT adoption, accompanied by notable improvements in security MTTR. For example, Copilot Autofix reduced XSS remediation times from 180 minutes to just 22 minutes, underscoring the tool’s potential for accelerating vulnerability management. PR throughput also increased by up to 40%. However, this efficiency gain coincided with a 20–30% decline in human code review interactions, highlighting a trade-off between automation benefits and the reduced depth of manual oversight. We conclude that ACRT tools, when integrated thoughtfully into development pipelines, can deliver measurable improvements in software quality and responsiveness. However, sustained benefits require careful tuning, contextual alerting, and a hybrid review strategy that maintains human involvement to preserve long term maintainability.
Real Time Mobility Intelligence: Evaluating Kafka Based Pipelines in Global Smart Transit Systems Sugianto; Arainy, Corizon Sinar
Digitus : Journal of Computer Science Applications Vol. 3 No. 4 (2025): October 2025
Publisher : Indonesian Scientific Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61978/digitus.v3i4.959

Abstract

Real-time streaming architectures are redefining the landscape of urban transit analytics by enabling low latency, data driven decision making. This study evaluates and compares the real time data processing capabilities of public transit systems in London, New York, and Singapore. The objective is to determine how architectural choices, data freshness, and machine learning integration influence key performance indicators such as latency, ETA accuracy, and anomaly detection. The methodology involves a multi city case study, where Kafka based pipelines integrated with Apache Flink and Spark were assessed for ingestion, processing, and service delivery. Datasets included GTFS Realtime, SIRI feeds, and contextual APIs (e.g., speed bands and crowd density). Metrics for evaluation included feed latency, mean absolute error (MAE) and root mean square error (RMSE) for ETA, and response times for anomaly detection. The results demonstrate that Singapore’s transit system outperformed its counterparts with the lowest latency (~12s), highest ETA accuracy (MAE = 18s; RMSE = 25s), and superior anomaly detection via multi sensor fusion. London and New York, while technologically robust, faced constraints due to longer feed update intervals and integration complexities. Kafka ML's online learning enhanced model adaptability, significantly reducing ETA prediction errors across dynamic conditions. Furthermore, stress testing revealed Singapore’s architecture as the most resilient under peak load. The study concludes that the effectiveness of real-time urban transit systems depends on harmonizing streaming infrastructure... Singapore’s architecture may serve as a potential reference model for other cities, while recognizing contextual differences in implementation. Singapore’s architecture offers a scalable template for other cities. Ethical considerations, including data governance and passenger privacy, are essential for sustainable implementation.
Evaluating Deep Learning Models for Humanitarian Sentiment Classification in Crisis Tweets: A Benchmark Study Junaedi, Edi
Digitus : Journal of Computer Science Applications Vol. 3 No. 4 (2025): October 2025
Publisher : Indonesian Scientific Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61978/digitus.v3i4.975

Abstract

Social media platforms have emerged as essential channels for real time crisis communication, offering valuable insights into public sentiment and humanitarian needs during emergencies. This study benchmarks the performance of state of the art deep learning models for classifying sentiment and humanitarian relevance in crisis related tweets. Using publicly available datasets CrisisMMD, HumAID, and CrisisBench we evaluate three architectures: IDBO CNN BiLSTM, BERTweet, and CrisisTransformers. These models were assessed using cross validation and standard performance metrics (accuracy, F1 score, precision, and recall). Results indicate that CrisisTransformers outperform both traditional CNN LSTM hybrids and general purpose transformers, achieving an accuracy of 0.861 and F1 score of 0.847. Domain specific pretraining significantly enhances contextual understanding, particularly in multilingual and ambiguous tweet scenarios. While transformer models offer superior classification capabilities, their computational complexity poses challenges for real time deployment. Additionally, operational risks, such as data bias and misinformation, necessitate careful management through structured human oversight and the integration of explainable AI mechanisms. This research provides a robust comparison of NLP models for crisis applications and recommends strategies for effective deployment, including bias mitigation and fairness aware learning. The findings contribute to building ethical and efficient NLP systems for humanitarian response.
Smart Farming Technologies for Global Food Security: A Review of Robotics and Automation Yuni T, Veronika; Saromah; Gunawan, Budi
Digitus : Journal of Computer Science Applications Vol. 3 No. 4 (2025): October 2025
Publisher : Indonesian Scientific Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61978/digitus.v3i4.1076

Abstract

This narrative review explores the role of robotics and automation in precision agriculture, particularly in addressing global challenges such as food security, labor shortages, and environmental sustainability. A systematic literature search was conducted using Scopus, Web of Science, and other supplementary databases, focusing on studies from 2015 to 2025. Findings show that AI-based models and UAV monitoring can enhance crop yield by up to 20% and reduce water and fertilizer use by 30%. Smart irrigation, soft robotics, and autonomous systems also demonstrate effectiveness in specific applications like pruning, weeding, and aquaponics. Despite promising outcomes, adoption varies due to financial, infrastructural, and governance barriers, especially in developing regions. The review concludes that integrating robotics with AI, IoT, and UAVs has transformative potential for agriculture. Future research should prioritize system interoperability, dataset quality, and environmental impact assessments to support widespread, equitable implementation.
Enabling Sustainability Through the Internet of Things: A Narrative Review of Global Applications and Challenges Sucipto, Purwo Agus; Dewi, Ratna Kusuma
Digitus : Journal of Computer Science Applications Vol. 3 No. 4 (2025): October 2025
Publisher : Indonesian Scientific Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61978/digitus.v3i4.1077

Abstract

The Internet of Things (IoT) has emerged as a transformative framework with broad applications in healthcare, agriculture, energy, and urban systems. This review aims to synthesize current evidence on IoT adoption, assessing both its benefits and the challenges hindering large-scale implementation. Literature was systematically retrieved from major databases, including Scopus, Web of Science, PubMed, and Google Scholar, using targeted keywords and strict inclusion and exclusion criteria. Findings reveal consistent evidence of IoT’s contribution to efficiency and sustainability: precision agriculture improves yields and resource use, while smart energy systems reduce consumption in urban infrastructures. Comparative results demonstrate disparities between developed and developing countries, with advanced economies emphasizing integrated high-tech solutions and resource-limited settings prioritizing affordable, accessible innovations. The discussion highlights the critical role of systemic and policy factors, including regulatory support, infrastructural investment, and societal trust, in shaping adoption outcomes. Emerging technologies such as blockchain and machine learning show promise for addressing challenges of security and energy efficiency but require further empirical validation in real-world contexts. While current research supports IoT’s role in advancing sustainable development, significant gaps remain in understanding its long-term socio-economic impacts and scalability. The findings emphasize the urgency of policy interventions, inclusive strategies, and interdisciplinary research to fully realize IoT’s potential as a driver of sustainable and equitable global transformation.

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