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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 45 Documents
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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.
Personalized Causal Targeting in E-commerce: An Uplift Modeling Approach for Campaign Optimization Marthalia, Lia
Digitus : Journal of Computer Science Applications Vol. 2 No. 1 (2024): January 2024
Publisher : Indonesian Scientific Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61978/digitus.v2i1.1090

Abstract

Evaluations of e-commerce marketing campaigns frequently depend on summary metrics like conversion and click-through rates, which fail to reveal the true causal effect of promotional activities. This study employs uplift modeling to estimate the individual-level causal impact of marketing interventions, clarifying where such approaches outperform traditional metrics, using both a simulated internal dataset and the Dunnhumby Complete Journey data. The objective is to identify which customer segments are causally influenced by marketing actions and to inform more precise targeting strategies. We implemented logistic regression, T Learner, and Causal Forest models to estimate individual treatment effects. Derived features include behavioral (recency, frequency, engagement), transactional (AOV, loyalty tier), and campaign based variables (channel, timing, offer type). Evaluation metrics include Uplift AUC, Qini Curve, and Precision@10%. Ethical safeguards such as pseudonymization and fairness audits were integrated throughout. Results show that Causal Forest significantly outperforms baseline models, achieving the highest uplift AUC and Precision@10%. Key drivers of uplift include campaign channel, customer recency, and loyalty tier. Segment analyses reveal that marketing effectiveness varies by lifecycle stage, device type, and region. Moreover, integrating uplift insights into real time marketing automation systems enables dynamic optimization of campaigns. In conclusion, uplift modeling offers a more robust framework for understanding and maximizing the causal impact of marketing strategies. It improves resource allocation, enhances personalization, and ensures marketing efforts are both effective and ethically responsible.
Hybrid Deep Learning Models for Intrusion Detection in Cloud Networks: A Benchmark-Based Comparative Study Abdurrohman; Arainy, Corizon Sinar
Digitus : Journal of Computer Science Applications Vol. 2 No. 1 (2024): January 2024
Publisher : Indonesian Scientific Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61978/digitus.v2i1.1116

Abstract

The increasing complexity of cyber threats targeting cloud infrastructures demands advanced and adaptive intrusion detection systems (IDS). This study explores the application of deep learning (DL) models—Convolutional Neural Networks (CNN), Long Short-Term Memory networks (LSTM), and a hybrid CNN+BiLSTM architecture—for detecting network intrusions using benchmark datasets CIC-IDS2017 and UNSW-NB15. This study contributes by demonstrating how hybrid CNN+BiLSTM architectures enhance intrusion detection accuracy on benchmark datasets, offering low latency and improved recall for rare attack classes, thereby validating their suitability for real-time cloud security deployment. Results show that hybrid CNN+BiLSTM models outperform standalone CNN and LSTM architectures in detection performance, achieving accuracies up to 97.4% on CIC-IDS2017 and 96.85% on UNSW-NB15, while maintaining acceptable latency for real-time deployment. The hybrid model also demonstrates superior F1-scores for rare attack classes and lower false positive rates. The discussion highlights the importance of dataset quality, feature engineering, and the role of adversarial training and model optimization in enhancing robustness. In conclusion, this work affirms the value of hybrid DL architectures for cloud-based IDS and suggests future directions in federated learning, adaptive retraining, and deployment in edge environments.
Co-Designing Inclusive Interfaces: Participatory Approaches to Accessible E-Learning for Learners with Disabilities Dewi, Ratna Kusuma; Sitorus, Anwar T
Digitus : Journal of Computer Science Applications Vol. 3 No. 1 (2025): January 2025
Publisher : Indonesian Scientific Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61978/digitus.v3i1.1117

Abstract

Inclusive e-learning environments are essential for equitable access to education, especially for the over one billion people worldwide living with disabilities. However, many e-learning platforms fail to meet accessibility standards due to top-down, non-participatory design approaches. This study aims to evaluate how iterative participatory design methods, including low- and high-fidelity prototyping, impact the accessibility and usability of specific UI elements such as navigation, readability, and input modalities for learners with visual, motor, and cognitive disabilities. The research utilized an iterative participatory design framework involving 15 participants with diverse disabilities (visual, motor, cognitive). Through three stages—needs identification, low-fidelity prototyping, and high-fidelity UI development—users co-designed and evaluated inclusive UI features. Usability was measured through System Usability Scale (SUS) scores, task success rates, completion times, and qualitative interviews. Quantitative results showed a 37% increase in task success rate, a 45% reduction in error count, and an increase in SUS score from 61 to 84. Preferred features included keyboard navigation (93%), font size adjustment (87%), and high contrast modes (82%). Qualitative feedback highlighted the importance of layout consistency, minimal visual clutter, and labeled icons. The study found that participatory design yielded more functional and satisfying UIs than conventional methods and aligned well with accessibility standards like WCAG, UDL, and COGA, while also revealing their practical limitations. Participatory UI design significantly enhances the accessibility and usability of e-learning platforms. Involving users with disabilities as co-creators ensures better alignment with real needs and reinforces the ethical imperative of inclusive education. The findings support institutional adoption of participatory methods to create more equitable digital learning environments

Page 5 of 5 | Total Record : 45

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