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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. 2 No. 1 (2024): January 2024" : 5 Documents clear
Ethical and Technical Frameworks for Deploying Honeypots in Public Wireless Networks Diantoro, Karno
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.v2i1.750

Abstract

Public Wireless Local Area Networks (WLANs) in government and public service institutions are highly vulnerable to cyberattacks, yet conventional firewalls and intrusion detection systems (IDS) often fail to provide proactive defense. This study aims to evaluate the effectiveness of honeypot-based security within the WLAN infrastructure of Dinas Perpustakaan dan Kearsipan Kota Pekanbaru. Using an applied experimental design, honeypots were integrated with Snort IDS and visualized through Honeymap to capture attacker behavior, detect anomalies, and benchmark detection performance. The results show that honeypots reduced detection latency, lowered false positives, and improved accuracy in identifying port scanning and brute force attacks compared to standard firewalls. Additionally, Honeymap enabled geographic analysis of attack origins, enhancing situational awareness. The findings highlight not only the technical benefits but also ethical challenges, particularly regarding user privacy and informed consent. This research recommends that public institutions adopt clear governance frameworks, ensure regular staff training, and maintain continuous system updates to sustain honeypot effectiveness. Strategically deployed, honeypots can strengthen cybersecurity readiness and inform policy development in public network environments.
Real-Time Threat Detection and Forensic Readiness in Wireless LANs: A Case Study Using Snort and HoneyPy Samroh
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.v2i1.751

Abstract

Wireless Local Area Networks (WLANs), especially in public sector infrastructures, face escalating security challenges due to their open architecture and exposure to various cyber threats. This study aims to evaluate the effectiveness of integrating Snort, an intrusion detection system (IDS), with HoneyPy, a low-interaction honeypot, to enhance real-time monitoring and forensic capabilities in WLAN environments. The methodology involved deploying Snort and HoneyPy within a simulated public network setup, using Ubuntu Server as the operating platform. Network attacks were emulated using tools such as Nmap, Hydra, and Metasploit to simulate various threat scenarios. Key metrics such as detection rate, false positive rate, and system responsiveness were used to evaluate performance. Visualization and log analysis tools including Kibana and Snorby were also incorporated to interpret intrusion data effectively. Results demonstrated that Snort successfully identified common scanning techniques and DDoS patterns using rule-based detection. HoneyPy effectively captured brute-force attack behaviors and provided rich interaction logs. The integrated setup facilitated enhanced incident correlation and provided valuable insights for forensic investigation. Visualization dashboards improved threat analysis and supported adaptive response strategies. In conclusion, the combined use of Snort and HoneyPy offers a scalable and cost-effective solution for public WLAN security. It enhances detection accuracy, supports forensic readiness, and provides actionable intelligence on attack behaviors. The findings highlight the practical relevance of layered defense models, offering concrete guidance for public institutions in strengthening WLAN security and forensic readiness.
Infrastructure Driven DevOps in Regulated Markets: A Case Study of Indonesia’s Financial Sector Sucipto, Purwo Agus
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.v2i1.949

Abstract

In regulated industries such as finance and healthcare, organizations must navigate the competing demands of digital innovation and strict compliance requirements. This study investigates how infrastructure localization enables the adoption of DevOps practices in Indonesia’s compliance heavy sectors. Drawing on qualitative case studies of BCA and Bank Jago, the research examines how local cloud infrastructure, regulatory policies, and platform strategies converge to support agile software delivery. The methodology involves comparative analysis using publicly available institutional documents, cloud provider rollouts, and compliance frameworks. The study evaluates DevOps maturity through organizational strategies, toolchains, and infrastructure readiness while mapping them against regulatory standards such as ISO/IEC 27001 and the Personal Data Protection Act. The findings indicate that local cloud infrastructure helps reduce latency and legal risks, thereby supporting secure CI/CD pipelines. BCA illustrates the benefits of using enterprise-level platform engineering with OpenShift, while Bank Jago showcases the flexibility of cloud-native DevOps through rapid CI/CD deployment. Furthermore, the study discusses the balance between innovation and compliance, stressing the role of platform engineering, multi-cloud strategies, and Compliance as Code in minimizing vendor lock-in and regulatory risks. The conclusion underscores Indonesia’s hybrid DevOps strategy as a blueprint for other emerging markets. Integrating infrastructure, policy, and talent development enables institutions to balance agility with governance, promoting scalable and compliant digital transformation in regulated sectors.
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.

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