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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 46 Documents
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Analysis of Broiler Chicken Production Success Classification Using K-Nearest Neighbors And Naive Bayes Methods at PT. Jandela Jaga Kaloka (Jajaka) Tukiyat; Anggai, Sajarwo; Agnia Bilqisti
Digitus : Journal of Computer Science Applications Vol. 2 No. 4 (2024): October 2024
Publisher : Indonesian Scientific Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61978/digitus.v2i4.396

Abstract

The livestock subsector, particularly broiler chickens, provides animal protein sources in Indonesia. However, low production efficiency, managerial challenges, and productivity fluctuations remain the primary obstacles to achieving sustainability in this sector. This study aims to analyze the success rate of broiler chicken production at PT. Jandela Jaga Kaloka (JAJAKA) using a data mining classification approach with the K-Nearest Neighbors (K-NN) and Naive Bayes algorithms. The research population comprises broiler production data from various branches of PT. JAJAKA, with a sample of 200 datasets selected based on representative criteria. The study employs the hold-out method with data splits of 60:40 and 70:30 for training and testing the models. The success rate of production is classified into three categories: good, less good, and excellent. The findings reveal that the K-NN algorithm outperforms with an accuracy of 92.59%, compared to Naive Bayes, which achieves 76.67%. Regarding recall, K-NN records a value of 96.67%, higher than Naive Bayes at 71.67%. However, Naive Bayes shows slightly better precision (94.29%) than K-NN (93.55%). These results affirm that the K-NN algorithm is more effective for classifying the success rate of broiler chicken production, supporting PT. JAJAKA in making more precise and strategic managerial decisions. Furthermore, this study contributes significantly to developing data mining methods in the poultry farming sector to improve efficiency and productivity sustainably. It provides valuable insights for PT. Jandela Jaga Kaloka will evaluate the success rate of broiler chicken production, facilitating more accurate managerial decision-making.
Internet of Things-Based Bus and Student Monitoring System on Free School Transportation Madiun City Rosyadi, Arfian Dwiki; Puspaningrum, Lintang Diah; Hakimi, Musawer
Digitus : Journal of Computer Science Applications Vol. 2 No. 2 (2024): April 2024
Publisher : Indonesian Scientific Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61978/digitus.v2i2.464

Abstract

Traffic incidents and violations, particularly among students, pose safety concerns for parents and authorities. To address this issue, the Madiun City Government introduced the Free School Transport program through the Madiun City Transportation Office, providing school buses for students in Madiun City. However, despite this initiative, students and parents face challenges tracking bus arrivals due to weather conditions, traffic congestion, and schedule delays. This study proposes developing MASBUS, an Internet of Things (IoT)-based monitoring system for real-time school bus tracking. The system integrates a GPS module for location tracking and an RFID module for student identification. Data is transmitted via NodeMCU ESP8266 to a web server, stored in a MySQL database, and accessed through an Android application. The MASBUS app provides real-time location updates and sends automatic notifications when students board or leave the bus. The implementation of this system enhances student safety, improves transportation efficiency, and enables real-time monitoring for parents and school authorities. MASBUS contributes to a smarter and safer school transportation system by offering a more transparent and reliable tracking solution.
Increasing Website Traffic with the On-Page Search Engine Optimization Approach: A Case Study of LaskarKoinSeribu.org Jihadi, Hilman; Dade Maulana; Aryanti Kristantini
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.v3i1.506

Abstract

This study aims to increase traffic to the LaskarKoinSeribu.org website, an Islamic-based donation platform, through the implementation of On-Page SEO strategies. A comprehensive analysis identified several key issues, including low organic traffic, suboptimal keyword usage, less SEO-friendly URL structures, inadequate content optimization, and weak internal linking. To address these challenges, this study utilizes various SEO tools such as Google Keyword Planner, Semrush, and Meta Tags Analyzer to design strategies based on targeted keywords, including "Islamic donation," "charity," and "Bogor donation." The implementation of On-Page SEO techniques leads to a significant increase in website traffic, with rankings improving to the top position on Google search results compared to prior SEO efforts. Key factors contributing to this success include technical optimization, content improvement, and long-tail keyword integration, all of which enhance website visibility, user engagement, and donation conversions. The results highlight the effectiveness of On-Page SEO in strengthening the platform’s online presence and supporting its social and religious mission by making donation opportunities more accessible to the target audience. This study demonstrates that proper SEO strategies can serve as a powerful tool for non-profit platforms in increasing digital reach and maximizing social impact.
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.
Toward Data-Driven Health Transformation: Accessibility, Interpretability, and Institutional Readiness for AI Soderi, Ahmad
Digitus : Journal of Computer Science Applications Vol. 2 No. 2 (2024): April 2024
Publisher : Indonesian Scientific Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61978/digitus.v2i2.833

Abstract

Artificial intelligence (AI) and big data analytics are increasingly recognized as vital tools in transforming healthcare delivery, particularly within hospital settings. This narrative review aims to explore the challenges and opportunities associated with the implementation of these technologies in urban healthcare systems. Using literature obtained from Scopus, PubMed, and Google Scholar, the review employs keywords such as "AI in healthcare," "big data analytics," and "predictive analytics in medicine" to synthesize peer-reviewed studies that examine both theoretical and practical dimensions of AI adoption. The analysis reveals that while developed countries are more equipped with infrastructure and training, developing nations often face systemic challenges such as limited funding, inadequate technology, and insufficient regulatory support. Accessibility remains a key concern, with disparities in technological adoption driven by geographic, demographic, and institutional factors. Furthermore, the review identifies gaps in the interpretability and integration of AI tools, especially in infection management and clinical decision-making. The discussion emphasizes the need for adaptive policy interventions, targeted investments in healthcare training, and the development of transparent AI systems. The study also recommends enhancing cross-sector collaboration to build scalable and inclusive health innovations. In conclusion, addressing the structural, ethical, and educational dimensions of AI deployment is essential for realizing its full potential in global healthcare improvement.
Toward Equitable Digital Mental Health: Integrating AI and Telepsychiatry in Global Practice Algristian, Hafid; Sitorus, Anwar T
Digitus : Journal of Computer Science Applications Vol. 2 No. 2 (2024): April 2024
Publisher : Indonesian Scientific Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61978/digitus.v2i2.834

Abstract

In response to the growing mental health crisis and the expansion of digital healthcare, this narrative review explores the application of telepsychiatry and artificial intelligence (AI) in mental health services. The study aims to synthesize recent developments, challenges, and future directions in digital mental health innovation. A systematic literature search was conducted across PubMed, Scopus, and Web of Science databases, focusing on studies published between 2016 and 2023. Keywords such as "telepsychiatry," "mental health," "artificial intelligence," and "technology adoption" were used to identify relevant empirical and theoretical works. Inclusion criteria emphasized real-world applications and stakeholder perspectives. The results reveal substantial variability in the understanding and implementation of telepsychiatry across different regions and populations. Socioeconomic factors, digital literacy, and cultural perceptions significantly influence the acceptance and success of digital interventions. While AI-driven tools improve diagnostic efficiency and reduce treatment delays, systemic barriers such as regulatory limitations, institutional resistance, and data privacy concerns impede widespread adoption. Comparative analysis highlights a more favorable reception in high-income countries, though underserved populations in both developed and developing nations continue to face accessibility challenges. These findings underscore the urgent need for inclusive policies, capacity-building initiatives, and ethical AI governance frameworks. Addressing these factors can bridge existing gaps and ensure more equitable mental healthcare. The study concludes by emphasizing the importance of sustained interdisciplinary research to refine telepsychiatric models and promote socially responsible technology integration.
Securing the Cloud: Privacy, Policy, and AI-Driven Cybersecurity Solutions Rinaldo
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.v2i3.835

Abstract

As cloud computing becomes the backbone of modern digital infrastructure, cybersecurity has emerged as a critical concern across public and private sectors. This narrative review investigates the multifaceted threats, defense strategies, and policy implications associated with cybersecurity in the cloud environment. Literature was systematically sourced from Scopus and Google Scholar using keywords such as "cybersecurity", "cloud security", "AI for cybersecurity", and "data privacy", with inclusion criteria focusing on recent, peer-reviewed studies. The review revealed that data security threats—particularly DDoS attacks, ransomware, and data leakage—are on the rise, with over 40% of organizations reporting incidents in the past two years. Privacy protection varies globally, depending on both technological implementations and regulatory frameworks like the GDPR. Strategies such as encryption, AI-based anomaly detection, and Zero Trust architecture are proving vital in threat mitigation. Yet, systemic challenges—such as policy inconsistency, digital skill gaps, and uneven infrastructure—hinder progress, particularly in developing regions. The discussion emphasized that successful implementations often involve coordinated governance, robust public-private partnerships, and inclusive education strategies. This study concludes by calling for targeted policy reform, investment in digital capacity building, and deeper research into scalable cybersecurity models for vulnerable contexts. These findings underscore the urgency of constructing adaptive and inclusive cybersecurity frameworks to support safe and resilient digital transformation.
Empowering Decision-Making through Big Data Analytics: A Narrative Review of Techniques, Tools, and Industrial Applications Nugroho, Aryo
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.v2i3.836

Abstract

Big Data Analytics (BDA) has become a pivotal enabler of data-driven decision-making across various industrial sectors. This narrative review aims to synthesize existing literature on BDA techniques, tools, and applications to identify their role and impact in decision support systems. The review draws upon scholarly databases such as Scopus, IEEE Xplore, and Google Scholar, utilizing a systematic search strategy with Boolean keyword combinations to retrieve relevant literature. Studies were screened based on inclusion and exclusion criteria, focusing on empirical findings and practical applications of BDA across domains. Findings reveal that techniques such as data mining, predictive analytics, and machine learning offer enhanced accuracy and real-time capabilities, leading to better outcomes in healthcare diagnostics, manufacturing efficiency, and logistics optimization. The utilization of platforms like Hadoop, Spark, and Tableau demonstrates both functional versatility and implementation challenges, influenced by cost, infrastructure, and human capital readiness. Furthermore, the success of BDA initiatives is closely linked to organizational factors including data quality and workforce expertise. Systemic barriers such as strict data policies, fragmented IT infrastructures, and limited data access in low-resource settings impede optimal BDA deployment. This review underscores the need for strategic policy reforms, technological investments, and capacity building to realize the full potential of BDA. By addressing existing limitations and supporting future research directions, organizations can harness BDA to enable informed, agile, and sustainable decision-making.
Navigating Interoperability, Security, and Scalability: A Narrative Review of IoT Architectures in Smart Cities Nasihien, Ronny Durrotun; Juwari
Digitus : Journal of Computer Science Applications Vol. 2 No. 4 (2024): October 2024
Publisher : Indonesian Scientific Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61978/digitus.v2i4.837

Abstract

The rapid advancement of Internet of Things (IoT) technologies has driven the transformation of urban centers into smart cities, yet significant challenges remain in the implementation of interoperable, secure, and scalable IoT architectures. This narrative review aims to explore the primary issues and proposed solutions related to interoperability, cybersecurity, and infrastructure scalability in IoT-based smart city frameworks. Literature was gathered from leading databases including Scopus, Web of Science, and IEEE Xplore, using Boolean search strategies with keywords such as "IoT," "smart cities," "interoperability," "security," and "edge computing." Inclusion criteria focused on peer-reviewed empirical studies published within the past decade. Studies were categorized thematically to identify trends and gaps. The findings show that a lack of interoperability standards remains a major bottleneck, while the growing volume of connected devices amplifies security and scalability concerns. Technical approaches such as Software Defined Networking (SDN), blockchain-based data protection, and edge computing have demonstrated potential in addressing these challenges. However, systemic constraints, including fragmented policies and insufficient data governance, continue to hinder full-scale adoption. The review highlights the importance of adopting a multidimensional framework that incorporates both technological innovations and adaptive policy-making to ensure the successful deployment of IoT in smart city contexts. This study calls for greater cross-sector collaboration, policy reform, and future research into AI-enhanced IoT systems to support inclusive and resilient smart city development.

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