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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. 3 (2024): July 2024" : 5 Documents clear
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.
Website Landing Page Design For Chandi's Harvestime For Marketing Development And Promotion Of Culinary Products Purwandari, Nuraini; Firmansyah, Boy; Kristantini, Rr. Aryanti; Jonathan, Michael
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.v2i3.298

Abstract

Chandi's Harvestime is a moving Micro , Small and Medium Enterprise in field culinary. The culinary in question is food and drink. Products made​ including kefir milk, mayonnaise risol , fruit salad , various juices, bakery, cakes dry and still lots again. However , Chandi's Harvestime Not yet has social media and a website promote the product. Promotion and sales in a way direct and only through whatsapp. This purpose of research is designing and creating Websites at Chandi's Harvestime for development marketing and promotion product culinary . The landing page will displays products flagship Chandi's Harvestime , as well information about services and contacts required by the candidate consumer . Method used is design thinking which consists of from empathy , definition , ideation , prototype , and testing . The result of research is in the form of a website- based landing page prototype for promotion product Chandi's Harvestime culinary and already done black box testing results conclusion in accordance with need user .
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.
Sentiment as Signal: Detecting Political Misinformation in Indonesia’s 2024 Election via Lexicon Based NLP Dewi, Ratna Kusuma; 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.v2i3.951

Abstract

The 2024 Indonesian presidential election witnessed heightened political discourse on social media, accompanied by an alarming rise in misinformation. This study explores the use of lexicon augmented sentiment analysis as a method to detect hoax content in electoral conversations across Twitter, TikTok, and Meta platforms. By combining sentiment polarity analysis with weak supervision and partial manual validation, we developed a hybrid model tailored to Bahasa Indonesia. Using around 50,000 social media posts combined with a verified hoax index from MAFINDO, we examined how sentiment changed over time within political hashtags. We found that sentiment sharply declined after major events like debates and result announcements. Importantly, posts with very negative tone were 3–9 times more likely to contain misinformation, with 18% directly matching confirmed hoaxes. The hybrid model improved classification accuracy from 64% to 78%, showing its practical potential. The results confirm that sentiment polarity particularly extreme negativity can serve as a leading indicator for misinformation outbreaks. By aligning lexicon based sentiment scores with external verification sources, this framework enables scalable and semi automated detection of political hoaxes in low resource language settings. Ethical considerations in data handling, platform compliance, and demographic inclusivity are emphasized throughout the methodology. This research contributes to computational political analysis by validating a practical, replicable model for electoral misinformation detection. Future work should extend toward multimodal detection, real time dashboards, and participatory collaborations with fact checkers and regulatory bodies.

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