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Cybersecurity and Innovative Technology Journal
Published by Gemilang Maju Publikasi Ilmiah
ISSN : -     EISSN : 30256682     DOI : http://dx.doi.org/10.53889/citj.v2i1.338
Core Subject : Science,
Computer Science & IT
Cybersecurity and Innovative Technology Journal is a peer-reviewed journal that covers research publications and review articles in the field of cybersecurity and innovative technology. Cybersecurity and Innovative Technology Journal is published by Gemilang Maju Publikasi Ilmiah (GMPI). Cybersecurity and Innovative Technology Journal has become a Crossref member since year 2023 with prefix 10.53889. Therefore all articles published by Cybersecurity and Innovative Technology Journal will have uniques DOI numbers since Vol.1, No.1, September 2023. Since Vol.1, No.1, September 2023. Cybersecurity and Innovative Technology Journal uses Anti-Plagiarism Software "Turnitin" to check the authenticity article.
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 5 Documents
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Search results for , issue "vol 4, no 2 (2026)" : 5 Documents clear
Machine Learning-Based Land Suitability Classification Using NPK and Environmental Factors Fathiah Alatas; Sujiyo Miranto; Brian Abdurafi Rambu Basae; Khansa Tatsbita; Elvan Yuniarti
Cybersecurity and Innovative Technology Journal Vol 4, No 2 (2026)
Publisher : Gemilang Maju Publikasi Ilmiah (GMPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53889/citj.v4i2.945

Abstract

An accurate estimation of soil fertility is needed for the purposes of nutrient management and the practice of precision agriculture. The objective of this research is to build and test a machine learning model for the classification of soil fertility status using seven parameters which are nitrogen (N), phosphorus (P), potassium (K), pH, EC, soil temperature and soil moisture content. Data were collected via measuring using an Internet of Things (IoT) based NPK Meter system. After the pre-processing of data, 91 observations were collected with 7 predictors, and they were grouped into three classes of soil fertility status, which are infertile, moderately fertile, and fertile. The three machine learning algorithms tested were the Artificial Neural Network (ANN), Gradient Boosting Machine (GBM) and CatBoost. The data was divided into 90% training data and 10% test data, whereas the performance of the model was evaluated using accuracy, precision, recall, F1 score, confusion matrix, and Area Under the ROC Curve (AUC). The test results showed that the ANN achieved an accuracy of 100 per cent, whilst the GBM and CatBoost each achieved 90 per cent using the data split employed. Feature contribution analysis indicates that the NPK parameters, moisture content and electrical conductivity provide predictive information for distinguishing soil fertility classes, although the magnitude of their contribution varies across the different algorithms. These results demonstrate the potential of combining IoT-based soil sensors and machine learning to support the classification of soil conditions.
The Effect of e-Bioinformatics Module (e-BIMO) in Forensic Science Theme on High School Students’ Conceptual Understanding in Genetics Concept Anas Lutfi; Indah Juwita Sari; Desi Eka Nur Fitriana; Dewi Murni
Cybersecurity and Innovative Technology Journal Vol 4, No 2 (2026)
Publisher : Gemilang Maju Publikasi Ilmiah (GMPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53889/citj.v4i2.872

Abstract

The study tried to find out the effect of e-Bioinformatics Module (e-BIMO) by using SMS tools for forensic science theme towards high school students' conceptual understanding of genetics. Quasi-experimental method was applied with pretest-posttest control group design in this study. The class XII MIPA A was used as experimental class, while XII MIPA B was control class at one of the senior high schools in Tangerang, Indonesia. Technique in collecting data were tests which were carried out to measure students' conceptual understanding. The result of the research showed that the average score of conceptual understanding in control class was 65.2 which belonged to sufficient category, meanwhile, in experimental class was 77.9 which belonged to good category. Analysis of hypothesis test was done through Mann Whitney Test for conceptual understanding and obtained p-value of 0.027 (0.05). It could be concluded that the use of e-BIMO with SMS tools for forensic science theme had significant effect on high school students' conceptual understanding in genetics.
Development of a DEMATEL-ANP Model for Analyzing Determinants of Artificial Intelligence Adoption Intention among SMEs Dutho Suh Utomo; Ridzky Zul Asdi; La Ode Ahmad Safar Tosungku
Cybersecurity and Innovative Technology Journal Vol 4, No 2 (2026)
Publisher : Gemilang Maju Publikasi Ilmiah (GMPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53889/citj.v4i2.958

Abstract

Artificial Intelligence (AI) adoption provides significant opportunities for Small and Medium Enterprises (SMEs) to improve operational efficiency, decision-making, and competitiveness. However, SMEs still face various challenges in adopting AI technologies, including limited financial resources, digital capability, technological readiness, and organizational preparedness. This study aims to develop a DEMATEL-ANP model to identify causal relationships and prioritize determinants influencing AI adoption intention among SMEs. A quantitative approach using Multi-Criteria Decision-Making (MCDM) was applied by integrating the Decision-Making Trial and Evaluation Laboratory (DEMATEL) and Analytic Network Process (ANP) methods. The proposed model incorporates technological, organizational, and environmental factors based on the Technology Acceptance Model (TAM) and Technology-Organization-Environment (TOE) framework. Data were collected through expert judgment from five experts with knowledge and experience in artificial intelligence, digital transformation, SME development, and technology adoption. The DEMATEL results indicate that all evaluated factors belong to the cause group, with Digital Capability (DC) identified as the most influential factor. Furthermore, ANP results reveal that Cost (0.15) and Digital Capability (0.15) are the highest priority factors, followed by Competitive Pressure (0.14) and Perceived Usefulness (0.13). The consistency evaluation confirms that all ANP comparison matrices meet the consistency requirement with a Consistency Ratio below 0.10. This study contributes by providing a comprehensive decision-making framework for understanding AI adoption determinants among SMEs and identifying key factors that should be prioritized to support successful AI implementation
Modeling Beyond the V: Using Model-Based Systems Engineering (MBSE) as a Platform for Creativity in Systems Engineering Education Christopher Green
Cybersecurity and Innovative Technology Journal Vol 4, No 2 (2026)
Publisher : Gemilang Maju Publikasi Ilmiah (GMPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53889/citj.v4i2.956

Abstract

Traditionally, the educational process of systems engineering focuses on systematic methodologies, which is effective in terms of learning analytical competencies but does not encourage innovation, creativity and development of other necessary skills. As the engineering processes get more complex and require interaction between a variety of disciplines, this limitation becomes critical. This paper suggests a new approach to teaching systems engineering that combines it with the creative aspect and involves integration of systems engineering and STEAM education. Using concepts of systems engineering and design thinking as well as tools like SysML and MBSE, this paper discusses methods of using these methodologies and tools to foster classroom conceptual exploration. These methods involve modeling different architectures, exploring system behavior and incorporating non-functional requirements, including social, ethical and aesthetic ones, into the system model. The suggested instructional methods include stakeholder-centric modeling by means of use case and requirement diagrams, exploring the system functions using activity and block definition diagrams as well as validating the scenarios. Thus, by incorporating the STEAM values into the MBSE, the instructors can teach their students traditional skills of systems engineering, including requirements engineering, interfaces, lifecycle etc. and foster innovation and empathic abilities as well.
Beyond the Platform: Immutable Portability and the Governance of Persistent Personal AI Profiles Kenna Young; Maurice McBride
Cybersecurity and Innovative Technology Journal Vol 4, No 2 (2026)
Publisher : Gemilang Maju Publikasi Ilmiah (GMPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53889/citj.v4i2.955

Abstract

Persistent AI memory is transforming artificial intelligence from a session-bound tool into an increasingly individualized relationship. Existing portability regimes allow users to retrieve certain records, but they do not yet govern the broader, evolving body of context, preferences, inferences, relationships, instructions, and provenance that enables continuity across time. This article defines that governance object as the Persistent Personal AI Profile (PPAIP) and proposes Immutable Portability as a governing principle. Immutable Portability combines two propositions: an enduring, non-revocable right and practical capability to retrieve, retain, and transfer a PPAIP in functionally usable form, and a restriction on post-termination beneficial exploitation by a former provider absent a separate legitimate basis. The article situates this proposal against existing European and U.S. portability and AI-governance frameworks, current platform export practices, and emerging work on portable AI memory and personal AI sovereignty. It further argues that effective governance will require interoperable standards, supervision, revision authority, and accountability, while also confronting lifecycle questions involving dormancy, incapacity, death, succession, destruction, and possible economic transfer. These issues warrant anticipatory attention from cybersecurity leaders, policymakers, standards bodies, legal scholars, and technology providers before proprietary dependence becomes structurally entrenched.

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