Publication Search

This study investigates the role of artificial intelligence (AI) in enhancing language learning, with a focus on five key applications: automatic text analysis, personalized learning, adaptive feedback, language error detection, and automatic translation. The study addresses the challenge of integrating AI effectively in educational contexts while balancing technological potential with pedagogical guidance. The objective is to provide a comprehensive understanding of how AI tools contribute to more adaptive, efficient, and engaging language learning experiences. A systematic literature review method was employed, selecting and critically analyzing studies published between 2020 and 2025 that examined AI-assisted language learning strategies. The findings indicate that automatic text analysis supports comprehension monitoring and guided learning, while personalized learning adapts content to individual learner needs, enhancing motivation and retention. Adaptive feedback delivers immediate, targeted guidance that fosters accuracy and self-regulated learning, and language error detection tools enable learners to identify and correct grammatical and lexical mistakes, promoting metalinguistic awareness. Automatic translation broadens access to authentic texts and cross-cultural materials, supporting comprehension and independent learning. Synthesizing these findings highlights the transformative potential of AI to improve learning outcomes while also revealing challenges such as tool reliability, ethical considerations, and the need for teacher oversight. The study concludes that AI, when thoughtfully integrated, complements instruction, enhances learner engagement, and supports differentiated and data-driven teaching strategies, providing valuable insights for language educators and guiding future research on AI-enabled language learning.

This study examines the application of Transparent Artificial Intelligence (AI) for fraud detection in public welfare programs using publicly available administrative data. Persistent challenges in welfare governance such as misallocation, fraud, and data inaccuracy necessitate analytical frameworks that are both effective and explainable. The research aims to design and evaluate an interpretable anomaly detection system capable of identifying irregularities in welfare distribution while maintaining transparency and accountability. Methodologically, the study employs two unsupervised models Isolation Forest and Local Outlier Factor (LOF) to detect anomalies in sub-district-level welfare data, incorporating features such as population size, number of beneficiaries, and coverage ratio. An Explainable AI (XAI) framework integrating surrogate Random Forests, Permutation Feature Importance (PFI), and local linear surrogates (LIME-like) is applied to ensure interpretability of both global and local model behaviors. Findings reveal that receivers per 1000 population and percentage coverage are dominant determinants of anomaly scores. Fifteen administrative units were flagged for potential inconsistencies suggesting over- or under-reporting of beneficiaries. Cross-validation between IF and LOF models confirmed consistency in identifying anomalous regions. The integrated XAI explanations enhance transparency, enabling policymakers and auditors to trace the rationale behind detected anomalies. In conclusion, the proposed Transparent AI framework demonstrates that combining anomaly detection with interpretability tools can strengthen accountability and fairness in welfare administration. It offers a reproducible, ethical, and data-driven approach to social program monitoring, reinforcing public trust and supporting responsible AI governance.

Explainable artificial intelligence (XAI) has become a critical requirement in cybersecurity due to the high-stakes nature of security decision-making and the limitations of black-box learning models. This study investigates the construction of an explainable cybersecurity knowledge representation by leveraging standardized terminology from the NIST cybersecurity glossary. The primary problem addressed is the lack of transparent and semantically grounded reasoning mechanisms in existing AI-driven cybersecurity systems, which limits trust, accountability, and analyst adoption. To address this challenge, we propose a NIST-based semantic knowledge graph that embeds explainability directly into its ontology structure and reasoning process. The proposed framework systematically extracts definitional entities and relations from NIST glossary entries to construct a domain ontology and a multi-relational knowledge graph. A rule-based semantic relation extraction method is employed to ensure faithful, interpretable, and reproducible reasoning paths. The resulting knowledge graph contains over 3,000 cybersecurity concepts and approximately 27,000 semantic relations, covering hierarchical, associative, dependency, and mitigation semantics. Experimental evaluation demonstrates that the proposed approach achieves a high level of explainability, with 92.4% of reasoning outcomes being fully traceable and only 1.4% classified as non-traceable. Most explainable reasoning paths are limited to two or three hops, indicating an effective balance between inferential depth and human interpretability. Structural analysis further confirms the presence of meaningful hub concepts that support multi-hop semantic inference. These results confirm that ontology-driven, standard-based knowledge graphs provide a robust foundation for explainable cybersecurity intelligence. The study concludes that explainability-by-design, grounded in authoritative standards, offers a viable and trustworthy alternative to opaque AI models for cybersecurity applications.

This study investigates the role of Artificial Intelligence (AI) tools, specifically ChatGPT and Jenni AI, in supporting academic integrity through accurate citation generation. Employing a descriptive qualitative method, the research involved 30 undergraduate students who evaluated AI-generated citations using Fishman’s (2014) Academic Integrity Theory and Smith’s (2020) Citation Accuracy Framework. Data were collected through questionnaires assessing students’ perceptions of reliability, ethical responsibility, and accuracy in AI-assisted citation practices. Quantitative analysis revealed that Honesty and Accountability were the most dominant values (22.58% each), followed by Fairness and Respect (19.35% each), Trust (12.90%), and Courage (3.23%). Qualitative findings showed that students recognized AI’s potential to enhance writing efficiency but emphasized the need for human verification to ensure factual correctness and ethical compliance. Comparatively, Jenni AI demonstrated greater consistency and citation verification than ChatGPT, which exhibited more frequent fabrication and inaccuracy. The study concludes that while AI tools can enhance academic productivity, maintaining academic integrity still requires critical human oversight, ethical awareness, and adherence to scholarly honesty and accountability.

This study aims to investigate the skill gaps experienced by bachelor’s degree graduates in Indonesia when facing the dual demands of Industry 4.0 and Society 5.0. The method applied in this research is a systematic literature review, synthesizing findings from 21 scientific articles focusing on the dynamics of skill gaps and the effectiveness of higher education interventions in Indonesia. The study findings reveal two main categories of competency gaps. In the context of Industry 4.0, the most significant deficiencies are observed in specific hard skills, particularly data literacy, artificial intelligence proficiency, and automation across various sectors. Meanwhile, within the Society 5.0 dimension, deeper gaps emerge in soft skills and human-centered competencies, including complex problem-solving, emotional intelligence, creativity, and environmentally sustainable skills. These human-centered skill gaps play a critical role in enhancing graduate value as AI technologies increasingly replace routine tasks. Although the Merdeka Belajar Kampus Merdeka program shows positive outcomes in improving sustainability skills and digital certification, it has not fully succeeded in driving fundamental transformation of higher education learning outcomes, limiting alignment with sustainability principles and the Society 5.0 approach. It can be concluded that Indonesian higher education faces the challenge of undertaking fundamental curriculum reform to integrate human-centered competencies as a foundation for preparing future human resources.

Smart cities are trending as an innovative approach to address urban problems. This study aims to analyse the trend of research publications on smart cities in Indonesia with a bibliometric analysis approach. The articles used in this study were obtained from Scopus data. From 131 articles found in the scopus database between 2013 and 2024. The selected articles were then managed using biblioshiny and Vosviewer software. The results showed that publications related to smart cities experienced fluctuations from the last 10 years. The article with the most citations is entitled Strengthening waste recycling industry in Malang (Indonesia): Lessons from waste management in the era of Industry 4.0 has the most citations of 85 citations. While seen from the highest affiliation shows that Gadjah Mada University is an institution with a total of 70 publications. Mapping articles based on the relationship between keywords (co-occurance) is formed into 12 clusters, each cluster describes topics that are often discussed in smart city-related literature, such as urban planning, social networking, e-government, public services, urban development, sustainable development, internet of things (IoT), urban growth, economic, artificial intelligence, and secondary datum.

This study aims to provide a comprehensive analysis of Predictive Policing and its implications for law enforcement transformation in Indonesia, based on an extensive review of its global applications, benefits, and challenges. The study uses qualitative literature and international case study review methods to assess the impact and complexity of implementing digital technologies such as artificial intelligence (AI), machine learning, and big data analytics within a Predictive Policing framework. The results of this review highlight that while Predictive Policing offers significant potential for proactive crime prevention and increased operational efficiency, its implementation is consistently fraught with critical legal, ethical, and technical challenges, including regulatory gaps, risks of algorithmic bias, and data privacy concerns, which are particularly relevant to Indonesia. The findings underscore that public trust and police legitimacy in the context of adopting such technologies are strongly influenced by transparency, strong accountability mechanisms, and community involvement in shaping their use. This study contributes to the growing discourse on digital policing in developing countries and culminates in practical policy recommendations designed to guide the Indonesian police towards the development and implementation of Predictive Policing models that are effective, efficient, and fundamentally respectful of legal and human rights principles.

The integration of Artificial Intelligence (AI) into precision agriculture has significantly improved plant disease recognition; however, many existing deep learning models remain computationally expensive and feature-redundant, limiting their deployment on low-power and edge devices. To address these limitations, this study proposes a lightweight framework for maize leaf disease recognition based on serial deep feature extraction, dimensionality reduction, and machine-learning–based classification. A pre-trained MobileNetV2 network is employed as a fixed feature extractor to obtain discriminative visual representations, while Principal Component Analysis (PCA) is applied to reduce feature dimensionality by approximately 76%, retaining 95% of the original variance and improving computational efficiency. The compressed features are subsequently classified using a Radial Basis Function Support Vector Machine (RBF-SVM), optimized via grid search and cross-validation. Experiments conducted on a four-class maize leaf disease dataset (Northern Leaf Blight, Common Rust, Gray Leaf Spot, and Healthy), with class imbalance handled during training, demonstrate that the proposed MobileNetV2–PCA–SVM pipeline achieves 97.58% accuracy, 96.60% precision, 96.59% recall, and 96.59% F1-score, outperforming the DenseNet201 + Bayesian-optimized SVM baseline (94.60%, 94.40%, 94.40%, and 94.40%, respectively). This improvement corresponds to a 2.98% accuracy gain, a 55% reduction in error rate, an 86% reduction in model parameters (20.31M to 2.75M), and an 85% reduction in model size (81 MB to 12 MB). These results indicate that the proposed framework provides a compact and efficient solution with strong potential for deployment in resource-constrained agricultural environments.

Prompt injection has emerged as a critical security threat for Large Language Models (LLMs), exploiting their inability to separate instructions from data within application contexts reliably. This paper provides a structured review of current attack vectors, including direct and indirect prompt injection, and highlights the limitations of existing defenses, with particular attention to the fragility of Known-Answer Detection (KAD) against adaptive attacks such as DataFlip. To address these gaps, we propose a novel, hybrid, multi-layered detection framework that operates in real-time. The architecture integrates heuristic pre-filtering for rapid elimination of obvious threats, semantic analysis using fine-tuned transformer embeddings for detecting obfuscated prompts, and behavioral pattern recognition to capture subtle manipulations that evade earlier layers. Our hybrid model achieved an accuracy of 0.974, precision of 1.000, recall of 0.950, and an F1 score of 0.974, indicating strong and balanced detection performance. Unlike prior siloed defenses, the framework proposes coverage across input, semantic, and behavioral dimensions. This layered approach offers a resilient and practical defense, advancing the state of security for LLM-integrated applications.

This study developed and evaluated an AI-integrated Virtual Reality (VR) system designed to enhance personalized learning in higher education. While VR improves engagement, existing systems often lack adaptivity or experience high latency during AI interactions. To address these limitations, this research introduces a novel integration of a cache-optimized Llama 2 Large Language Model (LLM) that delivers real-time, motivationally grounded feedback. The system was implemented using Unity 3D and validated with 50 undergraduate students. Technical validation showed that the cache layer reduced interaction latency from 17.7 ms to 14.2 ms and maintained zero system crashes throughout the pilot. Learner motivation was assessed using Keller’s ARCS model, yielding mean scores ranging from 4.08 to 4.69 across all dimensions. Independent t-tests (p > 0.05) and negligible effect sizes (Cohen’s d < 0.2) revealed no significant difference between technical (ICT) and non-technical (Physics) students. These findings confirm that the proposed system effectively bridges technological and motivational gaps, providing a robust model for adaptive, immersive education.

Artificial Intelligence (AI) is increasingly recognized as a transformative enabler of early disease detection, with the potential to improve diagnostic accuracy, support predictive risk stratification, and advance preventive healthcare. Despite rapid methodological progress, many existing reviews remain performance-centric, offering limited insight into generalizability, ethical governance, and real-world implementation constraints. This paper presents a narrative and integrative review with an adoption-focused, translational perspective, synthesizing recent developments in AI-driven early disease detection across oncology, cardiology, neurology, and infectious disease surveillance. Drawing on peer-reviewed literature published primarily between 2016 and 2025, the review examines reported performance gains alongside persistent limitations related to data heterogeneity, population bias, explainability, and regulatory fragmentation. Through cross-sectional synthesis, we identify three recurring gaps in prior reviews: (i) overgeneralization of AI’s diagnostic superiority, (ii) insufficient consideration of ethical and legal accountability, and (iii) a lack of actionable guidance for scalable clinical implementation. Integrating technical, ethical, and policy dimensions into a unified conceptual framework, this review demonstrates that while AI systems can consistently enhance diagnostic accuracy and early risk stratification in well-defined tasks, sustained clinical adoption depends on aligning technical performance with governance readiness, interpretability, and workflow integration. The analysis further highlights how implementation mechanisms—such as explainable AI, continuous post-deployment monitoring, and clinician-centered deployment strategies—mediate the translation of algorithmic innovation into real-world healthcare impact. Overall, this review provides a critical reference for researchers, clinicians, and policymakers seeking to translate AI innovation into safe, equitable, and trustworthy clinical practice.

The maritime education sector faces complex challenges in preparing competent seafarers amid the rapid advancement of digital technology. This study investigates the effect of artificial intelligence-based simulations and AI-based competency assessments on competency achievement levels among nautical cadets at Indonesian maritime training institutions. The research design employed a convergent parallel mixed-methods approach, integrating quantitative and qualitative methods to gain a comprehensive understanding. Quantitative data were collected from 150 cadets using a validated questionnaire. In comparison, qualitative data were obtained through in-depth semi-structured interviews with fifteen instructors and ten cadets. Multiple regression analysis revealed that the research model significantly predicted cadet competency achievement. The findings indicate that AI-based assessments exert a stronger influence than AI simulations in improving competency. The qualitative exploration highlighted adaptive feedback mechanisms and personalized learning pathways as critical success factors in implementing learning technologies. This study provides empirical evidence for maritime institutions to prioritize strategic investments in AI-based assessment systems while maintaining a human-centered pedagogy. The research contribution lies in integrating fourth industrial revolution technologies into the training, certification, and watchkeeping standards compliance framework for seafarers, thereby strengthening Indonesia's maritime education ecosystem and aligning it with international standards.

This study examines the construction of heroism in the character of Joshua Taylor in The Creator (2023) using Christopher Vogler’s Hero’s Journey framework. In modern cinema, heroism is no longer limited to physical strength or traditional bravery but is often defined through moral struggle, emotional conflict, and ethical decisions in complex situations. Joshua represents a modern hero whose journey unfolds amid a global conflict between humans and artificial intelligence (AI). This research applies a qualitative descriptive method with a structural mapping technique to analyze Joshua’s narrative progression across the stages of Vogler’s Hero’s Journey. The findings show that Joshua’s heroism is constructed through moral transformation, sacrifice, and his shift from a disillusioned soldier to a protector figure who prioritizes life and empathy over ideology. His journey highlights that modern heroism is rooted in ethical awareness and personal redemption rather than dominance or power, it reflects cinematic developments in depicting heroism that are more relevant to today's social and technological context.

The rapid growth of short-form social media platforms has increased the complexity of decision-making during the digital content planning stage. Content creators are required to evaluate the feasibility of content ideas and determine platform suitability prior to production, while most existing tools primarily focus on post-publication analytics. This study aims to design an Artificial Intelligence (AI)-enabled Decision Support System (DSS) to evaluate digital content ideas in the pre-production stage. Adopting a Design Science Research approach, the study develops a conceptual design artifact that integrates intrinsic content idea characteristics with cognitive and affective response modeling grounded in the Stimulus–Organism–Response (S-O-R) framework, alongside platform affordance mapping. The proposed artifact operationalizes a reflective evaluation mechanism that generates platform recommendation scores and idea enhancement suggestions without claiming deterministic or predictive performance modeling. Evaluation was conducted qualitatively through practitioner assessment to examine perceived usefulness, clarity of recommendations, and decision support contribution. The findings indicate that the developed artifact provides a structured reflective framework for early-stage content evaluation. Theoretically, this study extends the application of the S-O-R framework by operationalizing it as a design logic for a pre-production DSS artifact. Practically, the proposed system has the potential to support more systematic decision-making prior to content production.

Open-pit mining activities in Indonesia contribute significantly to the national economy but require stringent monitoring to mitigate environmental degradation. Conventional monitoring methods relying on terrestrial surveys are often constrained by vast coverage areas, high operational costs, and limited field accessibility. This study aims to develop an artificial intelligence model capable of automatically detecting and mapping mining areas to enhance surveillance efficiency. The applied method is Deep Semantic Segmentation utilizing the U-Net Convolutional Neural Network (CNN) architecture. The model was trained using Sentinel-2 satellite imagery, focusing exclusively on Red, Green, and Blue (RGB) spectral channels to replicate human visual perception. Experimental results demonstrate that the proposed model performs reliable segmentation of mining areas, achieving an Accuracy of 93.58% and a Global Intersection over Union (IoU) of 0.8067. These findings indicate that the U-Net architecture can effectively extract spatial features of mines even when utilizing standard visual data. This research contributes to the development of an efficient, cost-effective, and scalable digital monitoring prototype to support innovation in sustainable environmental governance.

This study analyzes the relationship between Christian faith and artificial intelligence (AI) through a theological-philosophical approach and assesses its relevance to the existence of the Catholic Church in the digital age. The background of this study stems from the rapid development of AI, which brings both opportunities and risks to the life of faith, such as the simplification of theological teachings, the reduction of personal relationships, and the emergence of a technocratic paradigm. The study uses qualitative methods with a literature review of the Holy Scriptures, Church documents, and literature on philosophy and technology ethics. The results of the analysis show that AI is a product of human creativity as the image of God, so it does not conflict with faith, but it remains instrumental and does not have moral or spiritual dimensions like humans. Therefore, AI cannot replace the role of humans in faith relationships. The Church is called to guide technological development through Christian ethical principles, upholding human dignity, being critical of the effects of dehumanization, and utilizing AI wisely for evangelization, catechesis, and faith education. With a reflective and critical approach, the Church can remain relevant amid technological advances without losing its identity and mission for the common good.  

The industrial revolution 4.0 has brought rapid advances in technology, one of which is artificial intelligence (AI). AI has the ability to imitate the human thought and action process in solving various problems. However, the implementation of AI raises legal problems related to responsibility for the negative impacts caused, such as cybercrime, information manipulation, privacy violations, and misuse of technology. Indonesia, as a country based on law, is faced with the challenge of regulating AI to be in line with technological developments. Currently, legal regulations in Indonesia do not specifically regulate the legal responsibility of AI. Positive laws, such as the Civil Code and the ITE Law, can be used interpretively, but are not enough to address the complexity of AI. Legal responsibility related to AI is debatable, whether it is imposed on the developer, owner, or user of AI. In addition, AI does not have a “mens rea” in criminal law, so that unlawful acts are more relevant to be imposed on the responsible human. To overcome this problem, legal reform or the creation of special regulations that comprehensively regulate AI are needed. These regulations must include privacy protection, data security, and criminal and civil liability due to the use of AI. With a clear legal framework, the risk of AI misuse can be minimized and its use can be optimized for the welfare of society.

Financial distress is a condition of declining financial health of a company that can develop gradually and lead to business failure if not detected early. With the increasing complexity of the business environment and the limitations of conventional statistical methods, Artificial Intelligence/AI is increasingly being adopted in the development of early warning systems (EWS) to predict financial distress. This study aims to examine the development of AI-based EWS research, identify the most widely used algorithms, and evaluate the effectiveness of AI models compared to conventional methods in predicting financial distress. The method used is a comprehensive systematic literature review of 15 relevant scientific articles. The results show that the paradigm has shifted from statistical models to machine learning and deep learning. Random Forest and Artificial Neural Network are the most widely used algorithms and have better predictive performance. This study offers a conceptual synthesis of the progress, effectiveness, and challenges of applying AI in predicting financial distress and opens opportunities for further research on the development of contextual and interpretative EWS.

The rapid development of Artificial Intelligence (AI) technology presents significant opportunities to improve the quality of learning; however, its utilization in school settings is still constrained by teachers’ limited competencies. This community service program aims to enhance teachers’ competencies in utilizing AI-based learning technologies in a pedagogical, ethical, and practical manner. The implementation method consisted of preparation, implementation, and evaluation stages. The activities were carried out through interactive workshops, demonstrations of AI-based educational tools, hands-on practice with mentoring, and evaluation using pre-tests and post-tests. The results indicate a significant improvement in teachers’ understanding and skills in using AI for lesson planning, instructional media development, and learning assessment. Teachers also demonstrated more positive attitudes and greater confidence in integrating AI into classroom practices. Nevertheless, challenges such as varying levels of digital literacy and limited technological infrastructure were still identified. Overall, this community service activity proved effective in improving teachers’ competencies and has the potential to support more innovative and adaptive learning transformation in the digital era.

The advancement of Artificial Intelligence (AI) presents substantial potential for improving the quality of learning in elementary education, particularly through the application of AI prompts as instructional support in lesson planning and classroom implementation. Nevertheless, many teachers at Integrated Islamic Elementary Schools (Sekolah Dasar Islam Terpadu/SDIT) experience limited AI literacy and insufficient competence in designing pedagogically appropriate and effective AI prompts. This community service program aims to enhance AI literacy and strengthen the professional competence of teachers at SDIT Lukmanul Hakim Puring, Kebumen, in utilizing AI prompts to support instructional design, teaching materials development, and learning assessment. The program employed participatory methods, including socialization sessions, hands-on training, guided practice, and continuous mentoring. The results demonstrate a measurable improvement in teachers’ understanding and practical skills in constructing AI prompts aligned with pedagogical objectives. Furthermore, the integration of AI prompts contributes to increased instructional efficiency and pedagogical creativity. This program is expected to provide a sustainable model for supporting digital transformation in elementary school education.