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This research investigates the impact of influencer marketing, brand awareness, and product quality on consumers' decisions to purchase Wardah lip cream. Employing a quantitative method, data was gathered through an online questionnaire filled out by 100 participants who are actual users of the product. The data was then analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) through the SmartPLS 4 software. Based on the analysis, influencer marketing was found to have a positive and statistically significant effect on purchase decisions. In contrast, brand awareness did not demonstrate a meaningful influence in this context. Among the variables studied, product quality emerged as the most influential and statistically significant factor, indicating that consumers prioritize product performance when deciding to make a purchase. The model's explanatory power is shown by an R-square value of 0.645, meaning that 64.5% of the variation in purchasing decisions can be explained by the three variables combined. Furthermore, the Q² Predict score of 0.620 indicates that the model is highly predictive and reliable in understanding consumer behavior. Overall, product quality plays a crucial role in shaping consumer choices regarding Wardah lip cream, surpassing the effects of brand familiarity or promotional efforts through influencers

This study aims to analyze the influence of soft skills and Industrial Field Experience (IPC) on students' readiness to enter the workforce by utilizing work motivation as a mediating variable. A quantitative approach was used in this study by applying the Structural Equation Modeling Partial Least Square (SEM-PLS) analysis technique. Data were collected through an online survey involving 102 students of the Cosmetology and Beauty Study Program at Padang State University. The results showed that both soft skills and industrial field experience had a significant influence on job readiness, both directly and through the mediation of work motivation. Work motivation was proven to be able to strengthen the relationship between the independent variables and student job readiness. This finding indicates that work motivation plays an important role as a psychological mechanism that connects non-technical skills and practical experience with readiness to enter the professional world. This research model showed strong predictive power, reflected in the R-square value of job readiness of 0.751. This value indicates that 75.1% of the variability in job readiness can be explained by the model involving soft skills, IPC, and work motivation. The implications of these results are very important for higher education institutions. To prepare graduates who are better prepared to face the challenges of the workplace, strengthening soft skills and providing relevant industrial field experience should be a primary focus of the curriculum. Furthermore, strategies to increase student work motivation must be systematically designed to optimize the results of this competency development.

The efficiency of investment decisions is one of the core axes in the success of organizations and the sustainability of their business, especially in light of the dynamic and complex business environment. In this context, the integrated role of both accounting and financial management systems is highlighted, as the harmony between them is a key pillar in providing accurate, real-time, and analytical data that supports the investment decision maker and reduces the degree of uncertainty and risks associated with investments. This research aims to analyze the impact of the integration between accounting systems and financial management on the quality and efficiency of investment decisions within institutions, with a focus on the nature of the causal relationship between the two variables. A conceptual model has been built that illustrates the interaction between the financial information generated by the accounting system and the analytical tools provided by the financial department, which contributes to raising the efficiency of strategic decisions related to investment. To achieve the objectives of the study, a descriptive-analytical approach supported by a standard analysis using a simple linear regression model was adopted on field data extracted from an intentional sample of financial officials in the banking and investment sector. The results showed that there is a statistically significant positive effect of the integration of accounting and financial management systems in enhancing the efficiency of investment decisions, as the model showed that integration contributes more than 50% to the explanation of changes in the quality of investment decisions. The study reached a number of important findings, the most prominent of which is that the lack of integration or poor coordination between accounting and financial management leads to delays in decisions or making them based on incomplete or contradictory information. Effective integration enables organizations to allocate resources more efficiently and evaluate investment alternatives in a thoughtful manner. The study concluded with a set of recommendations, most notably the need to develop the digital infrastructure of accounting and financial systems, adopt a unified system for data exchange, enhance the culture of teamwork between accounting and financial management units, in addition to activating the use of predictive financial analysis techniques to raise the level of accuracy in investment decisions.

Background: Maritime engineering has traditionally relied on reactive and preventive maintenance strategies, often leading to operational inefficiencies, unplanned downtime, and excessive costs. With the rise of smart ship technologies, predictive maintenance (PdM) has emerged as a data-driven solution, leveraging sensor-based monitoring and real-time diagnostics to optimize ship maintenance. However, its integration into maritime education remains underexplored, particularly in training vessels used for vocational learning. Original Value: This research contributes new insights into the feasibility, effectiveness, and educational relevance of predictive maintenance in maritime vocational training. Unlike previous studies that focus on commercial ship applications, this study examines PdM within the context of training vessels at Poltekpel SULUT, bridging the gap between academic training and industry expectations. Objectives: The study seeks to answer: How does predictive maintenance improve the efficiency, cost-effectiveness, and reliability of naval auxiliary systems in training vessels? Methodology: A qualitative approach was employed, integrating sensor-based performance analysis, structured interviews, and questionnaire surveys involving cadets, instructors, and industry professionals. Data were analyzed through thematic categorization, cross-group comparisons, and narrative synthesis. Results: PdM demonstrated high effectiveness in reducing downtime (92/100), optimizing maintenance efficiency (91/100), and aligning with industry practices (89/100). However, challenges in sensor accuracy (85/100) and training integration were identified. Conclusions: The findings highlight the necessity of incorporating predictive maintenance into maritime training curricula to equip future engineers with the skills required for Industry 4.0 maintenance solutions, ensuring better operational efficiency and sustainability in the maritime sector.

This study examines the application of Educational Data Mining (EDM) to predict the academic per-formance of first-year students at the Catholic University of Bukavu and the Higher Institute of Edu-cation (ISP) in the Democratic Republic of Congo. The primary objective is to develop a model that can identify at-risk students early, providing the university with a tool to enhance student support and academic guidance. To address the challenges posed by data imbalance (where successful cases outnumber failures), the study adopts a hybrid methodological approach. First, the SMOTE algorithm was applied to balance the dataset. Then, a stacking classification model was developed to combine the predictive power of multiple algorithms. The variables used for prediction include the National Exam score (PEx), the secondary school track (Humanities), and the type of prior institution (public, private, or religious-affiliated schools), as well as age and sex. The results demonstrate that this approach is highly effective. The model is not only capable of predicting success or failure but also of forecasting students' performance levels (e.g., honors or distinctions). Moreover, the use of the Apriori association rule mining algorithm allowed the identification of faculty-specific success profiles, transforming prediction into an interpretable decision-support tool. This research makes several significant contributions. Practically, it provides the University of Bukavu with a tool for student orientation and early risk detection. Methodologically, it illustrates the effectiveness of a combined approach to EDM in an African context. However, the study acknowledges certain limitations, including the non-public nature of the data and the geographical specificity of the sample. It therefore proposes avenues for future research, such as the integration of Explainable AI (XAI) techniques for more refined and transparent analysis of the results.

Artificial Intelligence (AI) has increasingly shaped the digital transformation of higher education, particularly through its integration with Learning Management Systems (LMS). Features such as intelligent tutoring, predictive analytics, plagiarism detection, and automated grading are reshaping teaching and learning. However, questions remain regarding the readiness of higher education institutions and the acceptance among lecturers and students. This paper presents a Systematic Literature Review (SLR) of studies published between 2020 and 2025, focusing on readiness and acceptance of AI in LMS. Guided by the PRISMA framework, 220 records were identified, 85 screened, 40 assessed for eligibility, and 20 included in the final analysis. Findings highlight that readiness is largely influenced by infrastructure, digital literacy, and institutional policy, while acceptance is shaped by perceived usefulness, ease of use, trust, and behavioural intention. Although challenges such as ethics, cost, and privacy concerns persist, opportunities exist in the form of personalized learning and intelligent decision-making. The review concludes that while AI adoption in LMS is progressing globally, developing contexts such as Malaysian polytechnics require further research and targeted interventions to enhance both readiness and acceptance.

The reliability of power distribution systems is a crucial factor in ensuring stable electricity supply for industrial, commercial, and household users. Conventional protection systems often face limitations in terms of real-time monitoring, remote control, and adaptive responses to fault conditions, which can result in longer outage durations and higher operational costs. This research aims to develop a smart protection system for power distribution using Internet of Things (IoT) technology to enhance system reliability. The proposed method integrates IoT-enabled sensors, microcontrollers, and communication modules to monitor critical parameters such as voltage, current, and frequency in real time. Data are transmitted to a cloud-based platform for analysis and decision-making, enabling rapid detection of abnormalities and remote tripping of circuit breakers. The prototype was tested under various fault scenarios, including short circuits and overloads, and demonstrated faster response times compared to conventional systems. Results show that the IoT-based protection system improved fault detection accuracy, reduced downtime, and provided predictive maintenance insights through data analytics. The synthesis of these findings highlights that integrating IoT into protection mechanisms not only increases operational reliability but also supports the transition toward smart grids. In conclusion, the developed system proves effective in addressing the limitations of traditional protection systems by offering real-time monitoring, automation, and enhanced decision-making for modern power distribution networks.

The integration of Internet of Things (IoT) with cloud computing has revolutionized healthcare systems, offering scalable and real-time patient monitoring. However, optimizing response times and energy consumption remains crucial for efficient healthcare delivery. This research evaluates various algorithmic approaches for workload migration and resource management within IoT cloud-based healthcare systems. The performance of the implemented algorithm in this research, Hybrid Dynamic Programming and Long Short-Term Memory (Hybrid DP+LSTM), was analyzed against other six key algorithms, namely Gradient Optimization with Back Propagation to Input (GOBI), Deep Reinforcement Learning (DRL), improved GOBI (GOBI2), Predictive Offloading for Network Devices (POND), Mixed Integer Linear Programming (MILP), and Genetic Algorithm (GA) based on their average response time and energy consumption. Hybrid DP+LSTM achieves the lowest response time (82.91ms) with an energy consumption of 2,835,048 joules per container. The outcome of the analysis showed that Hybrid DP+LSTM have significant response times improvement, with percentage increases of 89.3%, 79.0%, 83.8%, 97.0%, 99.8%, and 99.94% against GOBI, GOBI2, DRL, POND, MILP, and GA, respectively. In terms of energy consumption, Hybrid DP+LSTM outperforms other approaches, with GOBI2 (3,664,337 joules) consuming 29.3% more energy, DRL (2,973,238 joules) consuming 4.9% more, GOBI (4,463,010 joules) consuming 57.4% more, POND (3,310,966 joules) consuming 16.8% more, MILP (3,005,498 joules) consuming 6.0% more, and the GA (3,959,935 joules) consuming 39.7% more. The result of ablation of the Hybrid DP+LSTM model achieves a 47.05% improvement over DP-only (156.57ms) and a 70.64% improvement over LSTM-only (282.41ms) in response time. On the energy efficiency side, Hybrid DP+LSTM shows 22.80% improvement over LSTM-only (3,671,51 joules), but 7.34% underperformance compared to DP-only (2,640,93). These research findings indicate that the Hybrid DP+LSTM technique provides the best trade-off between response time and energy efficiency. Future research should further explore hybrid approaches to optimize these metrics in IoT cloud-based healthcare systems.

This study examines the influence of ship maintenance and spare-part availability on the operational continuity of the AHTS Transko Andalas, operated by Pertamina Marine Solutions. Using a qualitative-descriptive approach with supportive quantitative indicators, data were collected through semi-structured interviews, questionnaires, onboard observations, and document analysis. The Spare-Parts Readiness Index (SPRI) and Planned Maintenance Compliance (PMC) were applied to measure vessel preparedness. Results revealed that both maintenance and spare-part availability positively and significantly impact operational continuity, though challenges remain in procurement delays, critical maintenance compliance, and workforce competence in predictive diagnostics. Thematic analysis highlighted procurement bottlenecks, maintenance execution gaps, and human resource limitations as recurring barriers. Findings contribute to maritime operations management, shipping sustainability, and vocational education by demonstrating the interdependence of technical and human factors in sustaining offshore vessel reliability. The study emphasizes the urgency of integrated procurement reforms, condition-based maintenance, and workforce training to achieve sustainable maritime operations.

This study analyzes the effectiveness of implementing predictive maintenance (PdM) on the final drive components of the Komatsu PC200-8 unit at PT. Antareja Mahada Makmur, Site PT. Multi Harapan Utama, East Kalimantan, in an effort to reduce downtime and operational losses. Before the implementation of PdM in 2022, there were 12 repair cases for the final drive with a total downtime of 772.1 hours, repair costs amounting to IDR 310.6 million, rental income loss of IDR 208.03 million, and total losses of IDR 518.63 million. In 2023, during the PdM transition phase, the number of cases decreased to 4, with a total loss of IDR 252.05 million, although downtime remained high (714.6 hours) due to the limited scope of PdM implementation on certain units and components. In 2024, with full PdM implementation, the number of repair cases decreased to 5, with total downtime of only 96 hours and losses of IDR 45.75 million. The cost of PdM implementation for the year was only IDR 21.9 million. As of July 2025, no further damage to the final drive has been recorded, demonstrating a significant improvement in equipment reliability. The reduction in total losses from 2022 to 2024 amounted to IDR 472.88 million, indicating PdM’s effectiveness in avoiding significant costs through condition monitoring methods such as oil analysis, magnetic plug rating, thermal inspection, and oil leak testing (floating seal). The findings of this study confirm that PdM is effective in reducing downtime, repair costs, and enhancing asset management in the mining sector. It also improves equipment reliability and overall operational efficiency, proving PdM to be a successful strategy in reducing losses, increasing productivity, and supporting the sustainability of company operations.

This study aims to deeply examine the role and impact of AI implementation in the B2B sales process, including implementation challenges. The method used is a qualitative approach with descriptive studies, through a literature review of scientific journals, industry reports, and relevant previous studies. The analysis results show that the application of AI in B2B sales not only improves the accuracy of marketing strategies and lead conversion, but also strengthens the concept of value co-creation through collaboration between salespeople and AI systems. Technologies such as machine learning, predictive analytics, and NLP-based chatbots have been proven to accelerate sales cycles, expand service reach, and increase productivity by up to 30%. However, implementation challenges remain, including limited digital infrastructure, a lack of competent human resources, and organizational resistance to technological transformation. Therefore, optimal AI integration requires institutional readiness, adaptive strategies, and continuous investment in technology and human resource development. These findings provide theoretical and practical contributions to the development of AI-based B2B sales strategies, particularly in the context of the digital industry in Indonesia. Furthermore, the application of AI in B2B sales also opens up new opportunities for service personalization. With the support of real-time data analysis, companies can better understand the specific preferences and needs of their business partners. This enables the development of more targeted communication strategies and the enhancement of long-term relationships with business customers. AI also plays a role in reducing human error through automated systems that can validate data, provide predictive recommendations, and support faster and more accurate strategic decision-making. Furthermore, the adoption of AI in B2B requires clear regulations and governance, particularly regarding ethical data use and information security. Companies must be able to balance the use of technology to increase efficiency with the protection of privacy and consumer rights.

This paper explores the transformative influence of artificial intelligence (AI) on the accounting profession, particularly within the Accountant General of the Federation (OAGF). The research investigates how AI-driven innovations are reshaping traditional accounting practices and redefining the role of accountants. By conducting a systematic literature review, this study identifies three primary dimensions of AI’s impact: the automation of repetitive tasks such as data entry, transaction processing, and reconciliation; enhanced data analytics capabilities, which include predictive modeling and real-time decision support; and the evolution of accountants' roles toward more strategic and value-added activities, such as financial advisory and risk management. The automation of routine processes through AI allows accountants to focus on higher-level tasks that require judgment, creativity, and expertise, ultimately enhancing the overall efficiency of the accounting function. Furthermore, AI’s advanced data analytics tools provide more accurate insights, enabling accountants to offer more effective financial guidance and make more informed decisions. As AI reduces the time spent on manual processes, accounting professionals can improve their role in advising on business strategy, improving risk management, and identifying new growth opportunities. The study’s findings underscore the importance of embracing AI in the accounting profession, not only to improve operational efficiency, reduce costs, and scale operations but also to enable accountants to stay competitive in a rapidly evolving technological landscape. The paper concludes by emphasizing that adopting AI is essential for accountants to remain relevant and continue providing valuable contributions to their organizations. Future research should focus on the long-term implications of AI on accounting ethics and the development of necessary skills for accounting professionals to thrive in the age of AI.

Environmental health risks, including air pollution, unsafe water, and climate-sensitive diseases, remain pressing global challenges that continue to threaten public well-being. Conventional monitoring systems are typically manual, costly, and geographically limited, making it difficult to provide timely and accurate data for intervention. This study explores how digital health technologies—specifically mobile health (mHealth), the Internet of Things (IoT), artificial intelligence (AI), and remote sensing—are applied to strengthen the monitoring and management of environmental health risks. A structured literature review was carried out by synthesizing 87 peer-reviewed articles published between 2013 and 2024, using an evaluation framework built on keyword clustering, metadata filtering, and multi-criteria scoring to assess usability, scalability, interoperability, and relevance to health outcomes. Findings show that mHealth platforms are highly accessible and user-friendly but often face limitations in integration with broader health systems. IoT and AI technologies offer strong scalability and predictive capability, particularly in real-time risk detection, though they are hindered by interoperability issues across platforms. Meanwhile, remote sensing is powerful for capturing large-scale environmental data but lacks direct connections to health-specific applications. The analysis identifies a critical gap in the integration of these technologies, emphasizing the need for cross-sector collaboration to build more robust, interoperable systems. Additionally, the findings highlight the importance of ethical considerations, validation processes, and interdisciplinary approaches to ensure sustainable and impactful implementation. Overall, this study provides not only a comparative synthesis of current practices but also a methodological roadmap to guide future digital innovations in environmental health. By bridging technological potential with practical application, it underscores the urgent need for integrated strategies that can better address the growing complexity of environmental health risks in the modern era.

This study rigorously investigates the Ulul Albab spiritual leadership construct and its impact on psychological well-being among campus da'wah activists, employing a Confirmatory Factor Analysis (CFA) approach grounded in both theoretical integration and empirical data. The inquiry draws upon a meta-analysis of 47 quantitative studies encompassing a total of 12,847 respondents from leading universities in Indonesia, Malaysia, and Brunei Darussalam, spanning the years 2018 to 2024. This methodological scope enhances the external validity of the findings. The CFA results confirm that the Ulul Albab spiritual leadership model exhibits a robust level of model fit, as indicated by optimal statistical indices (χ²/df = 2.34; CFI = 0.956; TLI = 0.943; RMSEA = 0.047; SRMR = 0.039), suggesting strong coherence between the theoretical construct and field data. The three core dimensions conceptualized in this model are statistically validated through high factor loadings: intellectual spirituality (0.847), Islamic transformational leadership (0.823), and emotional-spiritual intelligence (0.791), each serving as integral pillars of the Ulul Albab paradigm. Furthermore, structural regression analysis reveals a significant effect of Ulul Albab spiritual leadership on the psychological well-being of da'wah activists (β = 0.673; p < 0.001; R² = 0.453), thereby affirming the model's predictive strength in fostering individual potential rooted in Islamic spiritual values. These findings reinforce the theoretical propositions advanced by Garden, M. (2004), Fry (2003), and Zohar and Marshall (2000) regarding the significance of spiritual leadership within organizational dynamics. However, this study offers a novel conceptual contribution through the epistemological synthesis of the Ulul Albab construct, integrating intellectual and spiritual intelligence within a holistic Islamic framework. Unlike Western-based models of spiritual leadership proposed by Fry and Nisiewicz (2020), the Ulul Albab construct demonstrates a notable advantage in the dimension of intellectual spirituality, as evidenced by its higher factor loading (0.847 compared to 0.634), underscoring the imperative of balancing dzikir and fikir as both ethical and cognitive foundations in contemporary Islamic leadership.

Chronic Kidney Disease (CKD) is a major global health issue that can lead to serious complications and long-term medical care. This study aims to identify key clinical factors associated with CKD status using binary logistic regression analysis. The dataset, obtained from Kaggle, contains 400 patient records with various clinical and demographic attributes. The dependent variable is CKD status (positive or negative), while the independent variables include age, blood pressure, hemoglobin level, urine albumin level, and serum creatinine. Initial analysis involved descriptive statistics and multicollinearity checks, followed by model estimation and evaluation using likelihood ratio and Wald tests. The final model identified four significant predictors: blood pressure, hemoglobin, urine albumin, and serum creatinine. The model achieved a high classification accuracy of 95.50% and an Area Under the ROC Curve (AUC) of 98.78%, indicating excellent predictive performance. These results highlight the importance of these clinical indicators in early CKD detection and support their use in risk assessment models for kidney disease screening Keywords: Chronic Kidney Disease, Binary Logistic Regression, Likelihood Ratio Test, Wald Test, Classification Accuracy

Dengue Fever (DF) continues to be a major public health threat in Indonesia, especially in urban areas with high population density, such as Purwokerto City. This study aims to develop a predictive model to identify high-risk areas for DF outbreaks by integrating Machine Learning (ML) algorithms and Geographic Information Systems (GIS). The research utilizes historical dengue case data, meteorological parameters (rainfall, temperature, humidity), and population density as predictive variables. Three ML classification algorithms—Naïve Bayes, Logistic Regression, and Support Vector Machine (SVM)—were implemented to develop risk prediction models. Extensive data preprocessing, feature selection, and spatial integration were applied to ensure model robustness. The results show that the SVM model outperformed other methods, achieving the highest accuracy, precision, recall, and F1-score in classifying dengue risk zones. Risk maps generated through GIS visualization successfully identify priority areas for targeted interventions. The novelty of this research lies in the combination of local epidemiological data, multi-algorithm comparison, and geospatial mapping to improve early warning systems for DF in Purwokerto. This integrated approach is expected to support more effective prevention strategies and enhance public health preparedness.

Airport security is a vital component in maintaining the stability of air transportation systems. Although scanning technologies and access control systems have significantly advanced, the potential threat posed by internal actors remains an unresolved vulnerability. This study aims to examine the feasibility of integrating artificial intelligence (AI) technologies to detect threat intentions through gesture and body temperature analysis, with a specific focus on the apron zone a highly vulnerable area of the airport. Utilizing a hypothetical scenario based on the Red Team method, this study maps potential breach pathways conducted by individuals with authorized access. The findings suggest that the integration of computer vision, thermal imaging, and behavioral profiling has the potential to identify anomalous behaviors indicative of malicious intent. This research highlights the importance of combining technological approaches with human-centered security strategies to develop a more adaptive and accurate predictive security system.

This study aims to analyze the failure risks of UV digital printing machines used at PT XYZ using the Failure Mode and Effects Analysis (FMEA) method. In the digital printing industry, the continuity of machine operations heavily depends on effective maintenance to prevent downtime that could disrupt the production process. The FMEA method is employed to identify potential failure modes and evaluate the severity, occurrence, and detection of each failure. This research adopts a quantitative approach, with data collected through observations, interviews, and historical documentation. The analysis reveals that the printhead, cooling system, and UV lamp are the components with the highest Risk Priority Numbers (RPN), at 504, 441, and 288 respectively. Based on these findings, it is recommended that preventive and predictive maintenance strategies be enhanced to avoid recurring failures.

In a context where students face increasingly complex academic choices, this work proposes a recommendation system based on Bayesian networks to guide new baccalaureate holders in their university choices. Using a dataset containing variables such as secondary school section, gender, type of school, percentage obtained, age, and first-year honors, we have constructed a probabilistic model capturing the dependencies between these characteristics and the option chosen. The data is collected at the Catholic University of Bukavu, the Official University of Bukavu, and the Higher Institute of Education of Bukavu, preprocessed and then used to learn the structure via the hill-climbing algorithm with the BIC score using R's bnlearn tool. The model enables us to estimate the probability that a candidate will choose a given stream, depending on their profile. The approach has been validated using metrics such as BIC, cross-validation, and bootstrap and offers a good compromise between interpretability and predictive performance. The results highlight the potential of Bayesian networks in constructing explainable recommendation systems in the field of academic guidance. The system produces orientation probability maps for each candidate, which can be used by enrollment service advisers, as well as an ordered list of options relevant to the candidate's profile. With a remarkable performance on a test sample of precision@k=0.85, recall@k=0.61, ndcg=0.8, and Map=0.88, it constitutes an effective lever for reducing the risk of being misdirected in universities in South-Kivu, in the Democratic Republic of Congo

The COVID-19 pandemic has exposed fundamental vulnerabilities in global supply chain systems, such as over-reliance on single suppliers and a lack of operational visibility. This has highlighted the urgent need for a new approach to risk management—one that leverages smart technologies. Artificial Intelligence (AI) has emerged as a promising solution, thanks to its capabilities in predictive analytics and adaptive, data-driven decision-making in real time. This study aims to develop an AI-based predictive system framework to enhance the resilience of global supply chains in the face of post-pandemic disruptions. Using the Design Science Research (DSR) methodology, the research designs and evaluates a system that integrates algorithms such as LSTM, Random Forest, Natural Language Processing (NLP), and Reinforcement Learning. It also applies a federated learning approach to ensure data privacy among supply chain partners. The study analyzes over 12,000 data entries from diverse sources, including IoT devices, weather data, demand trends, and social media. The system's effectiveness is evaluated through a combination of quantitative methods (PLS-SEM analysis on 103 respondents) and qualitative methods (interviews with 12 industry executives). The findings show that AI-driven predictive analytics significantly improve supply chain resilience (β = 0.67; p < 0.001), with demand forecasting accuracy increasing by up to 40% and delivery times reduced by 30%. Conceptually, the study contributes by designing a resilient model that integrates real-time visibility, adaptability, and cross-organizational collaborative learning. Unlike traditional approaches focused solely on automation, this framework offers a more holistic solution, addressing key gaps in the literature. The implication is clear: AI is becoming a strategic asset in building sustainable, resilient supply chains amid ongoing global uncertainty.