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Poverty is one of the main problems that hinder regional development. Deli Serdang Regency shows a fluctuating trend in the number of poor people from year to year. To support government policies in overcoming poverty, an accurate forecasting method is needed to predict the number of poor people in the future. This study uses the Single Moving Average (SMA) method with two period variations, namely n = 2 and n = 3, based on data from the Central Statistics Agency (BPS) of Deli Serdang Regency for 2017–2023. The forecasting results show that the SMA method with n = 3 provides better accuracy than n = 2, as indicated by the Mean Squared Error (MSE) value of 21.38, Mean Absolute Deviation (MAD) of 4.44, and Mean Absolute Percentage Error (MAPE) of 3.52%. These findings indicate that the SMA method is capable of providing fairly accurate predictions and can be used as a basis for regional development policy planning to reduce poverty in Deli Serdang Regency in 2024.

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 conduct customer segmentation and develop a classification model to predict the clusters of new customers at Monex Toys Abadi Bekasi, a micro, small, and medium enterprise (MSME). Segmentation was performed using the K-Means Clustering algorithm, incorporating parameters such as Recency, Frequency, Monetary (RFM), purchased products, payment methods, shipping cost discounts, and the total number of products purchased by customers. The segmentation results revealed two clusters: (1) Discount Hunters and (2) Loyal Customers. Subsequently, a classification process was conducted to predict customer clusters using the K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) algorithms. Evaluation results indicated that all models achieved high accuracy exceeding 98%. The best-performing model was obtained with SVM using a 70:30 data split, achieving an accuracy of 98.81%. This classification model was then implemented into a Streamlit-based cluster prediction application, enabling users to identify customer segments in real-time. The findings of this research are expected to assist MSMEs in understanding customer behavior, enhancing service quality, and supporting more effective marketing strategies.

Bankruptcy is a condition where a business cannot operate effectively due to severe financial difficulties it is currently experiencing.  This research purposes to analyze the ratio of the Altman, Springate, and Grover models in analyzing bankruptcy in food and beverage corporations listed on the IDX with reference to signaling theory. The data analysis approach used are One Way Anova test and accuracy level test. Using 20 company samples with purposive sampling method. The final proceeds of the research explain that there are significant differences between the Altman and Springate models, significant differences between the Altman and Grover models, and no significant differences between the Springate and Grover models in predicting bankruptcy in food and beverage companies for the period 2019-2023. The very accurate prediction model was achieved by the Grover model.

Financial distress refers to a company’s persistent inability to meet financial obligations, signaling severe monetary strain that precedes formal bankruptcy or liquidation proceedings. This study investigates the impact of intellectual capital (VAICTM), operational capacity (TATO), capital structure (DER), and operating cash flow (OCF) on financial distress (Altman Z-Score), with profitability (ROA) serving as a mediating variable. The theoretical framework of this research is grounded in signaling theory, agency theory, and resource-based view theory. The study focuses on basic materials companies listed on the Indonesia Stock Exchange (IDX) between 2019 and 2023. The study utilized criterion-based sampling to select qualified respondents. Secondary datasets were analyzed through panel regression and path analysis, with Eviews 12 as the computational tool. Key findings include: (1) intellectual capital and operating capacity demonstrate a statistically significant positive influence on profitability; (2) capital structure exerts a significant adverse impact on profitability; (3) operating cash flow exhibits no statistically discernible impact on profitability; (4) both operating cash flow and profitability are positively and significantly associated with increased financial distress; (5) capital structure displays a significant inverse relationship with financial distress severity; (6) intellectual capital and operating capacity show no statistically significant associations with direct financial distress prediction; (7) profitability partially mediates the influence of intellectual capital, operating capacity, and capital structure on financial distress; and (8) profitability does not serve as a mediating variable between operating cash flow and financial distress.

This study explores the development and application of a digital twin-driven cybersecurity risk assessment model for Industrial Internet of Things (IIoT) networks. The increasing complexity and interconnectivity of IIoT systems have expanded the attack surface, making them vulnerable to a wide range of cyber threats. The digital twin model addresses this challenge by creating real-time virtual replicas of physical systems, which can simulate and predict network vulnerabilities and attack vectors. The model uses machine learning algorithms and real-time data to simulate cyberattacks, including Distributed Denial of Service (DDoS), malware, and data breaches. By providing continuous monitoring and dynamic risk predictions, the digital twin model enhances the resilience of IIoT networks compared to traditional cybersecurity frameworks. The findings indicate that the model's ability to predict potential cyber threats and simulate various attack scenarios provides a more proactive and accurate approach to cybersecurity in IIoT environments. Additionally, the study highlights key mitigation strategies, including adaptive security mechanisms, real-time anomaly detection, and the use of lightweight encryption for resource-constrained devices. Despite its effectiveness, challenges such as computational requirements, integration with legacy systems, and scalability were identified. This research underscores the strategic importance of digital twin models in securing IIoT systems and advancing Manufacturing 4.0 ecosystems. Future research should focus on enhancing model accuracy, expanding its application to diverse industrial sectors, and improving interoperability with legacy systems to further strengthen the security posture of IIoT networks.

Background: The rapid advancement of digital technologies in the Industry 4.0 era has transformed industrial mechanical systems into highly interconnected and data driven environments through the integration of sensors, the Internet of Things (IoT), data analytics, and cyber physical systems. This increasing complexity requires more adaptive and accurate monitoring and prediction methods than conventional simulation approaches, which often face limitations in capturing real time dynamic system behavior. Objective: This study aims to develop a predictive performance model for industrial mechanical systems by integrating Digital Twin technology with Physics Informed Machine Learning in order to improve monitoring accuracy and support predictive maintenance strategies. Methods: This research adopts a data driven modeling and simulation approach by developing a digital representation of an industrial mechanical system that is connected to real time sensor data. The prediction model is constructed using a Physics Informed Neural Network (PINN), which integrates operational data with physical principles governing system dynamics. The research process includes the development of a Digital Twin model, integration of sensor data, training of the PINN model, model validation using experimental data, and evaluation of prediction performance using statistical metrics. Results: The results indicate that the integration of Digital Twin technology and PINN significantly improves the prediction accuracy of industrial mechanical system performance compared with conventional simulation methods and purely data driven machine learning models. The proposed model is capable of representing system dynamics more consistently, accurately following sensor data patterns, and providing strong potential for supporting machine condition monitoring and predictive maintenance strategies in modern industrial environments.

This study examines the factors that influence financial distress in companies affiliated with Israel, focusing on the roles of profitability, liquidity, leverage, sales growth, and firm size. The research is driven by the phenomenon of boycotts caused by geopolitical conflicts involving Israel, which have impacted the financial performance of several companies, particularly in Indonesia. The study uses a quantitative approach, analyzing a sample of companies listed on the Indonesia Stock Exchange (IDX) that are affiliated with Israel during the 2023-2024 period. The data consists of quarterly financial statements, which are analyzed using the Altman Z-Score bankruptcy prediction model. The findings show that profitability and liquidity have a significant effect on financial distress, while leverage and sales growth have a smaller impact. Firm size is also found to reduce the risk of financial distress. These results suggest that companies linked to Israel are more vulnerable to financial risks due to boycotts triggered by international political tensions.

Pneumonia detection from chest X-ray images is widely used in computer-aided diagnostic systems. However, effective clinical decision support requires not only accurate classification performance but also consideration of unequal error costs, since false negative predictions may lead to more severe consequences than false positives. In addition, prediction probabilities must be well calibrated to support threshold-based medical decisions such as triage and patient escalation. This research investigates asymmetric misclassification costs and probability calibration for binary classification (PNEUMONIA vs. NORMAL) using the Hugging Face dataset hf-vision/chest-xray-pneumonia. The proposed framework utilizes a ResNet-18 architecture integrated with cost-sensitive learning through weighted cross-entropy loss (FN:FP = 5:1), threshold optimization based on validation data to reduce expected cost, and post-hoc temperature scaling for improving probability calibration. Experimental results on the independent test set indicate that the cost-sensitive approach enhances specificity and decreases expected cost compared to the conventional cross-entropy baseline. Furthermore, temperature scaling improves the reliability of probabilistic predictions, as demonstrated by better negative log-likelihood and Brier score values. The study also explores selective prediction strategies to balance prediction coverage and risk reduction, complemented by Grad-CAM visualizations and structured failure-case analysis for qualitative assessment. Overall, the findings demonstrate that incorporating cost-aware decision thresholds and calibrated probability estimates can serve as lightweight yet effective enhancements for chest X-ray classification systems in clinical decision-support applications.

This study examines the role of smart technology, particularly Artificial Intelligence (AI) and the Internet of Things (IoT), in strengthening economic resilience in the face of climate change impacts. Using a qualitative descriptive approach with a literature stufy method, secondary data was obtained from scientific journals, books, proceedings, and relevant online articles. The analysis was conducted through reduction, categorization, and thematic analysis of the relevant literature. The results show that AI contributes significantly to improving economic efficiency and risk prediction compabilities. While IoT strengthens connectivity and automation that support supply chain stability, the intregration of AI and IoT in the agricultural sector significantly increases productivity and food security. In addition, smart technology is also an effective mitigation tool against exctreme climate variations that impact the economy and society. This study emphasizes the importance of cross-sector collaboration and digital infrastructure investment to build adaptive and sustainable economic resilience. The implication of this research provide a basis for policy strategies and digital innovation in an era of increasing dynamic climate change.

The importance of data mining in Indonesia is increasing along with the growth of big data in various strategic sectors. Data mining plays an important role in transforming complex data into useful information to support data-driven decision making, which is urgently needed in the face of competitive challenges and operational complexity. This research aims to examine the development of data mining techniques and applications in Indonesia over the last decade (2015-2024). Through a systematic literature review approach, data was collected from academic publications in SCOPUS indexed databases. From the initial 95 papers found, a further selection was made based on accessibility, title, and abstract until 64 papers were included in the article review. The results show that techniques such as K-Means, Naive Bayes, and Decision Tree are most commonly used. In the business sector, clustering through K-Means is widely applied for market segmentation and consumer pattern analysis. The healthcare sector mainly utilizes classification techniques, such as Naive Bayes and Decision Tree, for disease risk prediction and early diagnosis. Meanwhile, the education sector uses data mining to assess student performance and predict potential dropouts, assisting institutions in optimizing learning strategies.

The manufacturing industry faces major challenges in maintaining consistent product quality amidst the dynamics of technology and global competition. This study aims to develop an effective Business Intelligence (BI) implementation model to support data-based quality control. The method used is a conceptual design approach through integrated system simulation, including MySQL database, PHP backend, Power BI visualization, Google Cloud AutoML predictive analytics, and initial processing using Microsoft Excel. Historical production data for 12 months is used for model training and defect trend visualization. The simulation results show that the implementation of BI can reduce product defect rates, accelerate system response, and increase inspection process efficiency. Technical validation proves the model's prediction accuracy is above 90%, while field validation shows positive acceptance from users regarding the ease of use of the dashboard. This system not only supports early detection of quality deviations but also contributes to real-time strategic decision making. With an integrated technology approach, BI enables medium-sized manufacturing companies to adopt an adaptive and sustainable digital quality system, in line with the concept of Quality 4.0.

The performance of data mining techniques has been proven accurate in many studies, but each method in data mining techniques has different accuracy depending on the type of data that is the object of research. Methods in data mining techniques are divided into several functions, namely: clustering, association, classification, and prediction, where each data mining technique objective has a superior method. Therefore, in this case the author will compare the performance of the multiple linear regression method, and neural networks with fuzzy mamdani in predicting the income of PLN Unit Takengon. In several studies, the Backpropagation method shows the highest accuracy compared to other methods. Then the prediction model with multiple linear regression also has the highest accuracy as well as the Fuzzy Mamdani method has high accuracy too. Therefore, the purpose of this study is to compare the three methods, so that it can be determined which method has a higher accuracy value. The results of this study indicate that the Back propagation method has the highest accuracy and the lowest average error, namely a MAPE value of 5.9% with an accuracy of 94.1% and an RMSE of 14398.14, followed by the multiple linear regression method obtaining a MAPE value of 6.9% with an accuracy of 93.1% and an RMSE of 15527.41, then for Fuzzy Mamdani obtaining a MAPE value of 7% with an accuracy of 93% and an RMSE of 16077.76.

This research is based on the problem of low obedience and compliance of students in carrying out discipline at State Senior High School 1, Pasir Penyu Indragiri Hulu District. This study aims to find out that there is a significant influence of student management on the level of discipline of grade IX students at State Senior High School 1, Pasir PenyuIndragiri Hulu District. The research method used is the quantitative research method. Data collection uses observation, questionnaires, interviews and documentation. The research population was 60 people. The data analysis technique used is product moment correlation and multiple linear regression. To analyze the data using the IBM SPSS 25 Software program. The results of the study are first: There is a significant influence of student management on the level of discipline of grade IX students at State Senior High School 1, Pasir PenyuIndragiri Hulu District, because the results of the hypothesis test in the product moment analysis, a correlation coefficient value of 0.439 (calculated) > 0.254 (table) and a significance of 0.000 < 0.05 which means that there is a significant correlation of student management to the level of student discipline with a The relationship between variables is located at 0.41 – 0.60 which means it belongs to the category of moderate correlation. Then, based on the results of the regression equation calculation, the prediction of the value of the Y variable that is influenced by the X variable optimally is 26.432. This means that when the student management variable will increase by 18.88. Meanwhile, when the variable of student management is optimally increased, which is 40 points, the variable of the level of discipline of grade IX students will increase by 107,423. And based on the t-test, a regression coefficient value of 3,720 was obtained with a significance of 0.000 < 0.05. Therefore, it can be concluded that there is a significant influence of student management on the level of discipline of grade IX students at State Senior High School 1, Pasir Penyu Indragiri Hulu District.

The life insurance industry plays a strategic role in the national financial system, not only as a provider of protection against life risks such as premature death or critical illness, but also as an instrument of long-term fund accumulation. Increased public awareness of the importance of risk protection has driven significant growth in the number of active policies. This condition has a direct impact on the risk exposure of claims that must be carefully managed by insurance companies. One of the main challenges in risk management is to accurately estimate the number of claims in a certain period, to support premium setting, technical reserve planning, and maintain the company's financial stability. This study aims to examine the use of Poisson regression model in estimating the frequency of life insurance claims based on the number of active policies in life insurance company. The data used is simulative and represents an exponential relationship between the number of policies and claims. The model is analyzed using the Maximum Likelihood Estimation (MLE) approach and evaluated through goodness-of-fit indicators such as deviance, Pearson chi-square, log-likelihood, and Mean Squared Error (MSE). The results of the analysis show that the Poisson regression model can capture the significant relationship pattern between the number of active policies and claims, and provide accurate prediction results. Thus, Poisson regression is proven to be a relevant and applicable statistical method in supporting strategic decision-making in insurance companies, especially in the context of data-driven risk management.

Econometrics is a discipline in economics that combines economic theory, mathematics, and statistics to quantitatively assess economic phenomena. This paper aims to introduce econometrics as an important tool in economic analysis and explain its applications in various sectors such as macroeconomics, microeconomics, development, and finance. Using a descriptive qualitative research method based on literature review, this paper explains how econometrics is used to test economic conjectures, make predictions, and support data-driven decisions. In addition, this article also discusses the advantages of econometrics, the challenges that arise in its application, and the software used in econometric analysis, including Excel, SPSS, and EViews. The findings show that econometrics is useful not only for academics but also for decision makers and business actors in designing more efficient economic strategies and policies. However, several problems such as data limitations, model assumptions, and specification errors are still challenges that need to be overcome by increasing capabilities and utilizing technology. It is hoped that this paper can broaden understanding and encourage more effective and appropriate use of econometrics in Indonesia.

. The Framingham Risk Score (FRS) assesses coronary heart disease (CHD) risk and predicts acute coronary events. Metabolic markers like LDL cholesterol, fasting blood glucose, uric acid, triglycerides, and TG/HDL ratio play critical roles in atherosclerosis and cardiovascular risk. Elevated LDL cholesterol, fasting blood glucose, and uric acid contribute to plaque formation, inflammation, and vascular damage, while high triglycerides and low HDL cholesterol exacerbate atherogenesis. This study explores the relationship between these markers and FRS to enhance CHD risk prediction and support targeted cardiovascular interventions. This study analyzed LDL cholesterol, fasting blood glucose, uric acid, triglycerides, and TG/HDL ratio with Framingham Risk Score in 85 participants, excluding those with incomplete data or chronic illnesses. The analysis found significant correlations between metabolic parameters and the 10-year myocardial infarction risk. LDL cholesterol, triglycerides, and uric acid showed moderate positive associations with cardiovascular outcomes, while the triglyceride-to-HDL ratio and fasting blood glucose had weaker but significant correlations. These findings highlight lipid profiles and metabolic markers as key contributors to cardiovascular risk. This study highlights significant correlations between LDL cholesterol, fasting blood glucose, uric acid, triglycerides, and the triglyceride/HDL ratio with 10-year cardiovascular risk. These findings emphasize the importance of lipid profiles, glycemic control, and metabolic markers in predicting coronary outcomes and guiding targeted preventive interventions for improved cardiovascular risk management.

Thyroid cancer is the most common endocrine malignancy, with a steadily increasing incidence rate. Although the overall survival rate is relatively high, the risk of recurrence after definitive treatment such as Radioactive Iodine (RAI) therapy remains a significant clinical challenge. Predicting recurrence risk is crucial for optimizing monitoring strategies and interventions. With advances in technology, machine learning (ML) approaches are increasingly utilized to support medical predictions, including the recurrence of thyroid cancer. This study aims to evaluate the performance of four classification algorithms—Logistic Regression, XGBClassifier, Random Forest Classifier, and Voting Classifier—in predicting thyroid cancer recurrence using the Thyroid Cancer Recurrence After RAI Therapy dataset, which consists of 383 patient records and 13 key clinical attributes. The evaluation was conducted using accuracy, precision, recall, F1-score, and area under the curve (AUC) metrics. The results show that the XGBClassifier is the best-performing model with an accuracy of 97.4% and an AUC of 0.95, demonstrating superior performance in handling the minority class. This research is expected to contribute to the development of more effective machine learning–based clinical decision support systems for predicting thyroid cancer recurrence after therapy.

This study examines the application of big data analytics on Bhinneka.com, a leading e-commerce platform in Indonesia, to tackle the increasing in complexity of online user behavior in a swiftly changing digital environment. The primary issue is too challenges in evaluating extensive, unstructured, and heterogeneous user data, which obstructs personalization, marketing efficacy, and operational decision-making. The study seeks to assess the efficacy of big data instruments, specifically Artificial Intelligence Recommendation (AIRec) and Customer Data Platform (CDP), in improving user behavior forecasting. Service customization, and data-informed strategies. This study utilizes a qualitative case study methodology, including literature review and platform observation, to synthesis the many forms of big data analytics (descriptive, diagnostic, predictive, and prescriptive) and their implementation at Bhinneka.com. Significant findings indicate that the integration of AIRec and CDP has augmented the platform’s capacity to predict consumer preferences, improve marketing accuracy, and optimize logistics. However, obstacles stay the same, such as disjointed data systems, data quality concerns, and internal opposition to embracing a data-driven culture. The study suggests that although big data analytics substantially enhances Bhinneka.com’s digital competitiveness, ongoing investment in data infrastructure and organizational competence is crucial to fully harness its potential and preserve a competitive advantage in Indonesia’s e-commerce market.