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An answer to the worldwide need for solutions to food security, data fusion technology that combines climate data with satellite imagery greatly improves the accuracy of agricultural yield predictions; this study intends to examine the advancements, methods, and key contributions of this area. By sifting through 62 papers pulled from Scopus, this research employs the SLR methodology. Document type, data source, open access, subject area, and year of publication (2020–2024) are some of the categories filtered through by Boolean keywords in the selection process. To assess patterns in publications, the efficacy of machine learning models, and key contributions, bibliometric analysis was performed. An upward tendency in publication has been identified by the analysis, particularly beyond the year 2023. Integrating geographical and temporal data has been a great success with machine learning models like Random Forest, Random Forest, and Gradient Boosting. Data resolution, integration of data from several sources, and a real-time framework are still missing pieces to the puzzle when it comes to generalizing research outcomes. More complex data fusion approaches, multiregional datasets, and advanced machine learning models to back more accurate agricultural predictions are all things that this study notes as needing additional investigation in the future. To further innovate agricultural yield prediction, multidisciplinary collaboration is also crucial.

This study aims to predict the total population of Deli Serdang Regency for the year 2025 using a multiple linear regression approach. The data used were obtained from the Central Bureau of Statistics (BPS) of Deli Serdang for the years 2015–2024, with total population as the dependent variable and male population and the percentage of male population as the independent variables. The analysis was carried out through a series of basic assumption tests, including normality, multicollinearity, heteroscedasticity, and autocorrelation, all of which indicated that the model met the criteria for a valid regression model. The results of the F-test and t-test showed that both independent variables had a significant influence on the total population. The R² value of 1.000 indicates that the model is capable of explaining 100% of the variation in the population size. Based on the regression model obtained, the projected total population of Deli Serdang in 2025 is estimated to reach 4,075,362 people, an increase of 2,026,882 people from the previous year. These findings are expected to serve as a basis for regional development planning, particularly in the provision of public services and resource management.

Many used ABS plastic wastes have problems in their management. The aim of this research is to obtain a prediction of the fatigue life of a mixture of ABS plastic and used ABS as an injection molding product. The research method is carried out through the stages of mixing used ABS with a volume of 10%, 20%, and 30% in ABS grade A and grade B; making fatigue test specimen molds according to R. R. Moore standards, injection molding of a mixture of used ABS and ABS grade A and grade B, checking the straightness and smoothness of the specimen surface, fatigue testing with increasing serial loads, analysis and making a graph of bending stress (S) against fatigue life (N) (S-N Curve). The results of the study showed that the addition of used ABS had an effect on the fatigue life of both grade A and grade B of ABS, where the fatigue life of grade A of ABS increased with the addition of the volume percentage from 0% -30% of used ABS with the highest value at the addition of 30%, namely 43698.9 cycles, while in grade B of ABS, the fatigue life decreased with the increase in volume from 0% -30% with the highest fatigue life in grade B of ABS plastic without a mixture or 0%, namely 41377.5 cycles, the implication of which is that the addition of 0% -30% of used ABS in grade A can increase the fatigue life, but grade B of ABS actually decreases its fatigue life.

Logic is an important foundation in mathematics learning because it shapes critical and systematic thinking skills. Introducing the concept of proof early on plays a strategic role in developing reasoning skills, identifying patterns, and providing simple arguments. This article aims to examine the role of logic in mathematics learning by emphasizing the importance of introducing the concept of proof from the outset. The methods used are a literature review of various relevant studies and descriptive analysis based on learning practices in early childhood education. The results of the study show that play, exploration, and concrete experience-based learning activities can improve children's ability to understand cause-and-effect relationships, make predictions, and convey logical reasons. In addition, social interaction and dialogue in the learning process have been proven to strengthen simple reasoning skills. In conclusion, the integration of logic and reasoning from an early age not only supports the development of mathematical abilities, but also builds the foundation for critical and analytical thinking needed for further education.

This study aims to compare and analyze the accuracy levels of four financial distress prediction models—Altman Z-Score, Springate, Grover, and Zmijewski—in anticipating the potential bankruptcy of companies subjected to delisting from the Indonesian Stock Exchange (IDX). The delisting phenomenon, which is strongly linked to severe financial deterioration, provided the core motivation for identifying the most reliable predictive instrument, utilizing secondary data from the annual financial reports of delisted companies during the 2019-2023 observation period. Descriptive analysis techniques were employed to calculate the accuracy rate and Type Error for each model. The comparative results consistently indicate that the Springate Model is the most effective, consistent, and accurate model for predicting financial distress in delisted firms, achieving an accuracy rate of 89% in both the first and second years prior to delisting, while the Altman Z-Score model exhibited lower accuracy (68.75% and 62.50%). This key finding emphasizes the superiority of the Springate Model as a crucial diagnostic tool for investors and regulatory bodies in assessing corporate bankruptcy risk.

Chronic diseases such as chronic kidney disease (CKD), diabetes, and heart disease remain major causes of mortality worldwide, highlighting the need for accurate and interpretable diagnostic models. However, conventional machine learning methods often face challenges of limited generalization, feature redundancy, and class imbalance in medical datasets. This study proposes an integrated classification framework that unifies three complementary feature paradigms: classical tabular attributes, deep latent features extracted through an unsupervised Long Short-Term Memory (LSTM) encoder, and quantum-inspired features derived from a five-qubit circuit implemented in PennyLane. These heterogeneous features are fused using a feature-wise attention mechanism combined with an AdaBoost classifier to dynamically weight feature contributions and enhance decision boundaries. Experiments were conducted on three benchmark medical datasets—CKD, early-stage diabetes, and heart disease—under both balanced and imbalanced configurations using stratified five-fold cross-validation. All preprocessing and feature extraction steps were carefully isolated within each fold to ensure fair evaluation. The proposed hybrid model consistently outperformed conventional and ensemble baselines, achieving peak accuracies of 99.75% (CKD), 96.73% (diabetes), and 91.40% (heart disease) with corresponding ROC AUCs up to 1.00. Ablation analyses confirmed that attention-based fusion substantially improved both accuracy and recall, particularly under imbalanced conditions, while SMOTE contributed minimally once feature-level optimization was applied. Overall, the attention-guided AdaBoost framework provides a robust and interpretable approach for clinical risk prediction, demonstrating that integrating diverse quantum, deep, and classical representations can significantly enhance feature discriminability and model reliability in structured medical data.

Rabies is a deadly disease transmitted through animal bites, especially dogs, and is still a serious threat in Indonesia. As of April 2023, more than 31,000 bite cases have been recorded, with Southeast Sulawesi being the region with the highest cases. In Kendari City, data shows that bite cases have fluctuated since 2018. Research objectives: To develop a prediction model for rabies prevention measures to enforce an EWS in high school students in Kendari City. Research methods: Cross-sectional quantitative research, involving 350 students from three sub-districts (Baruga, Poasia, and Kadia) with independent variables in the form of knowledge, attitudes, and beliefs, and the dependent variable in the form of rabies prevention measures. Data were collected in February 2025 through a hybrid/online questionnaire. Research results: Knowledge, attitudes, and beliefs have a significant effect on rabies prevention measures in high school students in Kendari City. Knowledge is the most dominant factor (p = 0.000; Odds Ratio (B) = 8.747), followed by attitude (Odds Ratio (B) = 5.725) and belief (Odds Ratio (B) = 2.545). Students with good knowledge are 8.7 times more likely to take rabies prevention. If the three variables are at a low level, the probability of rabies prevention actions among students is 14.4%, but it increases significantly to 95.6% when all three variables are at a good level. Research Conclusion: There is a significant influence of knowledge, attitude, and belief on rabies prevention behavior to support the EWS among high school students in Kendari City in 2025. The probability of engaging in preventive behavior is 14.4% when all three variables are at a low level, but it increases significantly to 95.6% when all variables are at a high level. Knowledge contributes the most, while attitude and belief synergistically strengthen the overall effect.

Hair loss is one of the common health problems experienced by many people and often causes psychological impacts, particularly on self-confidence. The factors contributing to hair loss are diverse, ranging from genetics, diet, and stress to lifestyle. The lack of public knowledge about these risk factors, as well as the low level of digital literacy in the use of predictive technology, makes it difficult for people to take early preventive measures. This community service activity aims to provide education and simple training on predicting hair loss risk using the Support Vector Machine (SVM) algorithm for residents of Rantau Prapat Village. The implementation methods include a pre-test to measure initial understanding, interactive counseling on hair loss risk factors, practical simulation of risk prediction using SVM based on a simple dataset, and evaluation through a post-test. The results of the activity showed a significant increase in participants’ understanding, from an average of 45.2% in the pre-test to 81.6% in the post-test, with a participant satisfaction level reaching 92%. This counseling not only improved health literacy but also introduced the practical application of artificial intelligence in the health 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.

The integration of Augmented Reality (AR) and Explainable AI (XAI) within Cyber-Physical Systems (CPS) design is transforming the industrial automation landscape. This study explores how combining AR’s immersive visualization with XAI’s decision transparency enhances collaborative design processes in CPS. The AR-XAI platform developed in this research improves team collaboration by offering real-time visual feedback and enabling interactive decision-making. The platform provides interpretable insights into AI-driven decisions, fostering trust among engineers and decision-makers. Key features of the platform include the ability to visualize complex CPS models, facilitating faster iterations, reducing design errors, and improving design accuracy. The integration of XAI ensures transparency in decision-making by offering clear explanations of AI predictions, which is essential for ensuring accountability and building trust in automated systems. Testing with industrial engineers confirmed that the AR-XAI platform significantly improved design outcomes, with a reduction in errors and enhanced team performance compared to traditional design methods. Moreover, the platform enabled faster decision-making and improved collaboration across diverse teams, demonstrating its potential to optimize CPS design workflows. This research provides valuable insights into the role of AR and XAI in advancing Industry 4.0 and paves the way for more advanced integrations of these technologies in industrial settings. Future research should focus on developing scalable solutions for various industrial applications and exploring more sophisticated AR-XAI integrations for emerging fields like smart cities and autonomous manufacturing.

Traffic violations are one of the serious problems frequently occurring in various regions, including Binjai City. Various types of violations, such as disobeying road signs and markings, incomplete vehicle documents, and violations that threaten the safety of drivers and other road users, continue to increase despite preventive and repressive efforts carried out by the authorities. This condition indicates that handling traffic violations cannot rely solely on field enforcement but also requires the support of technology capable of analyzing data more comprehensively. This study aims to predict the level of traffic violations by applying the Naïve Bayes method through data mining techniques. The dataset used consists of traffic violation records in 2023 from the Binjai City Police Department, with the main variables including violations of traffic signs and markings, document completeness, and safety-related violations. The Naïve Bayes method was selected because of its ability to perform classification with good accuracy, simplicity, and efficiency in processing large amounts of data. The implementation of this research is realized by developing a web-based application using Visual Studio Code as the development environment and MySQL as the database system. The results of this study are expected to provide structured information regarding traffic violation patterns, support authorities in making more effective decisions, and serve as an alternative solution in the prevention and handling of traffic violations in Binjai City.

Sleep is a fundamental human need that plays a crucial role in maintaining both physical and mental health. Poor sleep quality can trigger a variety of health problems, ranging from decreased concentration to an increased risk of chronic diseases. The complexity of factors influencing sleep quality—such as stress levels, heart rate, blood pressure, physical activity, and lifestyle—makes its assessment difficult through direct observation alone. Therefore, data mining approaches are increasingly utilized to identify relevant patterns in sleep-related data. This study aims to compare the performance of the C4.5 (Decision Tree) algorithm and the Naïve Bayes algorithm in predicting sleep quality using the Sleep Health and Lifestyle dataset, which contains information from 374 respondents. The research method applied is a quantitative comparative approach employing classification techniques with 10-fold cross-validation to ensure robust evaluation. Model performance is assessed using accuracy, precision, and recall metrics to provide a comprehensive understanding of the effectiveness of each algorithm. The findings indicate that the C4.5 algorithm achieves an accuracy of 96.26% and offers advantages in terms of interpretability through its decision tree visualization, enabling easier understanding of variable relationships. In contrast, the Naïve Bayes algorithm demonstrates superior predictive performance, achieving an accuracy of 98.66% along with consistently high precision and recall across nearly all classes. These results suggest that Naïve Bayes is more effective for predictive tasks involving sleep quality, while C4.5 remains highly valuable when the goal is to interpret variable interactions and decision rules. Overall, this research highlights the potential of data mining techniques in health informatics, particularly in improving the understanding and prediction of sleep quality, which in turn can contribute to better prevention and management of sleep-related health issues.

Hypertension is one of the most prevalent non-communicable diseases and a major risk factor for heart disease, stroke, and kidney disorders. The high prevalence of hypertension cases in the community, particularly in the working area of Puskesmas Kota Rantau Prapat, highlights the urgent need for more effective early detection efforts to prevent severe complications in the future. However, the limited capacity of healthcare workers in utilizing data analysis technologies has resulted in hypertension risk detection being dominated by conventional methods, which are often less accurate and inefficient. To address this issue, this community service program was conducted through training on the application of the Random Forest algorithm to analyze patients’ medical history data in order to detect hypertension risks. The training method included an introduction to the fundamentals of machine learning, data pre-processing stages, implementation of the Random Forest algorithm, and interpretation of prediction results. The outcomes of the program demonstrated that healthcare workers were able to understand the use of data analysis technologies to support more accurate early detection of hypertension. Furthermore, the participants gained practical skills in utilizing medical datasets to produce predictions that can serve as a decision-support tool for preventive medical actions.Thus, this training contributed to enhancing the capacity of community healthcare workers in integrating machine learning-based technologies into preventive healthcare services. This program is expected to serve as an initial step toward developing more effective, efficient, and sustainable data-driven health systems.

Typhoid fever is an infectious disease caused by the bacterium Salmonella typhi, commonly found in developing countries, including Indonesia. Prompt and accurate treatment is crucial to prevent serious complications in patients. One way to assist in diagnosing typhoid fever is by applying machine learning methods to classify patient data. The Naive Bayes method is one of the machine learning algorithms frequently used in medical data classification due to its strong ability to handle large and complex datasets. This article discusses the application of the Naive Bayes method for classifying typhoid patient data at Rantauprapat General Hospital (RSUD Rantauprapat). By utilizing medical data that includes clinical symptoms, laboratory test results, and patients’ medical histories, the Naive Bayes model can provide fairly accurate predictions regarding the likelihood of a person having typhoid fever. The research findings indicate that Naive Bayes is reliable in predicting typhoid diagnoses with adequate accuracy, thereby supporting healthcare professionals in making faster and more precise decisions. It is expected that the implementation of this method can accelerate the diagnostic process and improve the quality of healthcare services at RSUD Rantauprapat, as well as in other regions.

This study aims to analyze the bankruptcy prediction of PT Matahari Department Store Tbk using the Zmijewski method. The Zmijewski method, developed in 1984, is one of the most widely used approaches to predict corporate financial distress through the use of financial ratios. The study covers the period from 2019 to 2023 and applies a quantitative research design. The data used in this study are secondary data obtained from the company’s financial reports. Data collection techniques include documentation and literature study, while the data analysis technique applied is the Zmijewski model, which employs three main ratios: return on assets (X1), debt to assets ratio (X2), and current ratio (X3). The results show that in 2019, 2021, and 2022, the X values were -1.92, -0.29, and -0.25, respectively, indicating that PT Matahari Department Store was not predicted to face potential bankruptcy, as the values were below 0. However, in 2020 and 2023, the X values were 1.51 and 0.85, respectively, suggesting that the company had the potential to go bankrupt, as the results exceeded 0. These findings highlight the financial fluctuations experienced by PT Matahari Department Store during the study period, emphasizing the importance of continuous financial performance evaluation and the use of bankruptcy prediction models as an early warning tool for stakeholders and decision makers.

This study aims to analyze the prediction of financial distress using the Altman Z-Score, Springate, and Zmijewski methods at PT Sri Rejeki Isman Tbk in 2019-2023. This type of research is descriptive research with a quantitative approach. Using secondary data with documentation techniques and literature studies in the form of related company financial reports, books, articles, journals and other publications related to the research topic. The sampling technique was carried out using a purposive sampling method. The sample in this study was obtained using a purposive sampling technique and obtained as many as 5 financial reports from the company PT Sri Rejeki Isman Tbk for the period 2019-2023. The results of the study show that the results of calculations using the Altman Z-Score method indicate that in 2019-2023 PT Sri Rejeki Isman Tbk experienced fluctuations in the company consistently still in the category of bankruptcy, the Springate method shows that the company experienced a decline in its financial performance, and the Zmijewski method shows that companies that experience fluctuations in financial performance conditions, Although there are fluctuations in the X-Score value and improvements in certain years.

This study proposes the integration of a Hybrid Recommendation method (combining Content-Based and Collaborative Filtering) with Random Forest Regression (RFR) to improve the accuracy of stay duration prediction in web-based boarding house booking systems. The main issue in online boarding booking systems is the inaccuracy of predicting user stay duration, affecting room allocation efficiency and customer satisfaction. The dataset was sourced from the hotel sector due to its attribute similarities and data validity. The research process includes data preprocessing (missing value imputation, normalization, and one-hot encoding), temporal and contextual feature engineering, hybrid recommendation system construction with CBF and CF score weighting, and RFR model training optimized through Grid Search and 10-fold cross-validation. Evaluation was conducted using MAE, RMSE, R² metrics, as well as recommendation metrics such as Precision@5, Recall@5, and Mean Reciprocal Rank (MRR). Results show that this integrated model achieved an R² of 0.7239 and an MAE of 1.0537 days, as well as a Precision@5 of 0.9636. This integration proves effective in improving prediction accuracy and recommendation relevance and contributes to the development of AI-based intelligent systems in the accommodation domain.

Indonesia is the world's leading producer of spices, but it still faces challenges in manual visual quality assessment, which is inconsistent. This study aims to develop a spice quality classification system using a Deep Learning approach based on Convolutional Neural Networks (CNN). Data was collected through digital images of five types of spices (cloves, cardamom, cinnamon, pepper, and nutmeg) classified into two categories: good and bad. The dataset was then processed and used to Train the CNN model using Tensorflow. The model architecture consists of several convolution, pooling, and dense layers, and is integrated into a web-based prototype application using Streamlit. Evaluation results show that the model achieves high Accuracy of 98.86% (Training), 98.45% (Validation), and 98.45% (Testing). The prototype application can provide automatic Predictions of spice quality through a simple and responsive interface. The results of this study indicate that CNN is effective in identifying the visual quality of spices and can serve as an objective, efficient technological solution that supports the enhancement of Indonesia's spice export competitiveness.

Human Resource Management (HRM) plays a strategic role in improving organizational competitiveness through proper management of employee placement, training, and performance evaluation. To support the achievement of these goals, a predictive model is needed that can provide an accurate picture of employee performance. This study utilizes a Human Resource Management (HRM) dataset of 1,200 data and applies several classification algorithms to compare their effectiveness, namely J48 or C4.5, Random Forest, Naive Bayes, K-Nearest Neighbor (KNN), Logistic Regression, and Support Vector Machine (SVM). To obtain more optimal results, this study uses resampling techniques and attribute selection methods with a correlation attribute eval approach, so that class distribution can be more balanced and model accuracy increases. From the test results, the Decision Tree J48 algorithm showed the best performance with an accuracy level reaching 95.41%, a kappa value of 0.8925, a mean absolute error (MAE) of 0.0432, a precision of 0.955, a recall of 0.954, and an area under the ROC curve of 0.964. These findings indicate that J48 has excellent predictive capabilities compared to other algorithms. Furthermore, this study also found that the most influential variables in determining employee performance include the percentage of the last salary increase (EmpLast Salary Hike Percent), the level of work environment satisfaction (Emp Environment Satisfaction), the length of time since the last promotion (Years Since Last Promotion), and experience in the current role (Experience Years in Current Role). Overall, the results of the study indicate that the C4.5 algorithm with the application of the resampling technique can be an optimal solution in building an employee performance prediction system. Thus, this model has the potential to be a strong basis for managerial decision-making, particularly in designing HR development strategies and policies to improve organizational performance.

Warfarin (commonly known by its trade name, Coumadin) is an oral anticoagulant that has been widely used for the prevention and treatment of thromboembolic disorders. Despite its clinical benefits, warfarin therapy is complicated by a very narrow therapeutic index and wide inter-individual variability in dose requirements. This variability represents a major challenge for clinicians, as inappropriate dosing may lead to serious adverse outcomes such as bleeding or thrombotic events. A growing body of evidence suggests that genetic polymorphisms are among the most important factors contributing to this variability, particularly those involving the Vitamin K Epoxide Reductase Complex Subunit 1 (VKORC1) gene. VKORC1 encodes a key enzyme that functions as a bottleneck in the vitamin K cycle, playing an essential role in the regeneration of reduced vitamin K (VKH). This active form of vitamin K is required for the γ-carboxylation of vitamin K–dependent clotting factors, including prothrombin and other coagulation proteins. Polymorphisms within VKORC1 can significantly alter the enzyme’s expression and activity, thereby modifying an individual’s sensitivity to warfarin. One of the most clinically relevant variants is the -1639G>A (rs9923231) polymorphism, which reduces VKORC1 transcription and subsequently decreases enzyme activity. Patients carrying the A allele often exhibit increased sensitivity to warfarin and therefore require lower maintenance doses compared to those with the G allele. Understanding these genetic influences not only improves our knowledge of warfarin pharmacogenomics but also highlights the importance of personalized medicine in anticoagulant therapy. Incorporating VKORC1 genotyping into clinical practice could optimize dose prediction, minimize adverse events, and enhance the safety and effectiveness of warfarin therapy. This narrative review aims to provide an in-depth discussion of the complex role of VKORC1 in vitamin K metabolism and its impact on warfarin sensitivity, thereby underscoring the critical relevance of genetic factors in guiding individualized anticoagulation therapy.