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Early detection of a potential heart attack is a crucial step in preventing sudden death from heart disease. This research aims to develop an Internet of Things (IoT)-based health monitoring system capable of measuring vital body data in real time and predicting the likelihood of a heart attack from CSV data obtained from sensors, integrated through RapidMiner as learning data using a machine learning algorithm, the Support Vector Machine (SVM). The system was built using an ESP32 microcontroller connected to a MAX30102 sensor to measure heart rate and finger oxygen levels (SpO₂), as well as a DHT22 sensor to measure temperature and humidity. The resulting data is sent to the Blynk application to display real-time data according to its parameters. The initial prediction logic was developed using a rule-based method based on medical thresholds for four vital parameters. The data was then used to train an SVM model as a classification system to detect potential heart attacks. Test results showed that the system can identify abnormal conditions with a good level of accuracy and provide early warnings based on changes in vital parameters in real time. This system is expected to be an initial solution for personal health monitoring, especially for individuals at risk of heart disease. It can be further developed with cloud integration and automatic notifications to users' devices.

Blasting is a critical method for overburden removal in open-pit coal mines, where fragmentation quality directly impacts loading efficiency and operational costs. This study aims to analyze the actual fragmentation resulting from overburden blasting at Pit 4 Middle of PT. Victor Dua Tiga Mega, Central Kalimantan, to predict fragmentation using the Kuz-Ram model, and to evaluate the conformity of both results against the company standard (boulder size ≤144.6 cm or ≥50 cm for analysis). The research employed a quantitative comparative method. Primary data included blasting geometry and photographs of muck piles from 10 blasting events, which were analyzed using WipFrag software to obtain actual fragmentation distribution. Secondary data comprised rock characteristics and explosive properties for Kuz-Ram prediction input. The results showed significant variation in actual boulder percentage (≥50 cm), ranging from 6.19% to 32.91% with an average of 16.05% (medium category). Statistical analysis revealed a very weak negative correlation (r = -0.21) between powder factor (PF) and boulder percentage, indicating that PF is not the dominant factor within the consistent application range (0.21-0.23 kg/bcm). Comparison with Kuz-Ram predictions showed that the model consistently over-predicted coarse material, with an average difference of +25.21%, suggesting the need for rock factor (A) recalibration. It is concluded that the blasting results are inconsistent, strongly influenced by uncontrollable factors such as geological conditions. Recommendations include geometri evaluation, particularly burden and spacing, and calibration of the Kuz-Ram model for more accurate future predictions.

This study examines customer churn prediction in subscription-based telecommunications from a digital marketing perspective using machine learning. The analysis utilizes a secondary dataset of 7,043 customer records that simulate behavioral, contractual, and financial attributes commonly found in telecom services. Three classification algorithms Logistic Regression, Random Forest, and Gradient Boosting are applied to model churn behavior. Data preprocessing includes handling missing values, encoding categorical variables, and splitting data into training and testing sets. Model performance is evaluated using accuracy, recall, and ROC-AUC, with emphasis on recall due to its importance in identifying at-risk customers. The results show that Gradient Boosting achieves the highest overall performance with an ROC-AUC of 0.84, while Logistic Regression provides relatively higher recall. Key drivers of churn include short-term contracts, higher monthly charges, and lower service engagement. However, recall remains moderate, indicating limitations in capturing complex behavioral factors. These findings suggest the need to combine predictive models with behavioral insights and highlight the importance of early customer engagement and long-term retention strategies.

Predictive maintenance (PdM) plays a crucial role in modern industrial systems by minimizing downtime, reducing maintenance costs, and optimizing asset performance. However, many predictive models operate as “black box” systems, limiting transparency and making it difficult for operators to interpret their outputs. This study aims to integrate Explainable Artificial Intelligence (XAI) techniques with Remaining Useful Life (RUL) prediction models to improve both accuracy and interpretability. Various machine learning and deep learning approaches, including Support Vector Machines (SVM), Random Forest (RF), XGBoost, Long Short-Term Memory (LSTM), and Convolutional Neural Networks (CNN), are employed to predict RUL using real-time sensor data from rotating machinery. XAI methods such as SHAP, LIME, and attention mechanisms are applied to provide human-understandable explanations of model predictions. The models are evaluated based on accuracy, Root Mean Square Error (RMSE), and interpretability scores. The results show that XAI-enhanced models outperform traditional approaches in predictive performance while offering greater transparency. These explanations help maintenance engineers better understand the factors influencing predictions, thereby improving decision-making and trust in the system. Nevertheless, the integration of XAI introduces additional computational complexity, which may pose challenges for large-scale industrial implementation. Overall, this study highlights the potential of combining XAI with RUL prediction to develop more reliable, transparent, and effective predictive maintenance solutions.

Introduction: Additive Manufacturing (AM) has revolutionized the production of complex geometries, offering flexibility, customization, and precision across various industries. However, optimizing multiple process parameters simultaneously to enhance AM performance remains a significant challenge. This study focuses on improving both mechanical properties and surface quality by utilizing multi-objective optimization techniques. Literature Review: The research reviews existing approaches in AM optimization, highlighting the limitations of single-objective optimization and the potential of multi-objective evolutionary algorithms (MOEAs). Previous studies demonstrate the difficulty of balancing competing objectives, such as tensile strength and surface roughness, within AM processes. Materials and Method: This study employs NSGA-II, MOEA/D, and SPEA2 algorithms to optimize AM parameters like layer thickness, build orientation, and infill density. The optimization aims to improve mechanical performance, including tensile strength and impact resistance, while reducing build time and surface roughness. The methodology integrates experimental validation with computational predictions to evaluate the effectiveness of these algorithms. Results and Discussion: The optimization process yielded Pareto-optimal solutions that balanced mechanical strength and surface quality. The results demonstrated improvements in tensile strength and surface finish without significantly increasing build time. Trade-off analysis highlighted the inherent conflicts between mechanical performance and surface quality, allowing for better decision-making in industrial applications. The study contributes to the AM industry by offering a comprehensive optimization framework for improving both efficiency and product quality.

This study aims to comprehensively evaluate the potential pharmacological activities and safety profiles of seven secondary metabolite compounds (Caffeic Acid, Syringic Acid, Quercetin, Luteolin, p-Coumaric Acid, Ferulic Acid, and Epicatechin) identified in the Bajakah plant (Spatholobus littoralis Hassk.). The research approach integrates in silico analysis using the PubChem database, biological activity prediction via PASS Online, oral toxicity assessment through ProTox-II, and pharmacokinetic evaluation using pkCSM, which were subsequently validated through an empirical literature review. The results indicate that these compounds exhibit significant activity probabilities, particularly as antimutagenic, antiseptic, and antioxidant agents. Luteolin demonstrated the highest antimutagenic potential, while Quercetin showed dominant antioxidant activity. Toxicity profiling revealed that Luteolin and Caffeic Acid possess the highest safety levels (Class 5), whereas Quercetin requires special attention (Class 3). These computational findings strongly correlate with empirical evidence demonstrating that Bajakah extract exhibits broad-spectrum antibacterial activity against Staphylococcus aureus, antifungal activity against Candida albicans, as well as high antioxidant and anti-inflammatory capacities. This study provides a strong molecular foundation for the development of Bajakah as a safe and effective phytopharmaceutical candidate.

This study aims to analyze the in silico profile of the compound orientin derived from the water hyacinth plant (Eichhornia crassipes) as a potential antioxidant candidate. Orientin was selected based on its chemical structure data registered in PubChem, which provides complete information regarding molecular identity, physicochemical properties, and 2D and 3D structural representations. The prediction of biological activity was conducted using PASS Online, which indicated that orientin possesses a promising likelihood of exhibiting antioxidant activity according to relevant probability values. Furthermore, the safety assessment of the compound was carried out through ProTox-II to identify potential toxicity, including toxicity class, possible hepatotoxic effects, and other predicted safety parameters. To determine its pharmacokinetic profile, pkCSM was employed to predict ADMET characteristics such as absorption, distribution, metabolism, excretion, and additional toxicity risks. The results of these analyses show that orientin demonstrates favorable potential as an antioxidant candidate, supported by predicted pharmacological properties and relatively low toxicity levels according to in silico evaluations. Therefore, orientin has promising potential for further development in subsequent in vitro and in vivo studies.

This study aims to analyze the phytochemical profile of Sambung Nyawa (Gynura procumbens) leaves as a potential herbal candidate for mild hypertension therapy using in silico methods. Plant samples were examined to identify active compounds documented in the PubChem database. The identified compounds were further analyzed using PASS Online to predict their pharmacological activities, ProTox-II to evaluate toxicity levels, and pkCSM to assess ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) characteristics. The findings reveal that several bioactive compounds present in Sambung Nyawa leaves demonstrate strong predicted anti-hypertensive activity accompanied by minimal toxicological risk. PASS Online analysis indicates potential mechanisms of action, including vascular receptor modulation and mild diuretic properties that may support blood pressure regulation. ProTox-II classification places most compounds in the low-toxicity category, while pkCSM predictions confirm acceptable bioavailability and favorable pharmacokinetic properties. Overall, these results provide a preliminary scientific foundation for the development of Gynura procumbens as an alternative herbal therapy for mild hypertension and support the need for further validation through in vitro and in vivo experimental studies.

This study aims to analyze the fluctuations in chili production in Kabanjahe District, Karo Regency, which affect market price instability and uncertain supply. One approach applied in this study is the Single Exponential Smoothing (SES) method to forecast chili production. SES was chosen for its simplicity, ease of implementation, and its ability to generate accurate predictions even when the data lacks significant seasonal patterns. The data used is secondary data on chili production obtained from official publications by the Karo Regency BPS for the period of 2020–2024. The analysis results show that a smoothing parameter (α) of 0.8 produced the lowest Mean Absolute Percentage Error (MAPE) of 3.08%. These findings indicate that applying a higher α makes the model more responsive to recent data changes, thus yielding more accurate forecasts. This study demonstrates the effectiveness of the SES method in forecasting chili production in areas with significant seasonal fluctuations.

This study aims to analyze the potential of eupatorin found in Cat’s Whiskers (Orthosiphon stamineus) as an anticancer agent using in silico methods. Eupatorin was selected due to its promising biological activity reported in previous literature. The research employed compound structure data registered in PubChem, analyzed through PASS Online for pharmacological activity prediction, ProTox-II for toxicity evaluation, and pkCSM for ADMET parameters. The results indicated that eupatorin has a high probability of inhibiting cancer cell proliferation with significant pharmacological activity values. Toxicity predictions showed a safe profile with an LD50 supporting its potential therapeutic use. ADMET analysis demonstrated that eupatorin possesses good bioavailability, adequate absorption and distribution, and minimal metabolic interactions, supporting its efficacy as an anticancer compound. These findings suggest that eupatorin could be an important candidate for herbalbased drug development, particularly for cancer therapy, and provide opportunities for further research through in vitro and in vivo experiments to validate anticancer activity comprehensively.

High-dimensional clinical data exhibit complex and non-linear relationships among patient attributes, where outcomes are often influenced by feature interactions rather than isolated variables. However, many existing machine learning models prioritize predictive performance while providing limited interpretability and insufficient insight into interaction structures. This study aims to address this limitation by developing an interpretable and robust framework for feature interaction mining in clinical data. We propose a hybrid tree–neural modeling framework that explicitly captures and ranks feature interactions while maintaining stable predictive performance. Tree-based ensemble models are employed to identify non-linear interaction patterns, while neural representations enhance learning flexibility and generalization. The framework integrates interaction importance analysis, cross-validation–based stability assessment, and evaluation across multiple data splits to ensure robustness and interpretability. Experiments conducted on a real-world high-dimensional clinical dataset demonstrate that the proposed approach achieves consistent predictive performance, with AUC values ranging from 0.628 to 0.641 across five cross-validation folds (mean AUC ≈ 0.633). Performance remains stable under varying train–test splits, indicating strong generalizability. Interaction analysis reveals that a small number of dominant feature interactions—such as age combined with length of hospital stay and medication count combined with diagnostic information—consistently contribute to model predictions, appearing in over 80% of validation folds. Ablation studies further confirm that removing interaction-aware components leads to noticeable performance degradation, highlighting their importance.  In conclusion, this study demonstrates that explicit feature interaction modeling enhances interpretability, stability, and generalization in clinical prediction tasks. The proposed hybrid framework provides a reliable foundation for developing trustworthy and transparent clinical decision-support systems

This research benchmarks multiple machine learning (ML) algorithms for large-scale loan default prediction using a real-world dataset of 255,000 borrower records, where default cases represent only ~9–12% of total observations. The study addresses the persistent gap in comparative analyses of ML models that balance predictive accuracy, interpretability, and computational efficiency for credit risk assessment. Six algorithmic families were evaluated Logistic Regression, Random Forest, XGBoost, LightGBM, CatBoost, Artificial Neural Networks (ANN), and Stacked Ensemble—using standardized preprocessing, hybrid imbalance handling (SMOTE, class weighting, under-sampling), and comprehensive evaluation metrics (AUC, F1, Recall, Precision, PR-AUC, and Brier Score). Empirical results show Logistic Regression achieved the highest AUC of 0.732, outperforming nonlinear models under the baseline configuration, while LightGBM attained perfect recall (1.0) but low precision (0.116), indicating over-prediction of defaults. Gradient boosting models demonstrated robust calibration (Brier ≈ 0.114–0.116) and the best computational efficiency, with LightGBM showing the fastest training and lowest memory use. CatBoost exhibited strong recall but the slowest computation, and ANN underperformed on tabular data (AUC ≈ 0.56). The Stacked Ensemble delivered balanced results with AUC = 0.664 and improved overall stability. These findings confirm that boosting-based models, particularly LightGBM and CatBoost, offer superior scalability and calibration, whereas Logistic Regression remains a valuable interpretable baseline. The study concludes that effective default prediction requires integrating rebalancing, calibration, and threshold optimization to enhance recall and operational deployment reliability in large-scale credit ecosystems.

One of the risk impacts of blasting activities is flyrock. The impact of flyrock can be minimized by evaluate the factors that influence flyrock such as blasting geometry. Flyrock cannot be completely eliminated but flyrock distance can be reduced to prevent damage. This study aims to determine the actual maximum flyrock distance in the field and the factors that influence the flyrock distance. This study was conducted at PT. Sims Jaya Kaltim, Paser Regency, East Kalimantan Province. This research was conducted 31 times and the average throwing distance was 79.8 meters, the actual maximum flyrock throwing distance was 134.3 meters and the minimum throw was 40.5 meters. In the flyrock throw prediction, the Richard & Moore calculation method was used with a face burst mechanism of 121.3 meters and cratering of 232.2 meters and the Ebrahim Ghasemi dimensional analysis method of 104.5 meters. From both methods, the Ebrahim Ghasemi method was found to be closest to the actual flyrock with a standard deviation of 29.49 meters and an error percentage of 2.90%. From the results of the correlation between the blasting parameters and the actual flyrock, it was found that the factors that influence the occurrence of flyrock are powder factors, so an analysis was carried out to obtain a maximum flyrock throwing distance of 90 meters so that the safe radius of the tool is 180 meters, then the maximum powder factor used is 0.14 kg/m3.

Improper water management in rice cultivation can lead to water stress, which reduces productivity. Conventional monitoring has limitations on large-scale lands, necessitating more efficient remote sensing technologies. This study aims to develop a water stress identification system for rice plants in the late vegetative phase using multispectral drone imagery integrated with an Artificial neural network (ANN). The research method employs an experimental approach with six water availability levels in Karyamukti Village, Sumedang. Field reference data were obtained through soil moisture sensors converted into Available Water (AW) values. Image processing stages included orthomosaic reconstruction, leaf object segmentation, and transformation of vegetation indices (NDVI, NDRE, GNDVI, etc.) as model inputs. The results show that the ANN model with a four-hidden-layer architecture achieved training and validation accuracies of 94–95%. In the independent testing phase, the model produced an accuracy of 94.60% with an F1-Score of 93.33%. Spatial visualization of the prediction results indicates a consistent water condition distribution across rice plots. In conclusion, the integration of multispectral drones and ANN provides an accurate non-destructive solution for spatial monitoring of water availability in rice plants.

This research develops a machine learning model to classify customer loyalty using the Random Forest algorithm. Customer churn is a critical issue that reduces revenue and increases acquisition costs. A dataset of 50,000 customers from global e-commerce and subscription platforms was processed through data cleaning, imputation, outlier handling, and class balancing with SMOTE. The Random Forest model was built as a baseline and optimized with hyperparameter tuning. Evaluation using accuracy, precision, recall, and F1-score shows that the optimized model achieved 90.81% accuracy and 83.87% F1-score, outperforming previous Naïve Bayes approaches. Feature importance analysis highlights customer service interactions, lifetime value, and demographic factors as key predictors of churn. These findings demonstrate Random Forest’s effectiveness in churn prediction and provide practical insights for customer retention strategies

Customer churn is a strategic challenge for digital streaming platforms because it directly Impacts revenue and business sustainability. This study aims to analyze the factors influencing customer Churn and develop a churn prediction model using the Random Forest algorithm. The study uses a Quantitative approach with an explanatory design and utilizes secondary data from the Netflix Customer Churn and Engagement Dataset available on Kaggle. The dataset consists of 1,000 customer data with 16 Variables covering demographic characteristics, service usage behavior, financial condition, and customer Satisfaction level. The data was processed through preprocessing, one-hot encoding, and a 70:30 split Between training and test data. Model performance was evaluated using accuracy, precision, recall, F1 Score, and ROC-AUC metrics. The results show that the Random Forest model produces an accuracy of 53.7%, precision of 56.3%, recall of 63.6%, F1-score of 59.7%, and ROC-AUC of 0.534, indicating Moderate predictive ability and only slightly better than random classification. Feature importanceAn.evealed that user engagement levels, such as viewing duration and frequency of interactions, Were the most dominant factors influencing churn, followed by economic factors and customer satisfaction. The results of this study are expected to provide a basis for streaming platforms to design more effective Customer retention strategies.

This study examines the integration of technology in the process of Human Resource (HR) transformation through the perspective of employee data analytics as a strategic approach to modern HR management. The primary focus of the study is to analyze the impact of the simultaneous integration of digital HR systems and organizational digital transformation on improving the efficiency of HR functions, with organizational agility positioned as a moderating variable that strengthens this relationship. In addition, the study explores the potential optimization of Artificial Intelligence (AI) technologies and predictive analytics methods, such as Bayesian Optimization, in predicting workforce dynamics, including employee attrition risk and competency development needs, while also bridging the analytical skills gap among HR practitioners. The research method employed is a systematic literature review of relevant scientific publications from 2021 to 2025, selecting sources that address digital HR transformation, HR analytics, and the application of AI in organizational contexts. The findings indicate that digital HR systems have a strong and significant effect on enhancing operational efficiency and the quality of HR decision-making, and this effect becomes more optimal when supported by a high level of organizational agility. Furthermore, AI and predictive analytics are proven to generate more accurate predictions and simplify technical complexity, making them easier for HR practitioners to adopt. This study concludes that the success of HR transformation requires a holistic approach that aligns the use of advanced technologies with organizational capabilities, human resource readiness, and ethical considerations to create sustainable organizational value.

Indonesia, as a country with the highest seismicity in the world, requires an accurate earthquake prediction system through the use of the BMKG earthquake catalogue. This research aims to implement ETL-based data pipeline engineering to process 92,887 earthquake catalog entries for the 2008-2023 period into ready-to-use daily time series for the LSTM seismicity forecasting model. The ETL process includes raw data extraction, cleaning of 97% missing values columns on focal mechanism parameters, datetime conversion, daily resampling producing 5,200 entries with earthquake count, total magnitude, and average magnitude features, as well as Min-Max Scaler normalization for LSTM compatibility. The dataset was processed using Google Colab with a stacked LSTM architecture of two layers of 50 and 25 units, dropout 0.2, Adam optimizer, and a sequence window of 30 days to predict the daily earthquake count. The model trained for 100 epochs shows the ability to capture stable seismic activity trends with a consistent decrease in MSE loss, although it shows deviations in extreme spikes due to aftershock sequences. The ETL pipeline proved crucial in ensuring temporal consistency, 100% data completeness, and relevant physics representation, resulting in a reproducible end-to-end framework for disaster mitigation.

Stock price prediction remains a complex challenge due to the dynamic and non-linear nature of financial markets, especially for banking stocks like PT Bank Negara Indonesia (Persero) Tbk (BBNI). This study aims to optimize BBNI stock price forecasting by integrating an automated Extract, Transform, Load (ETL) pipeline with the Long Short-Term Memory (LSTM) algorithm within a data engineering framework. Historical data from 2019 to 2025 were processed through a structured ETL sequence—including data cleaning, feature engineering, and MinMaxScaler normalization—to ensure high data quality. The dataset was partitioned into 80% for model training and 20% for testing to ensure rigorous evaluation. The results demonstrate that the systematic ETL approach significantly enhances model stability and predictive accuracy compared to conventional methods. The LSTM model effectively captured long-term temporal dependencies, providing reliable trend forecasts with an impressive test accuracy, achieving a Root Mean Squared Error (RMSE) of 0.0354. This research underscores that integrating robust data engineering practices with deep learning is essential for building resilient financial decision-support systems.

Rainfall is a crucial factor in the stability of the Earth's ecosystem and has a significant impact on agriculture, forestry, energy, and water management. However, increasingly unstable climate change makes rainfall patterns difficult to predict accurately using traditional methods. The city of Medan, the capital of North Sumatra Province, has a tropical rainforest climate with an average annual rainfall of approximately ±2200 mm and an average temperature of 27°C. Significant weather fluctuations in this area can trigger flooding when rainfall increases and cause water shortages when rainfall decreases (BMKG, 2021). Therefore, a prediction approach that can manage non-linear and dynamic data is needed. Artificial Neural Networks (ANN) are one of the reliable machine learning methods for detecting data patterns. By using the backpropagation algorithm, the model can gradually reduce prediction errors, making it widely used in weather forecasting applications. In this regard, this study uses ANN with the backpropagation method to forecast monthly rainfall in Medan City by utilizing data from 2022–2024 as training and testing data.