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The phenomenon of emotional healing has become increasingly prominent in popular culture as the younger generation experiences increasing loss, emotional exhaustion, and numbness. Popular music serves as a medium of expression, representing these emotional dynamics through poetic language and symbolic structures. This study aims to analyze the emotional representation of healing in Umay Shahab's song "Perayaan Mati Rasa" using Roland Barthes's semiotic approach. The research method used is descriptive qualitative, analyzing three layers of meaning: denotation, connotation, and myth, through breaking down the lyrics into several lexicons and reading signs. The results show that (1) denotatively, the lyrics construct an emotional arc from inner tension to self-contemplation; (2) connotatively, the use of diction and imagery represents vulnerability, resilience, and emotional reflection; and (3) at the mythical level, the song constructs release as a form of emotional maturity and loss as a process of identity formation. Thus, this song functions as a cultural representation in interpreting the process of emotional healing in popular culture.

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.

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.

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

This research investigates integrated legal-human resource frameworks for autonomous vessel operations in Indonesian archipelagic waters, addressing regulatory compliance gaps and seafarer workforce transition challenges. Through qualitative analysis involving 38 stakeholders including maritime lawyers, regulatory officials, ship operators, seafarer unions, training institutions, and autonomous technology developers, this study examines how existing maritime legal frameworks prove inadequate for unmanned operations while workforce displacement threatens 150,000+ Indonesian maritime workers. Results demonstrate that successful autonomous vessel adoption requires coordinated legal-HR approaches addressing liability allocation (achieving 75-85% clarity through multi-party frameworks), competency certification for remote operators (reducing training gaps by 60-70%), career transition pathways (enabling 55-65% workforce adaptation), and regulatory harmonization (improving compliance efficiency by 45-60%). Key barriers include UNCLOS Article 94 incompatibility, insurance unavailability, seafarer resistance, and jurisdictional fragmentation. Findings reveal that archipelagic contexts demand unique legal-HR solutions integrating traditional maritime rights, hybrid operational modes, and just transition principles. This research contributes frameworks enabling Indonesia to proactively shape autonomous vessel regulations protecting both technological innovation and maritime workforce interests during critical technology transition.

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.

The rapid development of digital technology has significantly increased public access to online activities, including online gambling, which has emerged as a serious social problem in Indonesia. This study aims to analyze the impact of online gambling on the mental health and social behavior of young people. This research employed a descriptive method with a survey approach. Data were collected through questionnaires distributed to young respondents who had experience or exposure to online gambling activities. The findings indicate that involvement in online gambling has a significant impact on mental health disorders such as stress, anxiety, and depressive symptoms. In addition, online gambling contributes to a decline in social interaction quality, increased family conflict, social withdrawal, and reduced productivity. Behavioral changes such as impulsivity and decreased self-control were also identified among respondents. These results demonstrate that online gambling not only affects individuals psychologically but also disrupts social relationships and family harmony. Therefore, comprehensive preventive efforts are needed through digital literacy enhancement, family involvement, and psychological support services to protect young people from the negative consequences of online gambling.

This study examines the development of a Crude Palm Oil (CPO) price forecasting model using an artificial neural network algorithm, specifically the backpropagation algorithm. As one of Indonesia’s main export commodities, CPO has a significant economic impact and influences the income of oil palm farmers. The CPO price data used in this study were obtained from CIF Rotterdam, covering the period from January 2019 to December 2023. The research methodology consists of several stages, including data collection, preprocessing, model design, and model implementation using Python programming. The training results of the backpropagation algorithm show an error value of 0.537829578 after 1,000 epochs, while the evaluation using Mean Squared Error (MSE) indicates an MSE of 0.022709 during the training process and 0.017604 during the testing process. The model also produces CPO price predictions for the next three months, namely 932.578 for the first month, 949.568 for the second month, and 774.855 for the third month. These findings indicate that the developed model is capable of predicting future CPO prices with adequate accuracy, which can assist companies in making better financial decisions and managing risks associated with CPO price fluctuations.

The retail industry generates an extremely large and continuously growing volume of transactional data along with the advancement of digital technology, thereby requiring sophisticated and systematic data analysis approaches to support effective and evidence-based business decision-making. This study aims to analyze retail sales data by utilizing the Retail Sales Dataset obtained from the Kaggle platform, which consists of 100,000 transaction records and broadly represents the characteristics of retail transactions. The main focus of this study is to classify product categories and predict customer segments, including the identification of high-spending customers (high spenders), based on demographic attributes such as age and gender, as well as various transaction-related features. The research methodology includes data preprocessing, label encoding, and feature engineering to generate additional variables, including Age_Group, Is_Holiday, and Spender_Group, which are expected to enhance the predictive capability of the models. Several machine learning algorithms, namely Decision Tree, Random Forest, and XGBoost, were implemented and evaluated to compare their respective performance. The experimental results indicate that multiclass product category classification achieves relatively low accuracy, ranging from 27% to 34%. These findings suggest the high complexity of retail data and highlight the need for further model optimization, class balancing techniques, and feature refinement to improve predictive performance in future studies.

The increasing number of Indonesian Migrant Workers (TKW) working abroad, particularly through programs organized by BP2MI, has become a significant concern in managing the labor export process. One of the challenges faced is the uncertainty of the number of TKW to be sent each year, which is influenced by various external and internal factors. Therefore, this study aims to apply artificial neural networks (ANN) with a backpropagation algorithm approach to predict the number of TKW that will be processed by BP2MI. This method was chosen due to its ability to recognize patterns and nonlinear relationships between variables that affect the decision-making process for TKW export. In this study, the data used includes factors such as the number of job seekers, government policies, and the condition of the international labor market. The artificial neural network with the backpropagation algorithm is used to train the model based on existing historical data, with the goal of generating accurate predictions regarding the number of TKW to be processed in the coming years. The results of the tests show that the developed model can provide fairly accurate predictions and can serve as a tool for BP2MI in planning and managing the export of TKW more effectively. With the application of this technology, it is expected that the decision-making process related to TKW export can become more efficient and well-predicted.

Gaya hidup remaja masa kini sangat erat kaitannya dengan penggunaan smartphone, di mana hampir seluruh remaja telah memiliki smartphone pribadi. Penggunaan smartphone yang tidak terkontrol berisiko menimbulkan kecanduan yang dapat berdampak negatif terhadap kualitas hidup remaja. Kondisi ini berdampak pada status gizi yang tidak normal pada remaja, diantaranya kekurangan berat badan (underweight) maupun kelebihan berat badan (overweight). Tujuan penelitian ini adalah mengetahui hubungan kecanduan smartphone terhadap Indeks Massa Tubuh. Desain penelitian ini cross sectional dengan sampel 242 remaja usia 15-18 tahun dan menggunaan teknik purposive sampling. Instrumen penelitian yang digunakan adalah Smartphone Addiction Scale-Short Version yang sudah diuji validitas dan reliabilitasnya dengan nilai cut off point kuesioner untuk laki-laki >31 dinyatakan kecanduan smartphone sedangkan untuk perempuan >33 dinyatakan kecanduan smartphone, selain itu dilakukan pengukuran IMT berdasarkan berat badan dan tinggi badan. Analisa data yang dilakukan menggunakan uji statistik Chi-Square dengan uji alternatif Fisher Extract melalui SPSS versi 25. Hasil yang diperoleh yaitu p-value (0,014 < 0,05) yang menunjukan terdapat hubungan antara kecanduan smartphone terhadap Indeks Massa Tubuh (IMT).

The dynamic fluctuations in stock prices present a major challenge for investors in making informed decisions. To anticipate such uncertainties, forecasting methods that can provide accurate predictions are required. This study compares two time series forecasting methods Autoregressive Integrated Moving Average (ARIMA) and Double Exponential Smoothing (Holt) in predicting the stock prices of PT Telkom Indonesia (TLKM). The dataset consists of monthly closing prices from January 2018 to December 2023. The performance of each model is evaluated using three error metrics: Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE). The results show that the ARIMA(1,1,1) model yields higher predictive accuracy than the Holt method, with MAE of 787.71, MSE of 771,844.2, and RMSE of 878.55. In contrast, the Holt method records a MAE of 837.19, MSE of 878,393.4, and RMSE of 937.23. These findings confirm that ARIMA is superior in capturing the complex patterns of stock price movements and is more effective in volatile market conditions such as the stock exchange.

Cloud-native systems are essential for modern software development, offering enhanced scalability, flexibility, and resilience through cloud computing environments. However, ensuring the reliability and performance of these systems presents a challenge due to their dynamic and distributed nature. Traditional testing methods, such as unit and integration testing, while valuable for detecting individual component defects and interactions, are insufficient for predicting failure rates in complex, cloud-native applications. This study explores the effectiveness of various testing techniques and quality metrics in predicting failure rates within scalable cloud-native systems. A comparative experimental study was conducted using three primary testing techniques: unit testing, integration testing, and chaos testing. The results indicate that chaos testing, when combined with advanced quality metrics such as migration rate and mismigration rate, significantly outperforms traditional methods in predicting failure rates and evaluating system resilience. These findings suggest that chaos testing offers a more comprehensive evaluation, simulating real-world disruptions to test system behavior under stress, which is essential for cloud-native environments where high availability and fault tolerance are critical. The study also highlights the importance of integrating predictive quality metrics, which improve the accuracy of failure predictions and enhance system reliability. The study concludes that for cloud-native systems, a combination of advanced testing techniques and predictive metrics is essential for ensuring high availability, scalability, and reliability in dynamic environments. Future research should focus on refining predictive testing approaches, developing standardized frameworks, and empirically validating new testing methods to address the growing complexity of cloud-native systems.

Concept drift, the phenomenon where the statistical properties of data streams change over time, poses a significant challenge in machine learning, particularly for long term data streams. Traditional machine learning models, including batch learning and non-adaptive approaches, struggle to detect and adapt to these changes, leading to degraded performance and inaccurate predictions. This study proposes an adaptive computational model designed to detect and respond to concept drift using incremental learning techniques and statistical drift detection mechanisms. The model integrates an Adaptive Drift Detector (ADD) and Incremental Learning System, enabling real-time adjustments to data distribution changes. The model is evaluated across synthetic and real-world datasets, demonstrating its superior ability to detect abrupt, gradual, and recurring drifts compared to traditional models. Experimental results indicate that the adaptive model maintains high prediction accuracy, minimizes false positive rates, and reduces detection delays. Furthermore, the model performs well in resource-constrained environments, making it suitable for real-time applications such as healthcare prediction, fault detection, and IoT systems. Despite its promising performance, the study identifies challenges related to computational complexity and the model’s performance with imbalanced datasets and noisy data. Future research should focus on optimizing the model’s scalability, computational efficiency, and adaptability to more complex data types to ensure broader applicability in dynamic environments. This work contributes to advancing the detection and adaptation of concept drift, offering a robust solution for dynamic and evolving data streams.

Explainable Artificial Intelligence (XAI) has become a critical area of research within artificial intelligence, focusing on improving the transparency and interpretability of machine learning (ML) models, often referred to as "black-box" models. The need for XAI techniques arises from the inherent complexity of ML models, which can make their decision-making processes difficult for users to understand. This study investigates various XAI techniques, including LIME (Local Interpretable Model-agnostic Explanations) and SHAP (SHapley Additive exPlanations), to assess their impact on model interpretability without significantly compromising predictive performance. A comparative experimental design was used, applying these XAI methods to different ML models, including deep neural networks and ensemble methods, within large-scale enterprise data analytics systems. The results indicate that XAI methods significantly enhance model transparency and decision traceability, allowing users to understand the influence of individual features on predictions. While a slight reduction in predictive accuracy was observed, especially with simpler models, the trade-off between interpretability and performance was deemed acceptable, particularly in fields requiring transparency, such as healthcare, finance, and autonomous systems. The use of XAI in enterprise data systems has practical implications for fostering trust and enabling informed decision-making among stakeholders. Furthermore, the study discusses the challenges and limitations of applying XAI techniques, such as complexity, scalability, and model-specific limitations. Future research is suggested to focus on developing more scalable and efficient XAI methods, enhancing their applicability across various model types, and addressing the challenges of real-time applications. This will be crucial in ensuring the widespread adoption of XAI in critical domains, promoting the ethical use of AI while maintaining predictive accuracy.

This study investigates the role of Information Technology (IT) governance in enhancing risk management performance and ensuring regulatory compliance within multinational digital enterprises. As digital transformation continues to reshape the global business landscape, organizations face increasing challenges in managing technological risks and complying with complex regulatory requirements across various jurisdictions. The study adopts a quantitative approach, using a survey methodology to collect data from senior IT and compliance managers in multinational digital enterprises. The survey focuses on how IT governance frameworks, such as COBIT 2019 and ISO 27000, are utilized to align IT strategies with business objectives, mitigate risks, and maintain regulatory compliance. The findings indicate that organizations with well-established IT governance structures are better positioned to proactively identify and mitigate risks, ensuring greater consistency in meeting regulatory requirements. These organizations demonstrate improved risk management effectiveness, especially concerning cybersecurity, data privacy, and compliance with global regulations like GDPR. In contrast, organizations with ad hoc or decentralized governance structures struggle with fragmented risk management and compliance efforts. The study further highlights the importance of integrating IT governance frameworks with internal audit functions, specifically the Chief Audit Executive (CAE), to enhance cybersecurity resilience and ensure compliance with global standards. This research contributes to the literature by providing empirical evidence on the integration of IT governance, risk management, and regulatory compliance in multinational enterprises. It also highlights the need for a structured and systematic approach to IT governance to improve organizational performance in managing risks and ensuring consistent regulatory adherence. The study offers practical insights for organizations looking to optimize their IT governance structures in the face of rapid digital transformation.

The growing use of smartphones among adolescents carries a high risk of addiction, which can negatively impact physical, mental, and social well-being. To address this issue, a community service program was implemented to conduct early detection of gadget addiction among high school students and provide educational health media to raise knowledge and awareness about excessive smartphone use. The program involved 240 students of SMA Negeri 1 Tuntang and was conducted in three stages: screening, development of educational materials, and health counseling. Screening used the short version of the Smartphone Addiction Scale and revealed that 90% of students were classified as addicted. Prior to counseling, 81.25% of students had low levels of knowledge regarding healthy gadget use, which significantly improved after the intervention, with 83.4% achieving high knowledge levels. Health counseling proved crucial in enhancing students’ understanding and promoting responsible smartphone habits. The program highlights the importance of collaborative efforts by schools, parents, and students to limit smartphone use, encourage physical activity, and foster face-to-face social interactions to support adolescent well-being.