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This study develops an interpretable machine learning model to predict the settlement status of Hajj fees in Jambi Province, Indonesia. Utilizing the XGBoost algorithm on a dataset of 4,332 prospective pilgrims from 2025, the research addresses the critical challenge of class imbalance where only 28.5% of samples are labeled "Unsettled". The baseline XGBoost model achieved a ROC-AUC of 0.7778, with a recall of 0.3482 for the minority class. SHAP (SHapley Additive exPlanations) analysis was employed to interpret model predictions, revealing that financial features specifically NILAI_VA (Virtual Account Value), JML_SETORAN (Deposit Amount), and JML_PELUNASAN (Settlement Amount) are the most significant factors influencing repayment risk, with negative SHAP values indicating increased default probability. The findings demonstrate that an interpretable XGBoost framework can provide both predictive accuracy and actionable insights for policymakers, enabling targeted interventions such as flexible payment schemes and enhanced financial monitoring for high-risk pilgrims..

Hypertension is a major global health risk that requires accurate early detection, yet conventional methods struggle with complex and imbalanced health datasets. This study aims to optimize hypertension prediction using a Logistic Regression model integrated with Borderline-SMOTE to enhance recall and provide model transparency through SHAP (Shapley Additive Explanations). The method utilizes the BRFSS dataset, applying Borderline-SMOTE to address class imbalance at the decision boundary and XAI techniques for global and local interpretation. The findings show that the model achieved an accuracy of 0.719, an AUC of 0.800, and a significantly improved recall of 0.756. SHAP analysis identified age, high cholesterol, and BMI as the most influential risk factors, while waterfall plots successfully clarified individual risk extremes, ranging from 1.72% to 99.43% probability. These results imply that the proposed approach provides a sensitive and transparent screening tool for public health practitioners, effectively balancing statistical efficiency with clinical accountability.

This study aims to analyze and project consumer prices of cabbage commodities at four levels: Ngawi Regency, Pacitan Regency, East Java Province, and nationally, using the additive Holt–Winters forecasting model. Monthly price data for the period January 2020–December 2024 were used to capture the dynamics of levels, trends, and seasonal patterns that affect price fluctuations. Model performance was evaluated using the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) indicators. The results showed differences in model accuracy between regions. East Java Province produced the best performance with the lowest MAE and RMSE values, indicating a more stable price pattern that was easier for the model to capture. In contrast, Ngawi Regency showed the highest volatility, resulting in greater forecasting errors. Pacitan Regency displayed a relatively consistent seasonal pattern with moderate accuracy, while national data showed smoother fluctuations due to the aggregation effect. Overall, the additive Holt–Winters model is effective for short-term projections in regions with low to moderate variability, but is less optimal in regions with highly volatile price dynamics.

This study aims to examine the use of cattle waste as organic fertilizer to minimize agricultural operational costs in Mrawan Village, Tapen District, Bondowoso Regency. Cattle waste, particularly manure, is an abundant local resource that has not been optimally utilized by the local community. The majority of farmers in the village still rely on chemical fertilizers, which are relatively expensive and have a negative impact on long-term soil health. Therefore, this program is designed to provide a sustainable alternative solution through an educational approach and community empowerment. The methods used in this activity include outreach, technical training, and direct assistance in the process of making organic fertilizer from cow manure. Education focuses on simple fermentation techniques, the composition of natural additives, and appropriate fertilizer application methods. Farmers are actively involved in every stage of the activity, so they become not only beneficiaries but also agents of change in environmentally friendly agricultural practices. The results of the activity indicate that the use of organic fertilizer from cattle waste can reduce the cost of purchasing chemical fertilizers by up to 40% in a single planting season. In addition, organic fertilizer has been shown to increase soil fertility, improve soil structure, and support healthier plant growth. Environmental impacts are also reduced, as livestock waste management is more controlled and does not pollute water or air sources. Therefore, utilizing cattle waste as organic fertilizer not only reduces environmental pollution but also provides an economic and ecological solution that benefits local farmers. This program is expected to become a model for empowerment that can be replicated in other areas with similar characteristics.

The selection system at Bina Negara Gubug Vocational School Jl. KH. Hasan Anwar No.9 Gubug currently processes data on the criteria for each student for each type of scholarship. It does not yet have a database system but uses a computerized system with Microsoft Excel, so there are often delays in the selection process in preparing the selection report for scholarship recipients. This research uses the Research and Development (R & D) development model by Borg and Gall with 6 steps of development, namely Research and Information Collecting, Planning, Develop Premilinary Form of Product, Premilinary Field Testing, Main Product Revision, Main Field Testing. The scholarship selection decision support system application product uses the SAW (Simple Additive Waighting) method. Visual Basic 6.0 development software and Microsoft Access database. This system can provide a useful solution for the decision-making system for selecting scholarship recipients for schools so that a better and faster selection can be achieved.