Mount Klabat College

The present study explored the dimensionality of destination brand sensescape (DBS) and its interplay on customers' brand co-creation behavior (CBCB). Employing a mixed-method research design, the study utilized a multi-stage data collection technique and analysis. Initially, a qualitative dataset was gathered through an intensive literature review, interviews, and netnography. Thematic content analysis revealed four underlying dimensions of brand sensescape: recreational, leisure, cultural, and educational. Subsequently, the dimensionality of brand sensescape was confirmed via exploratory factor analysis based on a survey of 331 sample tourists. Finally, a conceptual model was empirically tested using structural equation modelling with an independent sample (n = 563). The results affirm the significant and positive influence of the four dimensions of DBS on CBCB. This study contributes to both theoretical understanding and practical implications in the field of destination branding by elucidating the dimensionality of DBS and its impact on CBCB.

Despite the growing emphasis on character education, many Christian schools still lack a systematic and contextual learning design that effectively integrates Gospel values into classroom practice. This study addresses the problem of how to design a Christian character education learning model that is pedagogically structured, value-based, and applicable in real learning situations. The study employed a Research and Development (R&D) approach using the ADDIE model, consisting of analysis of learning needs, design of instructional components, development of learning prototypes, implementation in an eighth-grade classroom, and evaluation through expert validation and user testing. The research involved 30 eighth-grade students and two Christian Religious Education teachers at SMPN 11 Tidore Kepulauan. The results indicate that the developed learning design achieved high validity scores from content experts (4.6), language experts (4.2), and media experts (4.1), and was positively received by users (4.4). In addition, observational data showed that 87% of students demonstrated improved understanding and application of Christian character values, particularly love and honesty. The novelty of this study lies in the integration of Gospel-based character values with an ADDIE-based instructional design that emphasizes active learning, reflection, and real-life application. The study concludes that the proposed learning design is feasible and effective for strengthening Christian character education and has practical implications for teachers in designing

Accurate food calorie prediction is essential for nutritional assessment, dietary planning, and intelligent health applications. However, achieving high predictive accuracy while maintaining model interpretability remains a challenge for many machine learning approaches. This study proposes an explainable machine learning framework for food calorie prediction using tree-based ensemble models and SHAP (SHapley Additive exPlanations). A dataset containing 1,346 food samples with three nutritional attributes-proteins, fat, and carbohydrate-was used. Data preprocessing included logarithmic transformation and an 80:20 train-test split. Three machine learning models, namely Linear Regression, Random Forest, and XGBoost, were developed and evaluated using Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and the coefficient of determination (R²). Hyperparameter optimization for XGBoost was performed using RandomizedSearchCV with five-fold cross-validation. Experimental results showed that Random Forest achieved the best predictive performance with an MAE of 0.1149, RMSE of 0.2869, and an R² score of 0.9008, outperforming both XGBoost and Linear Regression. Cross-validation further demonstrated the robustness of the selected model, yielding a mean R² of 0.9316 with a standard deviation of 0.0239. Residual analysis indicated prediction errors centered near zero without noticeable systematic bias. SHAP analysis provided both global and local model interpretability, identifying carbohydrate as the most influential feature, followed by fat and proteins. The findings demonstrate that integrating tree-based ensemble learning with SHAP enables accurate and transparent calorie prediction, making the proposed approach suitable for nutritional decision support and explainable artificial intelligence applications. Keywords - Food calorie prediction, Explainable artificial intelligence, SHAP, Random Forest, XGBoost, Machine learning, Nutritional analysis.

This study was conducted due to the increasing competition between furniture stores and suppliers in Bitung City, where many suppliers sell directly through social media at lower prices, potentially reducing customer satisfaction for furniture stores. The purpose of this research is to analyze the influence of Social Media Marketing and Customer Experience on Customer Satisfaction, as well as to examine whether Disconfirmation serves as a mediating variable. A quantitative approach was applied using a survey design, with data collected through questionnaires from 157 respondents and analyzed using PLS-SEM. The findings show that Social Media Marketing and Customer Experience have a positive and significant effect on Customer Satisfaction. Both variables also significantly influence Disconfirmation, and Disconfirmation is proven to mediate these relationships. These results indicate that customer satisfaction increases when marketing information and shopping experiences meet expectations, implying that businesses should maintain consistent information and enhance service quality.

Purpose: This study aims to examine the effect of sustainable branding on customer loyalty, with brand image as a mediating variable in the context of local restaurants. Research Method: This research employs a quantitative approach using Partial Least Squares Structural Equation Modeling (PLS-SEM) to analyze the proposed relationships. Data were collected through an online survey from 138 customers of local restaurants in North Sulawesi, Indonesia. Results and Discussion: The findings reveal that brand image has a significant positive effect on customer loyalty. The People and Planet dimensions of sustainable branding significantly influence brand image but do not directly affect customer loyalty; instead, their effects are fully mediated by brand image. In contrast, the Prosperity dimension does not significantly influence brand image but demonstrates a strong direct effect on customer loyalty. These results indicate that sustainable branding operates through dual pathways: an indirect, perception-based mechanism via brand image (People and Planet) and a direct, value-based mechanism (Prosperity). Implications: This study contributes to the literature by providing a contextual application of sustainability-driven branding in the restaurant industry and offers practical guidance for managers on how to strategically leverage sustainability initiatives to strengthen brand image and foster long-term customer loyalty.