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The account of Moses’ death in Deuteronomy 34 has long drawn attention because of the questions it raises about what truly happened at the end of his life. The text leaves certain details unclear, especially regarding his burial and the absence of a known grave, which has led to different interpretations among scholars and faith communities. This study seeks to explore these interpretive possibilities, asking whether Moses simply died, was buried by God, or experienced a unique form of being taken by God. Using a qualitative approach based on literature review, this research engages biblical texts alongside selected contemporary studies. The discussion shows that the narrative intentionally allows room for multiple perspectives, rather than offering a single, definitive explanation. It also becomes clear that later Jewish and Christian traditions played a role in shaping how Moses’ death has been understood, often highlighting its deeper theological meaning. In the end, Moses’ death can be seen not only as the closing of his personal journey, but also as part of a larger story about leadership, transition, and God’s ongoing work among His people.

The role of the Holy Spirit is one of the central aspects of Christian theology, particularly within Evangelical theology, which seeks to uphold a balance between biblical truth and spiritual experience. This study aims to examine the understanding of the Holy Spirit in Evangelical theology, with a specific focus on the relationship between biblical pneumatology and the expressions of contemporary charismatic movements. The approach used is a theological–descriptive study through an analysis of biblical literature, classical Evangelical theological works, and contemporary research on charismatic movements in Indonesia and around the world. The findings indicate that Evangelical theology understands the Holy Spirit primarily as a divine person who works in regeneration, sanctification, and ministry empowerment based on the authority of Scripture. Meanwhile, the charismatic movement emphasizes more phenomenological aspects of the Holy Spirit’s work, such as Spirit baptism, spiritual gifts (charismata), and manifestations of supernatural power. Theological tension arises when charismatic experiences do not always align with the strict Evangelical hermeneutical principles applied to Scripture. Nevertheless, both approaches offer important contributions: biblical pneumatology preserves a healthy doctrinal foundation, while charismatic spirituality reminds the church of the importance of the Holy Spirit’s dynamic power in contemporary ecclesial life. This study concludes that integrating biblical foundations with openness to the work of the Holy Spirit can enrich the understanding and praxis of the contemporary Evangelical church. The church needs to develop a balanced pneumatology that is rooted in Scripture yet responsive to the movement of the Holy Spirit within the changing contexts of time and culture.

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.

This research  examines the transfiguration of Jesus in the Synoptic Gospels, with particular emphasis on the theological distinctiveness of the Gospel of Mark. The study employs a qualitative method using a literature review approach, focusing on narrative analysis, textual comparison, and theological reflection. The analysis shows that although Matthew, Mark, and Luke present the transfiguration event, each Gospel offers distinctive features in wording, narrative structure, and theological emphasis. Mark presents the transfiguration in a concise form, portraying Jesus as the messianic Son of God and as a prophet like Moses, while highlighting the apocalyptic and symbolic aspects of this divine encounter. From Mark’s perspective, the transfiguration serves as a prefiguration of Jesus’ resurrection and a revelation of His messianic identity, which in the Greco-Roman context may be understood as apotheosis—the elevation of a human into divinity. This study affirms that the transfiguration in Mark is not only a historical event but also a theological event that encompasses eschatological and christological dimensions, as well as a reflection on divine hiddenness. In conclusion, the transfiguration is understood as a manifestation of Jesus’ hidden glory within suffering, confirming that the path to resurrection and glory must pass through the cross. This article contributes to biblical and theological studies by demonstrating how Mark articulates a unique christology, one that remains relevant for contemporary Christian faith and opens avenues for further exploration through apocalyptic theology and scriptural intertextuality.

This article explores the exemplary life of Moses in the Old Testament as an inspiration for Generation Z in nurturing and sustaining their faith amidst the challenges of the digital era. As a spiritual leader and bearer of God's law, Moses exemplified obedience, courage, and faith-based leadership—values that remain relevant for today’s youth. Through significant events in his life such as the burning bush encounter, the Exodus, and the wilderness journey, Moses emphasized the importance of trusting God and living according to His commandments. The article also highlights the role of the family, particularly fathers, in providing spiritual education, as well as the necessity for continuous and contextually relevant faith education. By emulating Moses, Generation Z is encouraged to develop strong faith, maintain hope in God, and face societal pressures and life uncertainties with resilience and spiritual conviction.

This article aims to discuss the life of faith of the Israelites as God's chosen people, whether they were loyal to God or to Baal, especially on the journey to the promised land. The Israelites undertook a long and challenging journey. They were ordered by the prophet Moses to be free from Egypt and after Moses' death he was replaced by Joshua, the Israelis lived their lives in the desert for about 40 years, finally reaching the promised land promised by God. For 40 years in the desert, they got food in the form of manna and quail. They also experienced various tests and trials including rebellion and disbelief in God's guiding hand. The Israelites were known as an impatient nation. For example, when Moses was on Mount Sinai when God gave His law to Moses and Moses took too long to come down from the mountain. The Israelites there could not wait patiently because they were afraid of death and starvation because there was no one to lead. They talked to Harun to make a golden calf statue, which was considered to have power and was believed to be a substitute for God to continue their journey. They made a statue of a golden calf because Moses had not come down from Mount Sinai and was thought to be dead. The impatient attitude of the Israelites finally took a shortcut by making a golden calf that was considered to be able to replace God's position. Of course this made God angry because it had violated the 2nd commandment, namely "do not make for yourself any idol".

This article examines the exemplary life of Moses as the key to the success of teaching the word of God and its relevance for Christian Religious Education (PAK) teachers today. Moses is not only known as a prophet and leader of the Israelites, but also as a teacher with high integrity, who relies on God in every aspect of his ministry. Character values ​​such as gentleness, loyalty, dependence on God, and humility are the main attributes that make Moses a figure worthy of emulation. Using a descriptive qualitative method through literature study, this article highlights the importance of a teacher's spiritual life in supporting the effectiveness of teaching the Christian faith. PAK teachers are not enough to just master the cognitive and pedagogical aspects, but need to be living representatives of the teachings they convey. By emulating Moses, PAK teachers are expected to be able to build teaching that is not only informative, but also transformative and inspiring in forming a faithful and responsible generation.

This study aims to examine how the stories of the prophets in the Qur’an can serve as an effective medium for character building in children from an early age. The research is motivated by the growing need for character education amidst the moral challenges of modern times, and by the strong potential of prophetic stories to convey Islamic values such as honesty, patience, responsibility, and monotheism. A qualitative approach was employed through a literature review method. The findings indicate that the stories of Prophet Musa (Moses), Ibrahim (Abraham), Muhammad, and Yunus (Jonah) contain relevant and applicable character values for children. By presenting moral teachings through storytelling, these narratives allow children to internalize values in an engaging and meaningful way. When delivered according to the child’s developmental stage, prophetic stories become an educational tool that effectively nurtures religious, responsible, and noble character. This study is expected to contribute to the development of Islamic value-based character education models in both family and formal educational settings.

This article examines theoretically about visionary leadership starting from the definition, benefits, weaknesses, and biblical views on visionary leadership. This research uses a library research approach. Visionary leaders are able to formulate a vision of the future, inspire commitment, and lead innovative change. The benefits include encouraging innovation and adaptive culture, but there is a risk of dependence and lack of attention to short-term needs. From a Christian perspective, visionary leadership stems from a relationship with God, where vision is divine revelation manifested through prayer and obedience. Biblical figures such as Nehemiah, Joshua, and Moses are examples of visionary leaders who combine faith and strategy. In conclusion, effective visionary leadership must balance strategic and spiritual vision for the success of the organization and community.

Learners are continually faced with choosing appropriate courses or making career choices due to increased educational opportunities. The emergence of machine learning-based course and career recommender systems has the potential to address this issue, offering personalized course recommendations tailored to individual learning pathways, preferences, and learning history. The optimization and feature engineering techniques and practical deployment environments have not been collectively examined in the previous research, despite the significant advancements in this area of research. Furthermore, previous research has rarely synthesized how these technical components help students choose appropriate courses and careers. This systematic review was carried out to investigate the current state of machine learning-based course and career recommender systems, focusing on key elements, such as primary data sources, feature engineering methods, algorithms, optimization techniques, evaluation metrics, and the environments where the existing course recommendation models are deployed. The PRISMA method for conducting a systematic review was used to choose studies that met the requirements for inclusion and exclusion. The study findings show significant reliance on interpretable and traditional machine learning algorithms, such as K-Nearest Neighbor and Random Forest, to develop recommender models. Feature engineering remains basic, as most studies rely on normalization, while optimization processes are often underreported. Also, evaluation metrics varied widely, impeding comparability, while most of the recommender models are deployed in an e-learning environment, leaving the traditional learning environment underrepresented. Furthermore, the study findings identified issues including data sparsity and diversity, data security and privacy, and changes in learner preferences that may have an impact on the performance of recommender systems while recommending further studies to make use of standardized optimization methods, and automated domain-informed feature engineering frameworks, benchmark and annotated datasets in developing models the gives priority to learners’ success and educational relevance.

Breast cancer is the most prevalent cancer among women worldwide, requiring early and accurate diagnosis to reduce mortality. This study proposes a hybrid classification pipeline that integrates Hybrid Statistical Feature Selection (HSFS) with unsupervised LSTM-guided feature extraction for breast cancer detection using the Wisconsin Diagnostic Breast Cancer (WDBC) dataset. Initially, 20 features were selected using HSFS based on Mutual Information, Chi-square, and Pearson Correlation. To address class imbalance, the training set was balanced using the Synthetic Minority Over-sampling Technique (SMOTE). Subsequently, an LSTM encoder extracted non-linear latent features from the selected features. A fusion strategy was applied by concatenating the statistical and latent features, followed by re-selection of the top 30 features. The final classification was performed using a Support Vector Machine (SVM) with RBF kernel and evaluated using 5-fold cross-validation and a held-out test set. Experimental results showed that the proposed method achieved an average training accuracy of 98.13%, F1-score of 98.13%, and AUC-ROC of 99.55%. On the held-out test set, the model reached an accuracy of 99.30%, precision of 100%, and F1-score of 99.05%, with an AUC-ROC of 0.9973. The proposed pipeline demonstrates improved generalization and interpretability compared to existing methods such as LightGBM-PSO, DHH-GRU, and ensemble deep networks. These results highlight the effectiveness of combining statistical selection and LSTM-based latent feature encoding in a balanced classification framework.

Aspect-based sentiment Analysis (ABSA) is vital in capturing customer opinions on specific e-commerce products and service attributes. This study proposes a hybrid deep learning model integrating Bi-Directional Gated Recurrent Units (BiGRU) and Bi-Directional Attention Flow (BiDAF) to perform aspect-level sentiment classification. BiGRU captures sequential dependencies, while BiDAF enhances attention by focusing on sentiment-relevant segments. The model is trained on an Amazon review dataset with preprocessing steps, including emoji handling, slang normalization, and lemmatization. It achieves a peak training accuracy of 99.78% at epoch 138 with early stopping. The model delivers a strong performance on the Amazon test set across four key aspects: price, quality, service, and delivery, with F1 scores ranging from 0.90 to 0.92. The model was also evaluated on the SemEval 2014 ABSA dataset to assess generalizability. Results on the restaurant domain achieved an F1-score of 88.78% and 83.66% on the laptop domain, outperforming several state-of-the-art baselines. These findings confirm the effectiveness of the BiGRU-BiDAF architecture in modeling aspect-specific sentiment across diverse domains.

Monkeypox is a zoonotic disease caused by Orthopoxvirus, presenting clinical challenges due to its visual similarity to other dermatological conditions. Early and accurate detection is crucial to prevent further transmission, yet conventional diagnostic methods are often resource-intensive and time-consuming. This study proposes a deep learning-based classification model by integrating Xception and InceptionV3 using feature fusion to enhance performance in classifying Monkeypox skin lesions. Given the limited availability of annotated medical images, data augmentation was applied using Albumentation to improve model generalization. The proposed model was trained and evaluated on the Monkeypox Skin Lesion Dataset (MSLD), achieving 85.96% accuracy, 86.47% precision, 85.25% recall, 78.43% specificity, and an AUC score of 0.8931, outperforming existing methods. Notably, data augmentation significantly improved recall from 81.23% to 85.25%, demonstrating its effectiveness in enhancing sensitivity to positive cases. Ablation studies further validated that augmentation increased overall accuracy from 82.02% to 85.96%, emphasizing its role in improving model robustness. Comparative analysis with other models confirmed the superiority of our approach. This research enhances automated Monkeypox detection, offering a robust and efficient tool for low-resource clinical settings. The findings reinforce the potential of feature fusion and augmentation in improving deep learn-ing-based medical image classification, facilitating more reliable and accessible disease identification.

This study presents a Quantum Machine Learning (QML) architecture for perfectly classifying the Iris flower dataset. The research addresses improving classification accuracy using quantum models in machine-learning tasks. The objective is to demonstrate the effectiveness of QML approaches, specifically the Variational Quantum Circuit (VQC), Quantum Neural Network (QNN), and Quantum Support Vector Machine (QSVM), in achieving high performance on the Iris dataset. The proposed methods result in perfect classification, with all models attaining accuracy, precision, recall, and an F1-score of 1.00. The main finding is that the QML architecture successfully achieves flawless classification, contributing significantly to the field. These results underscore the potential of QML in solving complex classification problems and highlight its promise for future applications across various domains. The study concludes that QML techniques can offer transformative solutions in machine learning tasks, particularly those leveraging VQC, QNN, and QSVM.

The rapid use of smartphones and the internet in the modern era has brought significant impacts, especially in the spread of animal abuse content. In 2021, there were 5,480 animal abuse contents spread around the world, with 1,626 contents coming from Indonesia. The Art Director's role as the supervisor and director of art and visual elements in various creative projects, including public service announcements, is crucial. Art Directors are responsible for ensuring the aesthetic and creative vision of a project is in line with the objectives and target audience. This research aims to explain the importance of concept preparation in the creation of public service advertisements to readers or audiences. The general benefit of this study is to increase public understanding of the role of the Art Director as the conceptualizer behind public service advertisements. This study focuses on a public service announcement titled "Stop Animal Torture as a Means of Entertainment," which aims to remind people about the human rights of animals. Considering that Indonesia is the world's top producer of animal torture content, this ad calls for improving this bad data as it highlights the importance of the issue. With around 1,600 animal abuse content originating from Indonesia out of a total of 5,480 global content, this ad is an effort to reduce the negative impact on living beings and raise awareness of the issue.

Self-confidence is an important element in achieving success and happiness, and has a significant positive relationship with motivation to learn. However, many individuals face challenges in building their self-confidence due to social pressure and negative experiences. In this context, this study explores sources of motivation from a Quranic perspective, specifically through Surah At-Thaha verses 25-28, which describe Prophet Moses' plea to Allah for peace and ease in facing life's challenges. Tafsir Al-Misbah by Quraish Shihab provides the insight that self-confidence should be built on faith in Allah and recognition of one's potential. This study also highlights the importance of social support, appreciation, and a supportive environment in building self-confidence. Using a qualitative approach and literature review, the results show that self-confidence can be strengthened through spiritual connection, internal reinforcement, and recognition of personal weaknesses, which ultimately encourages individuals to express themselves and reach their full potential. This research is expected to inspire readers to apply Quranic values in their daily lives, in order to increase self-confidence and freedom of expression.

Credit approval prediction is one of the critical challenges in the financial industry, where the accuracy and efficiency of credit decision-making can significantly affect business risk. This study proposes an outlier detection method using the Gaussian Mixture Model (GMM) combined with Extreme Gradient Boosting (XGBoost) to improve prediction accuracy. GMM is used to detect outliers with a probabilistic approach, allowing for finer-grained anomaly identification compared to distance- or density-based methods. Furthermore, the data cleaned through GMM is processed using XGBoost, a decision tree-based boosting algorithm that efficiently handles complex datasets. This study compares the performance of XGBoost with various outlier detection methods, such as LOF, CBLOF, DBSCAN, IF, and K-Means, as well as various other classification algorithms based on machine learning and deep learning. Experimental results show that the combination of GMM and XGBoost provides the best performance with an accuracy of 95.493%, a recall of 91.650%, and an AUC of 95.145%, outperforming other models in the context of credit approval prediction on an imbalanced dataset. The proposed method has been proven to reduce prediction errors and improve the model's reliability in detecting eligible credit applications.

This research aims to design packaging and promotional media for the Lebaran edition of Tango Walut products with a focus on increasing consumer appeal in the competitive modern market. The design process includes in-depth analysis of products and markets,exploration of creative ideas, development of attractive designs, and implementation of effective marketing strategies. The resulting packaging design displays typical Eid visual elements with bright colors and illustrations that evoke the spirit of celebration. The marketing strategy implemented involves offline marketing and online marketing with social media campaigns, both of which are designed to increase consumer awareness, interest, desires and purchasing actions. The final result of this design is expected to increase the visibility and sales of Tango Walut products during the Eid period, as well as strengthen the brand's position in the FMCG market.

Customer attrition has become the focus of many businesses today – since the online market space has continued to proffer customers, various choices and alternatives to goods, services, and products for their monies. Businesses must seek to improve value, meet customers' teething demands/needs, enhance their strategies toward customer retention, and better monetize. The study compares the effects of data resampling schemes on predicting customer churn for both Random Forest (RF) and XGBoost ensembles. Data resampling schemes used include: (a) default mode, (b) random-under-sampling RUS, (c) synthetic minority oversampling technique (SMOTE), and (d) SMOTE-edited nearest neighbor (SMOTEEN). Both tree-based ensembles were constructed and trained to assess how well they performed with the chi-square feature selection mode. The result shows that RF achieved F1 0.9898, Accuracy 0.9973, Precision 0.9457, and Recall 0.9698 for the default, RUS, SMOTE, and SMOTEEN resampling, respectively. Xgboost outperformed Random Forest with F1 0.9945, Accuracy 0.9984, Precision 0.9616, and Recall 0.9890 for the default, RUS, SMOTE, and SMOTEEN, respectively. Studies support that the use of SMOTEEN resampling outperforms other schemes; while, it attributed XGBoost enhanced performance to hyper-parameter tuning of its decision trees. Retention strategies of recency-frequency-monetization were used and have been found to curb churn and improve monetization policies that will place business managers ahead of the curve of churning by customers.

Leadership of Joshua in the Bible, highlighting his role, method, results of discussion, and relevance in modern context. Joshua was chosen by God as the successor of Moses, leading the Israelites towards the land of Canaan with courage, steadfastness, and obedience to God's commands. The research method employed was a qualitative approach using literature review, exploring the leadership values of Joshua in the context of the Bible. Joshua's selection by God, preparation and strategies in conquering the Promised Land, and his obedience to God's commands were discussed. There are leadership principles embodied by Joshua, such as firmness, courage, good character, innovation, steadfastness, and moral principles, which are relevant for modern leaders in creating stable and innovative environments. These principles encourage integrity, courage, and the ability to overcome challenges with appropriate solutions, while maintaining focus on the main goals and building strong relationships with the team.