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That Christian Religious Education teachers update their professionalism and competencies to ensure that Christian education remains relevant, effective, and transformative. This study aims to analyze the modernization of the professionalism of Christian Religious Education teachers, including the integration of spirituality with modern pedagogical approaches and strategies for developing teacher competencies in addressing the challenges of the digital generation. The study employs a descriptive qualitative approach, collecting data through literature review from books, journals, notes, and relevant reports, and presenting the findings in a narrative form. The results show that the modernization of teacher competencies involves the development of pedagogical, personal, social, spiritual, and professional competencies, with the ability to integrate spirituality, technology, and modern teaching methods such as collaborative and digital learning to create contextual, engaging, and transformative learning experiences. In conclusion, the modernization of professionalism among Christian Religious Education teachers is an urgent necessity to enable teachers to deliver God’s Word in a relevant manner, shape students’ character and spirituality, and enhance the effectiveness of Christian education through mastery of holistic and collaborative competencies.  

This study aims to classify customer payment methods at 17 Coffee & Eatery using machine learning algorithms, namely Naïve Bayes and Support Vector Machine (SVM). The increasing use of digital and non-cash payments has generated large volumes of transaction data that are rarely analyzed optimally, even though such data contain valuable information for business decision making. This research used secondary transaction data collected from January to March 2025, consisting of 10,147 transaction records. The dataset included several attributes such as order time, payment time, transaction type, total sales, number of items, and payment method. Data preprocessing was performed through data cleaning, feature engineering, normalization, and label encoding before being divided into training and testing sets with an 80:20 ratio. The Naïve Bayes and SVM models were then trained and evaluated using accuracy, precision, recall, F1-score, and ROC–AUC metrics. The results show that both algorithms were able to classify payment methods effectively, but SVM achieved higher accuracy and more stable performance than Naïve Bayes. These findings indicate that SVM is more suitable for handling complex and heterogeneous transaction patterns. The implementation of machine learning for transaction classification can support more efficient financial management and data-driven decision making for small and medium enterprises in the culinary sector.

The Mastery Learning Strategy is a learning approach oriented towards achieving comprehensive student competencies before they move on to the next material or learning stage. This approach is based on the assumption that every student has the potential to succeed, provided they are given the appropriate time, methods, and guidance. In its application, Mastery Learning emphasizes systematic learning planning, the establishment of clear learning objectives, and ongoing evaluation to measure the level of student mastery. Students who have not yet achieved competency standards will receive corrective feedback and remedial activities, while students who have completed them will be provided with enrichment programs to deepen their understanding. With this mechanism, learning gaps can be minimized so as not to hinder the learning process in the next stage. Furthermore, this strategy encourages individualized, structured, and measurable learning according to student needs. Therefore, the implementation of Mastery Learning is considered effective in improving the quality of the learning process, strengthening conceptual understanding, and contributing to optimal and sustainable learning outcomes.  

Jambi Province is one of the largest natural rubber producing regions in Indonesia; however, rubber factories under GAPKINDO Jambi still face productivity issues, particularly the gap between production capacity and actual output, and productivity assessment that is still conducted manually by GAPKINDO Jambi. This study employs Decision Tree, Random Forest, KNN, and SVM algorithms within a structured pipeline involving preprocessing, feature selection, standardization, data balancing using SMOTE, and hyperparameter tuning. The proposed solution applies productivity level classification both individually and through paired combinations (ensemble voting). The results show that the Decision Tree + Random Forest model achieves the best performance with an accuracy of 0.84 and an F1-score of 0.83, confirming the effectiveness of ensemble methods in supporting productivity improvement decisions.

This study aims to evaluate the impact of role-playing methods on learning interest and number concept mastery in 4-5-year-old children at TK Inomata, Suwawa Selatan District, Bone Bolango Regency. The approach used is a quantitative experimental method with purposive sampling of 8 children, and data collection was carried out through observations of aspects such as enjoyment, focus, activity, and enthusiasm, using a 1-4 scale. The results show that the average learning interest score using the traditional method was only 5.86 (low category), with details: enjoyment 1.71, focus 1.29, activity 1.29, and enthusiasm 1.57. In contrast, the role-playing method of trading/store games showed an average score of 13.86 (very good category), with details: enjoyment 3.57, focus 3.43, activity 3.29, and enthusiasm 3.57. This significant improvement indicates that the role-playing method is more effective in creating interactive, enjoyable, and contextual learning, as well as supporting number comprehension through everyday activity imitation and social skill development. It is recommended to routinely apply this method in early childhood education (PAUD) with adequate teaching aids, as well as involve parents to support the holistic development of children during the golden age.

Flower disease detection is a significant challenge in modern agriculture, particularly with factors such as changes in leaf color, petal shape and structure, and environmental conditions affecting the accuracy of conventional models. These factors make it difficult to achieve optimal results using traditional methods. Transfer learning is an effective solution to improve image detection performance, especially when data is limited. This study used several pre-trained models, namely VGG16, ResNet50, and EfficientNet-B0, to detect three types of flower diseases: black spot on roses, white powdery mildew, and leaf rust. The research process included data processing, increasing the data volume using augmentation techniques, model training, and evaluation of the results. Experimental results showed that the EfficientNet-B0 model produced the highest accuracy of 97.2%, significantly better than the CNN model built from scratch with an accuracy of 85.1%. This study demonstrates that transfer learning is highly effective in improving the accuracy of flower disease detection, making it a more reliable alternative to methods that do not utilize pre-trained models, especially for agricultural applications that require high levels of accuracy in disease detection.

This study presents a high-accuracy real-time soft failure detection framework for large-scale fiber-to-the-home(FTTH) optical access network using a hybrid ensemble of Isolation Forest and One-Class Support Vector Machine (OCVSM). The proposed model was trainde and validated on a real-word multivariate performance dataset comprising more than 1.8 million samples collected at 5-minute intervals from 50 Optical Line Terminal (OLTs) and over 3,000 Optical Network Terminals (ONTs) across a five-month periode(June-October 2025). Ground-truth validation was performed using 111 confirmed network incidents in October 2025 affecting 12,990 customer. The hybrid ensemble achieved Precision 0.940, Recall 0.982, with an average detection delay of only 7.8 minutes-representing an 87.7% reduction compared to conventional manual response (63.5 minutes). The framework significantly outperforms traditional threesholding and recent ML-based methods while demonstrating practical deployability in live operational enviroments.

The rapid development of digital streaming platforms such as Netflix has generated a large volume of content data with diverse characteristics, thereby requiring effective analytical methods to understand emerging patterns and trends. This study aims to classify Netflix content into two main categories, namely movies and television shows, and to analyze genre trends and content characteristics using a data mining approach with the Naive Bayes algorithm. The dataset used in this study is the Netflix Shows dataset, consisting of 8,809 content entries, with the primary features analyzed including genre, rating, and country of production. The research process begins with data exploration and preprocessing stages, including data cleaning, handling missing values, and transforming categorical features to enable effective model construction. Subsequently, the dataset is divided into training and testing sets to objectively and systematically build and evaluate the Naive Bayes classification model. Model performance is evaluated using accuracy, precision, recall, and F1-score metrics to assess the model’s ability to accurately distinguish between Netflix content types. The experimental results demonstrate that the Naive Bayes algorithm is able to classify Netflix content into Movie and TV Show categories with accuracy, precision, recall, and F1-score values of 100%, respectively. The confusion matrix indicates that no misclassification occurred, suggesting that genre, rating, and country of production features provide a very clear separation between content classes. These findings indicate that the Naive Bayes algorithm can achieve exceptionally high classification performance with optimal evaluation results. The results further reveal distinct differences in characteristics between movies and television shows based on genre and production attributes. Therefore, this study is expected to contribute to the development of content recommendation systems and strategic content management within the streaming industry.

Training on creating Heyzine-Canva-based e-book learning media through the Subject Teacher Working Group (MGMP) aims to improve teachers' professional competence in facing educational demands and digital technology integration. This training activity is designed using demonstration, hands-on practice, and group discussion methods, so that it can accommodate various levels of technology mastery among teachers. Through this training, teachers are guided to develop interactive e-books, learning videos, and innovative digital quizzes using the Heyzine and Canva platforms. The training results show a significant improvement in teachers' skills in utilizing digital media, confidence in integrating technology into learning, and motivation to continue innovating. Additionally, this training also encourages collaboration among teachers and builds a professional network that supports continuous development. Challenges faced, such as variations in technology skills and device limitations, are addressed through ongoing mentoring and further training. This training contributes to improving the quality of learning, student engagement, and supports the achievement of national education goals in the digital era.

This study aims to describe the implementation of the Christian Religious Education teaching module on the topic "Gratitude for God's Creation" in grade VII of SMP Negeri 12 Palangka Raya. The implementation was carried out to help students understand the meaning of gratitude for God's creation and apply it in everyday life through active and creative learning activities. The learning activities used group discussion methods, faith projects, role-playing, presentations, and personal reflection so that students were directly involved in learning, not only as listeners but as active actors in the learning process. The research subjects were three students who participated in the learning according to the Christian Religious Education subject schedule. The results showed that students were able to recognize and name various creations of God through reading Genesis 1:1–31, express gratitude through poster works and simple songs, and show enthusiasm during the learning process. The assessment of learning outcomes showed that two students obtained the good category and one student the adequate category, which means that the implementation of this teaching module has a positive impact on the development of students' knowledge, attitudes, and skills.

This study discusses the introduction of digital signature patterns using the Backpropagation method on Artificial Neural Network (JST) to identify a person's characteristics and potential. The increasing use of digital identities demands a verification system that is more secure, accurate, and adaptive to the variations of each individual's signature. The main problem faced in the signature recognition system is the low level of accuracy when the visual features of the signature have similarities between users, both in terms of shape, size, and stroke pressure. In addition, variations of signatures made by the same individual are also a challenge in the identification process. As a solution, this study implements Principal Component Analysis (PCA) to extract important features from the signature image before the training process using JST. PCA is used to reduce the data dimension so that the learning process becomes more efficient and optimal. A total of 80 signature images were used in this study, consisting of 60 training data and 20 test data. The results showed that the system was able to achieve an accuracy level of 92.5%. These findings prove that the combination of PCA and JST methods is effective in recognizing digital signature patterns and has the potential to be applied to digital security-based biometric identification systems.

This study aims to implement the Christian Religious Education and Character Education (PAK) teaching module in grade V of SDN 1 Sabaru, Palangka Raya, with a focus on the topic “Jesus Was Crucified, Died, and Risen for Me.” The learning process is designed to support students’ spiritual and character development through an interactive and project-based approach. Teaching methods include group discussions, the use of miniature media as visual learning aids, collaborative activities, and guided reflection on Christian values relevant to students’ daily experiences. The results of classroom observations show that most students are active, enthusiastic, and involved in the learning process, able to understand the theological meaning of the crucifixion, death, and resurrection of Jesus, and apply the values of love, gratitude, and responsibility in everyday life. Obstacles found include a lack of self-confidence in some students and less optimal group dynamics during activities. These findings serve as important evaluation materials for improving the effectiveness of future learning implementation

This article explores the concept of a “House of Faith” as a holistic educational framework designed to build students’ character based on religious values. The fundamental idea of the House of Faith is likened to a school environment that provides a comfortable and safe space for students to develop their faith and noble character through learning activities connected to religious teachings. A religious school environment is created through everyday atmospheres that encourage the practice of worship, the presence of religious symbols, and community activities that strengthen students’ spirituality. Programs to reinforce religious spirit include activities such as communal prayers, religious studies, and social projects designed to gradually enhance students’ spiritual awareness. Teachers and educational staff play a highly effective role, acting as role models and facilitators in the learning process. Their approach to instilling faith and moral values involves interactive methods such as group discussions, inspirational stories, and personal reflection, all adapted to students’ developmental stages. Collaboration between the school and parents becomes a key factor in ensuring consistent support. This is carried out through regular meetings, joint workshops, and open communication that reinforces family values. The impact of religious spirit on students can be seen in increased discipline, social empathy, emotional resilience, and stronger academic motivation. However, implementation faces challenges such as limited curriculum time, diversity of student backgrounds, and cultural resistance. These challenges are addressed through solutions, including regular teacher training, program evaluation, and the integration of technology for distance learning. Overall, the article emphasizes that a school functioning as a House of Faith can shape young generations who are not only intellectually capable but also strong in faith and morals, contributing to a more harmonious and meaningful society.

Children living in orphanages face various psychological pressures, such as the loss of attachment figures, unstable social dynamics, and difficulty expressing emotions adaptively. These conditions require appropriate coping strategies to build emotional resilience. This community service activity utilizes a Service Learning (SL) approach, which integrates academic learning with community service. It aims to provide psychoeducation on emotions and coping strategies, and implements expressive writing as a means of emotional processing. The activity methods included interactive lectures, a pretest and posttest to measure understanding, an expressive writing therapy session, and a reflective interview at the end of the activity. Results showed an increase in understanding of emotions and coping strategies, as evidenced by improved posttest scores. Furthermore, expressive writing helped children express previously suppressed emotions, reduced psychological tension, and fostered a sense of relief, calm, and insight into personal problems. Overall, expressive writing has proven effective as a coping strategy in building emotional resilience in orphanage children, and the Service Learning approach has the potential for sustainable application in psychosocial support programs.

Tourism is one of the sectors that plays an important role in boosting economic growth through travel activities and destination exploration. Tourists' preferences for nature-based tourism options, such as mountain hiking or beach tourism, are influenced by various factors, ranging from personal experiences and recreational interests to social characteristics. Therefore, a technology-based approach is needed to predict destination choice tendencies more accurately. As artificial intelligence technology develops, deep learning methods have been widely used in classification processes due to their ability to process large amounts of data and recognize complex patterns. In this study, a Multilayer Perceptron (MLP) model is used to classify tourists' preferences between mountain or beach destinations based on a survey dataset. The research stages include data processing, data splitting using a train-test split, model training, and performance evaluation using accuracy, precision, recall, and F1-score. The test results show that the MLP model is capable of achieving an accuracy rate of 99%, confirming that deep learning methods are effective in automatically mapping tourism preference trends. This research is expected to serve as a basis for the development of more personalized travel destination recommendation systems, as well as to support tourism management in formulating targeted promotional strategies.

This study discusses the concept of lifelong education from an Islamic perspective by highlighting the thoughts of Ibn Khaldun as a prominent figure in the history of education. Using a qualitative method based on a literature study, this research examines sources from the Qur’an, Hadith, and related scholarly works to understand the theological and philosophical foundations of lifelong education. The findings indicate that education in Islam is viewed as a continuous process that takes place from birth until the end of life. Ibn Khaldun emphasized the importance of a gradual and systematic learning process, as well as the use of methods that align with learners’ developmental stages. This concept is consistent with the demands of modern education, which emphasize active, creative, and adaptive learning. Furthermore, the study highlights various forms of lifelong education implementation, such as vocational, professional, civic, and cultural education. These findings affirm that lifelong education is an essential necessity in responding to social, technological, and civilizational dynamics, while also constituting an act of worship and a human responsibility as khalifah (stewards) on earth.

This study aims to describe the difficulties faced by Geography teachers at SMAN 1 Sunggal in identifying students' learning potential and interests during the implementation of the Merdeka Curriculum. The research uses a descriptive qualitative approach with primary data consisting of in-depth interviews and non-participant observation of two experienced Geography teachers. The research findings indicate that teachers face major constraints, including limited time for formal diagnostic assessments, a lack of practical training on diagnostic assessments, a large number of students making individual observation difficult, and the absence of adequate assessment instruments. The teacher's efforts to overcome these difficulties include a personal approach to students, behavioral observation, and varied teaching methods, although these are not yet optimal in supporting differentiated learning. This research provides important recommendations for the development of teacher training and school policies to more effectively support the assessment of student potential and interests within the context of the Merdeka Curriculum.

This community service program aims to introduce basic English vocabulary to Kindergarten students through the medium of bilingual songs. The background of this activity is the limitation of interactive learning media and the dominance of traditional methods that are less attractive for early childhood. Bilingual songs were chosen because they have a simple rhythm, easy to remember lyrics, and are able to create a fun and contextual learning atmosphere. The method used is the Participatory Action Learning System approach which includes the preparation stage, flexible action, and reflection. Bilingual songs are inserted at the transition moments of class activities and combined with simple movements to stimulate natural vocabulary acquisition. The results of the observations showed a significant improvement in children's language skills: most students were able to remember new vocabulary, enthusiasm for learning increased, and active participation in singing together was higher. Teachers also reported that the use of bilingual songs helped keep children's attention and created a positive learning atmosphere. Overall, this activity proves that bilingual songs are an effective, fun, and appropriate medium for children's developmental stages to strengthen mastery of basic English vocabulary. This program is recommended to be applied continuously in daily learning activities in kindergarten classrooms.

The purpose of this study was to evaluate the application of the Deep Learning approach in teaching mathematics in the first grade of addition and subtraction of numbers 1 to 20 in a story problem through the concrete media Si PONPEL and the educational game Quizizz Paper Mode. This technology-based learning is intended to make learning more interactive and enjoyable, but does not require personal digital devices. Qualitative and quantitative data were collected through a mixed-method research design to measure student understanding the result show that the Si PONPEL and Quizizz Paper Mode methods successfully improved students’ understanding of basic mathematical concepts, with an average increase in student scores of 38.33%. In addition, findings from interviews conducted with teachers and students showed that the Si PONPEL concrete media and this technology made students more engaged and motivated to learn. It is hoped that this research will make a significant contribution to creating more inclusive and interactive mathematics kearning in elementary schools.

This study explores the major challenges faced by moral education in the modern era, particularly within Madrasah Ibtidaiyah, where rapid technological advancement, globalization, and shifting patterns of student interaction have significantly influenced the traditional process of moral development. The purpose of this research is to analyze teachers’ strategies in revitalizing moral education, identify the supporting and inhibiting factors in its implementation, and evaluate its impact on students’ discipline. Employing a qualitative approach with a case study design, data were collected through in-depth interviews with the school principal, Islamic ethics teachers, and students, as well as observations of daily moral habituation practices within the school environment. The findings indicate that effective revitalization of moral education occurs through an integration of teacher role modeling, consistent habituation of moral practices, and value internalization supported by interactive learning methods. These strategies contribute to improved student discipline, greater independence, and more stable behavioral patterns. However, the effectiveness of these efforts is still influenced by external factors such as excessive gadget use and limited continuity of moral guidance at home. The study provides an integrative and adaptive model of moral education that aligns with the characteristics of the digital generation and emphasizes the essential collaboration between teachers, schools, and families. The results highlight the importance of a responsive, contextually relevant approach to moral development that meets the evolving needs and challenges of 21st-century learners.