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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.

Public speaking is an essential communication skill that plays a crucial role in enhancing self-confidence, delivering ideas effectively, and fostering social participation. However, many women in rural areas still lack opportunities to develop this skill due to limited access to training, gender stereotypes, and minimal experience in public forums. These challenges lead to low levels of women’s involvement, particularly housewives who are members of PKK, in community activities and decision-making processes at the local level. This study aims to examine the impact of public speaking training on improving the knowledge, self-confidence, and social participation of PKK mothers in Nagari Sumpur Kudus. The research employed a descriptive qualitative method with a participatory approach through interactive lectures, speaking practice simulations, reflective discussions, participatory observations, and qualitative evaluations. Data were analyzed using data reduction, data display, and conclusion drawing based on field notes, observations, and participants’ feedback. The findings indicate that the training successfully improved participants’ understanding of basic public speaking techniques, including intonation, eye contact, body language, and structured idea delivery. Furthermore, the participants experienced significant growth in self-confidence, courage to speak in front of groups, and motivation to continuously practice their communication skills. The interactive and supportive learning atmosphere also strengthened collaboration and solidarity among PKK members. Thus, public speaking training not only enhances individual competencies but also contributes to women’s empowerment and their active participation in community development at the village level.  

This psychoeducation is motivated by the need for service support for students with special needs at SLB Negeri Pembina Yogyakarta. Some students with intellectual disabilities require assistance in recognizing and expressing their emotions. The purpose of this counseling activity is to provide appropriate media needed to support the development of students’ emotional expression in everyday social interactions. The counseling method employed was a play-based learning approach. The evaluation of the effectiveness of this method involved 10 students with intellectual disabilities, consisting of 7 male students and 3 female students in Grade VII of junior high school. Based on the results of the counseling activities, which were conducted in three stages—namely the opening stage, the emotion recognition stage, and the application of the emotion wheel—it was found that the use of the emotion wheel media strengthened the emotional expression abilities of students with intellectual disabilities. This improvement was evident from observable changes in the emotional behavior of some students.

Sentiment analysis is a technique in Natural Language Processing (NLP) used to identify user opinion tendencies based on textual reviews. This study analyzer user reviews of the Maxim application on the Google Play Store and compares three Machine Learning algoritmhs-Naïve Bayes, Support Vector Machine (SVM), and CatBoost-in classifying sentiment. The research stages include data collection, text preprocessing, feature extraction using TF-IDF and Chi-Square, class balancing using SMOTE, and performance evaluation through Accuracy, Precision, Recall, and F1-Score. ANOVA is used to examine the influence of feature selection on model performance. The results show that each model exhibits different performance level across the tested feature combinations. The CatBoost achieved the highest accuracy of 99,26% and demonstrating the most stable performance. Meanwhile, the Naïve Bayes and SVM models experienced performance decreases experiments, especially after applying SMOTE. These findings indicate that the choise of algorithm, feature extraction method, and class balancing technique significantly affects classification outcomes. Overall, CatBoost is identified as the best-performing model, providing more consistenst classification result in accordance with the characteristics of the user reviews.

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.

The rapid development of information technology encourages the use of digital media as an educational tool in the health sector, particularly for pregnant women. One of the problems faced by Klinik Bidan Rima Pondok Meja is the limited use of conventional educational media, such as books and posters, which are considered less attractive and difficult to understand. This study aims to design and develop an Android-based Augmented Reality (AR) application as an educational medium for nutrition and fetal development for pregnant women. The application presents three-dimensional (3D) visualizations of fetal development from week to week, along with information on nutritional needs during pregnancy. The system development method used in this research is the Prototype model, while the Augmented Reality technology applies marker-based tracking. The development tools used include Unity, and Blender 3D. The result of this study is an Android-based AR application prototype that provides interactive and easily understandable information about fetal development and maternal nutrition. This application is expected to increase learning interest and understanding of pregnant women in maintaining a healthy pregnancy at Klinik Bidan Rima Pondok Meja.

General education at Islamic Religious Higher Education Institutions (PTKI) plays an important role in developing students’ critical thinking skills so they can analyze religious and social issues reflectively and contextually. However, learning practices in PTKI are still dominated by teacher-centered approaches and low cognitive achievement, which limits the development of analytical, evaluative, and problem-solving abilities. Meanwhile, studies show that integrating digital media, particularly video, can improve conceptual understanding, motivation, and cognitive engagement, yet a digital video-based meaningful learning model specifically designed for the PTKI context remains limited. Therefore, this study aims to formulate a Digital Video-Based Meaningful Learning Model (MPBBVD) to enhance students’ critical thinking skills. This research employed a literature review method by analyzing books, scientific articles, research reports, and digital sources related to meaningful learning, constructivism, multimedia learning, digital video use, and critical thinking development. Data were analyzed descriptively and qualitatively to identify the theoretical foundations, learning syntax, and design principles of the model. The findings indicate that MPBBVD integrates Ausubel’s Meaningful Learning theory, constructivist perspectives from Piaget, Vygotsky, and Bruner, Mayer’s Multimedia Learning theory, and the critical thinking framework of Facione and Ennis. The model uses digital video as the main learning medium and applies the ACTIVE stages (Attend, Connect, Think, Interpret, Verify, Evaluate) to promote contextual and interactive learning. Integrating pre-, during-, and post-video activities supports knowledge construction, reduces cognitive load, and increases student engagement, contributing theoretically and practically to improving critical thinking in PTKI in the Industrial Revolution 4.0 era.

Globalization and modernization amid the strong digital flow have become a huge challenges and obstacles for shaping moral and ethics of the students. There are many students who come to the class physically without having readiness and awareness in the study. Teacher as an educator needs to give an efforts in every process of learning with attractive concept and model to give attention and meaning in the study. Through the mindful learning, it is considered capable of giving full attention and self-awareness while studying which in the end not only gives good quality in learning but also shapes islamic ethics in the stundents. This research aims to know the concept, urgency, and the implementation of mindful learning which is capable of shaping islamic ethics. The method of the research is a qualitative approach with a literature study from various readings such as articles and journals that are relevant to the topic. The reasearch results show that mindful learning is a learning process that emphasizes self-reflection and self-awareness so students feel ready and motivated in learning. It shows that deep learning has a huge urgency in shaping student’s Islamic ethics. The implementation of mindful learning in Islamic education Studies is carried out through the pedagogic strategies that emphasize the conditioning of awareness, active students involvement, and deep reflection of Islamic values. Thus, mindful learning is capable of increasing internal awareness through muraqabah with the involving of students in every learning process.

This research examines the implementation of the Deep Learning approach in Islamic Religious Education (PAI) learning at SMAN 61 Jakarta and its contribution to the development of students' critical thinking skills. This study was conducted due to the need for a learning model that encourages students to understand religious concepts through active, reflective, and meaningful learning experiences. The purpose of this research is to describe the implementation of Deep Learning in PAI learning and its influence on the development of students' critical thinking skills. This study employs a qualitative method with a descriptive research design, using interviews, classroom observations, and documentation as data collection techniques. The findings indicate that the Deep Learning approach supports students in participating more actively in discussions, analyzing religious issues related to real-life situations, and constructing arguments based on their own understanding. Students also demonstrate improvement in evaluating information, considering diverse perspectives, and providing more reflective responses to classroom problems. The study further identifies several challenges, including limited digital facilities, varying levels of student participation, and teachers’ readiness in managing diverse learning strategies and media. These findings suggest that Deep Learning has strong potential to enhance students’ critical thinking skills when applied systematically and designed to foster active participation and meaningful exploration during the learning process.

This community service activity aims to increase the understanding of students at State Senior High School 2 Mataram regarding information security by introducing the concepts of coding and cryptography in digital messages. The rapid use of messaging applications among teenagers makes students increasingly vulnerable to cyber threats, necessitating education on how data protection works in online communication. This program is implemented using a descriptive method consisting of planning, implementation, and evaluation stages. The material covered includes basic cryptography concepts, end-to-end encryption mechanisms, and the practical process of the Diffie–Hellman key exchange thru the interactive simulation "Alice and Bob." Learning is designed contextually and participatively so that students can connect theory with the digital applications they use every day. The evaluation results showed an improvement in students' understanding, reflected in their active participation, ability to answer questions, and adequate post-test scores. This activity not only strengthens digital security literacy but also raises students' awareness of the importance of protecting personal data in online communication. This program is expected to be the beginning of more sustainable digital security learning development in the school environment.

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 study examines the role of teachers as Islamic counselors in addressing various psychological problems experienced by students, such as learning anxiety, academic stress, low self-confidence, difficulty controlling emotions, and social problems such as bullying. Teachers serve not only as instructors, but also as murabbi, mursyid, and moral role models in guiding students' spiritual and emotional development. The study used a qualitative method with a library study approach, where data were obtained through relevant literature related to Islamic counseling, Islamic education, and student psychology. The results of the study indicate that the implementation of Islamic counseling through strategies such as mau'izhah hasanah, tazkiyah an-nafs, prayer and dhikr, empathy development, and collaboration with guidance and counseling teachers and parents has been proven to reduce students' anxiety and stress levels, increase learning motivation, strengthen morals and character, and provide spiritual peace. Thus, teachers play a strategic role in creating a holistic educational environment and supporting students' psychological well-being. In addition, the implementation of Islamic counseling also plays a role in building students' mental resilience in facing academic and social pressures. In the long term, this will strengthen the Islamic values ​​applied in students, while creating a more resilient and noble generation.

This community service activity aims to increase the understanding of students at State Senior High School 2 Mataram regarding information security by introducing the concepts of coding and cryptography in digital messages. The rapid use of messaging applications among teenagers makes students increasingly vulnerable to cyber threats, necessitating education on how data protection works in online communication. This program is implemented using a descriptive method consisting of planning, implementation, and evaluation stages. The material covered includes basic cryptography concepts, end-to-end encryption mechanisms, and the practical process of the Diffie–Hellman key exchange thru the interactive simulation "Alice and Bob." Learning is designed contextually and participatively so that students can connect theory with the digital applications they use every day. The evaluation results showed an improvement in students' understanding, reflected in their active participation, ability to answer questions, and adequate post-test scores. This activity not only strengthens digital security literacy but also raises students' awareness of the importance of protecting personal data in online communication. This program is expected to be the beginning of more sustainable digital security learning development in the school environment.

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.

Diseases in primary health services exhibit complex spatial-temporal dynamics due to urbanization and population mobility. Conventional surveillance approaches are difficult to capture these patterns adaptively. Machine learning (ML) based on spatio-temporal modeling offers a solution with the ability to detect disease clusters automatically and with high precision. Research Objectives: This research aims to develop a machine learning model to detect disease hotspots from primary service data in Indonesia, with a focus on improving prediction accuracy, interpretability, and relevance of health policies. Methodology: The primary service dataset for 2024 (5,343 entries) was analyzed using three ML models Gradient Boosting Machine (GBM), Temporal Random Forest (TRF), and Multi-EigenSpot with spatial (village) and temporal (week, month) features. Performance evaluation includes predictive (AUC, F1-score) and spatial (Moran's I, Spatio-Temporal Correlation Index) metrics. Results: The results showed that Multi-EigenSpot achieved the best performance (AUC=0.91; F1=0.86), with the detection of dominant hotspots in Sungai Asam and Beringin Villages. Moran's I value of 0.63 indicates a strong spatial autocorrelation, while STCI=0.57 indicates moderate temporal stability. Conclusions: ML-based spatio-temporal models are effective in identifying hidden disease patterns and have the potential to be integrated into national digital surveillance systems. This approach supports precision public health by providing a scientific basis for real-time location- and time-based intervention policies.

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.

The development of technology in the digital era has brought significant changes to global education systems, including in Indonesia. Digital transformation encourages learning models that are more flexible, interactive, and focused on 21st-century competencies. However, this progress also presents various challenges, particularly related to moral degradation among youth, unequal access to technology, and low digital literacy among teachers and students. This study aims to analyze character education models from a global perspective and evaluate the challenges and strategies for their implementation in Indonesia in the digital era. Using a qualitative research method with a library research approach, data were obtained from credible sources such as scientific journals, books, official reports, and national and international educational documents. The findings indicate that developed countries have successfully integrated technology into their curricula, teacher training, and educational policies, resulting in well- established digital learning ecosystems. In Indonesia, digitalization in education has grown rapidly, especially since the pandemic, but still faces obstacles such as limited infrastructure, varied levels of digital competence among teachers, and a lack of integration of digital pedagogy. In addition, student readiness and access inequality in frontier, outermost, and disadvantaged regions (3T) present further challenges in applying global education models

Gross motor development is an essential aspect of early childhood education because it contributes to body coordination, balance, muscle strength, and children’s readiness for physical activities. One of the activities that can be used to stimulate gross motor skills is dancing, particularly regional creative dance. This community service program aims to implement the Janger creative dance as a medium to develop gross motor skills in kindergarten group B children. The method used was evaluative with the CIPP model (Context, Input, Process, Product), involving observation, interviews, and documentation. The activity was carried out once a week through stages of rhythm introduction, basic movements, body coordination, and simple dance sequences. The results showed that more than 80% of the children experienced improvement in aspects of balance, movement coordination, agility, and large muscle control. In addition, the activity also enhanced children's self-confidence, courage, and social interaction. Therefore, the Janger dance is proven to be effective as a gross motor stimulation and is suitable to be used as a culturally based learning strategy in early childhood education.

: The rapid expansion of cybersecurity standards and threat intelligence frameworks has led to significant semantic fragmentation among security terminologies, hindering effective information retrieval and interoperability across systems. Traditional keyword-based search approaches are inadequate for capturing the contextual meaning of security terms, particularly within formal frameworks such as NIST, MITRE ATT&CK, and CWE. This study addresses this challenge by proposing CyberBERT, a transformer-based semantic search framework designed to align cybersecurity terminologies through deep contextual representation and ontology-driven reasoning. Research Objectives: The primary objective of this research is to develop a semantic retrieval model capable of understanding conceptual relationships between security terms beyond lexical similarity. Methodology: The proposed methodology fine-tunes a BERT-based model on the NIST Glossary corpus using a combination of masked language modeling and triplet loss objectives to generate discriminative semantic embeddings. These embeddings are further aligned with cybersecurity ontologies, including MITRE ATT&CK and CWE, to enhance semantic consistency and explainability. Semantic retrieval is performed using cosine similarity within a 768-dimensional embedding space and evaluated using Mean Reciprocal Rank (MRR) and Precision@K metrics. Results: Experimental results demonstrate that CyberBERT achieves an MRR of 0.832, outperforming domain-adapted baselines such as SecureBERT and CyBERT. The integration of ontology alignment improves semantic accuracy by over 6%, while robustness evaluations confirm resilience against adversarial linguistic perturbations. Visualization using t-SNE reveals coherent semantic clustering aligned with the five core NIST Cybersecurity Framework functions. Conclusions: In conclusion, CyberBERT effectively bridges semantic gaps across cybersecurity terminologies by combining transformer-based contextual learning with ontological reasoning. The framework offers a robust, interpretable, and scalable solution for semantic search, supporting improved interoperability and knowledge discovery in cybersecurity operations and standards harmonization.

Gross motor development is an essential aspect of early childhood education because it contributes to body coordination, balance, muscle strength, and children’s readiness for physical activities. One of the activities that can be used to stimulate gross motor skills is dancing, particularly regional creative dance. This community service program aims to implement the Janger creative dance as a medium to develop gross motor skills in kindergarten group B children. The method used was evaluative with the CIPP model (Context, Input, Process, Product), involving observation, interviews, and documentation. The activity was carried out once a week through stages of rhythm introduction, basic movements, body coordination, and simple dance sequences. The results showed that more than 80% of the children experienced improvement in aspects of balance, movement coordination, agility, and large muscle control. In addition, the activity also enhanced children's self-confidence, courage, and social interaction. Therefore, the Janger dance is proven to be effective as a gross motor stimulation and is suitable to be used as a culturally based learning strategy in early childhood education.