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The development of fine motor skills in elementary school-aged children is crucial for academic achievement and daily activities. However, some children still experience delays in fine motor skills due to lack of stimulation, a lack of varied teaching methods, and the impact of technology use and learning after the pandemic. This study aims to improve fine motor skills in elementary school children through Educational Activities Using LEGO Bricks. The method used is a play-based learning approach integrated into thematic learning. The activities were conducted in several structured sessions, including an introduction, core activities, and reflection and evaluation. Participants were elementary school students who actively participated in LEGO assembly activities both individually and in groups. The results showed improvements in fine motor skills, evident in improvements in hand-eye coordination, accuracy, concentration, and manipulative abilities. Students also demonstrated great enthusiasm and progress in creativity when creating various types of works. The discussion indicates that playing with LEGO is beneficial for training small muscles in the hands through pinching, pressing, and arranging activities, as well as supporting cognitive development and a passion for learning. In other words, the LEGO Brick Educational Play Movement has proven to be an innovative and fun learning medium, as well as effective in developing the fine motor skills of elementary school students.

Sentiment analysis for Indonesian regional languages faces two persistent challenges: labeled training data is extremely limited for most regional varieties, and transformer models pre-trained on Bahasa Indonesia do not generalize reliably to languages with substantially different morphological structures. Prior work on the NusaX benchmark has primarily relied on direct fine-tuning, treating each regional language independently and without exploiting linguistic proximity between related languages as a transfer signal. This paper proposes Language-Similarity-Guided Transfer (LSGT), a sequential fine-tuning strategy that first adapts a pre-trained model to a pivot language selected using character trigram similarity, followed by fine-tuning on the target language. Four transformer models are evaluated across all 12 NusaX languages using the official train/validation/test splits: IndoBERT, NusaBERT, mBERT, and XLM-R. Performance is evaluated using four metrics: accuracy, macro F1, macro precision, and macro recall. Experimental results show that LSGT improves macro F1 in 44 of 48 model-language combinations, demonstrating that the fine-tuning strategy itself is a major factor in low-resource cross-lingual sentiment classification. XLM-R benefits most strongly from LSGT, achieving an average improvement of +0.137 macro F1 and a peak gain of +0.298 on Madurese. SHAP-based token attribution analysis further reveals that predictions rely heavily on named entities and domain-specific nouns rather than sentiment-bearing vocabulary, indicating a dataset-level bias inherited from the original SmSA corpus and propagated through the NusaX translation pipeline.

Learning fiqh is a very important thing to learn because fiqh regulates all the laws of Allah related to human work where the laws are taken from the Qur'an and Sunnah by means of ijtihad. However, in the field there are still many students who do not understand how to perform tayammum taharah correctly. This happens due to lack of understanding and insufficient habits. Thus, this PKM activity aims to educate students related to taharah so that students not only understand theory, but can apply it in daily life according to Islamic law. The method used is Asset Based Community Development (ABCD) with the stages of discovery, dream, design, define, and destiny. To optimize local potentials such as the competence of ustazah and TPQ facilities, through lectures, demonstrations, discussions, hands-on practice, and evaluation based on interactive quizzes. The results of the tayammum mentoring activities show that TPQ Darul Falah students are very active and interactive during learning. Students who initially did not understand the procedures of tayammum properly and perfunctorially, are now able to do it according to Islamic law. In addition, this training activity provides direct understanding and experience to ustazah and students of TPQ Darul Falah in learning tayamum practice.

Christian Religious Education (PAK) learning is undergoing significant changes in the Digital 5.0 era with the utilization of technology as the main support. This study explores the challenges in Christian learning and explores technological solutions that can improve student engagement and material comprehension. Through the literature study, the main challenges identified include the limitations of traditional learning resources and the lack of attractiveness of the materials. Solutions are proposed by integrating E-Learning, Virtual Reality, Augmented Reality, and mobile applications in the curriculum. Teacher training and support from the community and church are key in implementing this approach. Continuous evaluation and problem solving are the focus to ensure success and sustainability. The results show that technology contributes significantly to creating an interactive and relevant learning environment, building a solid foundation for increased student engagement and understanding of Christian teachings. By stepping with technology, this article presents a foundation for enhancing Christian Religious Education learning amidst the dynamics of the digital world

Abstract: The digital era demands that every individual possess visual literacy competencies, but in Islamic boarding schools (pesantren), access to technology is often limited to maintain focus on worship. The main problem at the Salafiyah Al-Furqon Islamic Boarding School in Sawangan, Magelang Regency, is the digital skills gap among 12th-grade students. This is caused by the boarding school's policy of only allowing mobile phone use during semester breaks, leaving students lacking practical multimedia skills prior to graduation. This community service activity aims to provide a solution through basic photography and videography training as a soft skill for alumni independence. The method used is a workshop that combines theoretical explanations and direct practice (experiential learning). The results of this activity show a significant increase in students' technical competency; participants are able to operate smartphone cameras with stable shooting techniques, understand the principles of visual composition (such as the rule of thirds and leading lines), and are able to perform basic video editing using mobile-based software applications. Through this training, Islamic boarding school graduates are expected to become individuals who are adaptive to technological developments and able to utilize visual media as a means of creative economics and digital da'wah after graduation.

This study examines customer churn prediction in subscription-based telecommunications from a digital marketing perspective using machine learning. The analysis utilizes a secondary dataset of 7,043 customer records that simulate behavioral, contractual, and financial attributes commonly found in telecom services. Three classification algorithms Logistic Regression, Random Forest, and Gradient Boosting are applied to model churn behavior. Data preprocessing includes handling missing values, encoding categorical variables, and splitting data into training and testing sets. Model performance is evaluated using accuracy, recall, and ROC-AUC, with emphasis on recall due to its importance in identifying at-risk customers. The results show that Gradient Boosting achieves the highest overall performance with an ROC-AUC of 0.84, while Logistic Regression provides relatively higher recall. Key drivers of churn include short-term contracts, higher monthly charges, and lower service engagement. However, recall remains moderate, indicating limitations in capturing complex behavioral factors. These findings suggest the need to combine predictive models with behavioral insights and highlight the importance of early customer engagement and long-term retention strategies.

This study examines the role of innovation management in learning to improve the quality of basic education, particularly in elementary schools. The background of this research is the increasing demand for adaptive, creative, and student-centered learning approaches in response to rapid educational changes in the digital era. The objective of this study is to analyze how the implementation of innovation management strategies in learning can enhance teaching effectiveness, student engagement, and overall educational quality. This research uses a qualitative descriptive method through literature review by analyzing relevant books, journals, and previous studies related to learning innovation and educational management. The findings indicate that effective management of learning innovation—such as the integration of technology, the use of active learning models, and teacher professional development—significantly contributes to improving student learning outcomes and classroom interaction. Furthermore, innovation management encourages teachers to be more creative in designing learning activities that are contextual and meaningful for students. The study implies that schools and education stakeholders need to strengthen institutional support, provide continuous training for teachers, and develop policies that encourage sustainable innovation in learning. Thus, structured innovation management in learning becomes a key factor in enhancing the quality of basic education in a competitive global era.

The rapid development of Artificial Intelligence (AI) technology requires an improvement in digital literacy and skills among students, particularly at the vocational high school level. However, many students still lack sufficient understanding of the basic concepts and practical applications of generative AI in learning. This Community Service Program (Pengabdian kepada Masyarakat/PKM) aims to enhance students’ understanding of AI technology through generative AI training at SMK Al-Muhajirin Depok. The methods used in this program include interactive lectures, demonstrations of generative AI applications, and hands-on practice sessions. The activity was attended by 30 students and supported by the school management. The results indicate a significant improvement in students’ understanding of generative AI concepts and their ability to apply the technology in a simple learning context. In addition, students gained awareness of the ethical use of AI in an appropriate and responsible manner. Therefore, this PKM activity contributes positively to improving students’ digital literacy and readiness to face technological advancements in the modern era.

This study aims to analyze the factors that influence cognitive development and the role of learning motivation in the learning process of teacher training students. Using a quantitative approach with a descriptive correlational design, this study involved 120 students from four study programs at the Faculty of Teacher Training and Education (FKIP) Galuh University, selected by purposive sampling from active students who regularly participate in lectures. The instruments used were a cognitive development questionnaire based on Piaget's theory and Bloom's taxonomy, and a learning motivation questionnaire with a Likert scale of 1–5. Data were analyzed using descriptive statistics, Pearson correlation, and simple and multiple linear regression. The results showed that cognitive development (β = 0.43, p < 0.01) and learning motivation (β = 0.37, p < 0.05) each had a significant influence on student achievement. A strong positive correlation between the two variables indicates a mutually supportive relationship. Academic environmental factors, independent learning strategies, and lecturer support were identified as the main drivers of cognitive development. These findings emphasize the need for higher education to integrate learning approaches that address students' cognitive development while simultaneously fostering intrinsic motivation to improve learning quality and future professional readiness.

This research aims to classify students’ lifestyles using the K-Nearest Neighbors (KNN) algorithm. The dataset consists of 392 high school students obtained from Kaggle, with key attributes including study hours, social media usage, Netflix viewing duration, attendance, sleep quality, internet quality, mental health, and extracurricular activities. KNN was chosen for its simplicity in distance-based classification, measured using Euclidean Distance. The data was divided into training and testing sets, then evaluated using accuracy and a confusion matrix. The results show that KNN effectively classifies students’ lifestyles into four categories: healthy, less active, at risk, and highly at risk. This classification is expected to assist educational institutions, parents, and students in understanding lifestyle patterns and their impact on academic performance and mental well-being. Furthermore, this study emphasizes the relevance of applying machine learning in education, aligned with Islamic values concerning health, discipline, and the optimal use of time.

This research aims to classify students’ lifestyles using the K-Nearest Neighbors (KNN) algorithm. The dataset consists of 392 high school students obtained from Kaggle, with key attributes including study hours, social media usage, Netflix viewing duration, attendance, sleep quality, internet quality, mental health, and extracurricular activities. KNN was chosen for its simplicity in distance-based classification, measured using Euclidean Distance. The data was divided into training and testing sets, then evaluated using accuracy and a confusion matrix. The results show that KNN effectively classifies students’ lifestyles into four categories: healthy, less active, at risk, and highly at risk. This classification is expected to assist educational institutions, parents, and students in understanding lifestyle patterns and their impact on academic performance and mental well-being. Furthermore, this study emphasizes the relevance of applying machine learning in education, aligned with Islamic values concerning health, discipline, and the optimal use of time.

This community service program aimed to improve teachers’ understanding and practical skills in preparing Classroom Action Research (CAR) proposals among senior high school teachers in Semarang City, Indonesia. Classroom Action Research is an essential component of teacher professional development, enabling educators to systematically identify classroom problems, implement pedagogical interventions, and evaluate learning improvement strategies. However, many teachers encounter difficulties in translating classroom challenges into structured research proposals. The program involved 22 teachers from public and private senior high schools in Semarang City and was implemented using a participatory and experiential learning approach integrating theoretical sessions, guided discussion, proposal-writing workshops, and direct mentoring. Training materials covered classroom problem identification, research cycles, proposal structure, formulation of objectives, intervention design, and indicators of success. Program effectiveness was evaluated through direct assessment of participants’ practical outputs. The success indicator was defined as a minimum of 90% of participants being able to prepare an initial classroom action research proposal draft correctly according to the provided guidelines. The results showed that the program successfully achieved its target, with 90.9% of participants able to produce an initial classroom action research proposal draft containing essential proposal components. Participants also demonstrated improved confidence and understanding regarding classroom-based research implementation. This program contributes a practical professional development model integrating research literacy enhancement and hands-on proposal writing practice to strengthen teachers’ reflective teaching and evidence-based instructional improvement capacity.

This community service program aimed to improve household welfare, nutritional literacy, and stunting prevention awareness through halal seafood processing training for housewives in North Semarang District, Semarang City, Indonesia. The program involved 42 participants and was implemented using a participatory approach combined with experiential learning methods, integrating theoretical sessions, practical food processing training, nutrition counseling, and basic entrepreneurial education. Program effectiveness was evaluated through participant observation and post-activity semi-structured interviews. Success indicators were determined based on participant responses, with the target that at least 80% of participants reported increased knowledge and understanding of balanced nutrition, seafood utilization, and halal-thayyib food processing principles. Findings indicated that more than 80% of participants acknowledged improved understanding of B2SA nutrition concepts, seafood-based food processing techniques, and opportunities for small-scale household entrepreneurship. Participants also demonstrated high engagement during practical sessions and expressed interest in developing value-added seafood microenterprises. This program contributes an integrated community empowerment model combining nutrition education, women’s economic empowerment, Islamic dietary values, and local resource utilization, which may serve as a replicable strategy for urban communities facing nutritional and economic vulnerabilities

Digital transformation in the public sector encourages civil servants to utilize technology not only for administrative purposes but also to improve efficiency, accuracy, and work performance. This study examines the influence of Intelligent Learning-Based Training and Digital Training Components on the Digital Productivity of civil servants at the Bureau of Economic Affairs of the Regional Secretariat of Lampung Province, with Adaptive Competence serving as a mediating variable. A quantitative approach with a non-experimental survey design was applied, and data were collected through structured questionnaires distributed to employees engaged in digital-based work activities. The data were analyzed using structural model testing procedures. The findings reveal that Intelligent Learning-Based Training does not significantly influence Adaptive Competence or Digital Productivity. Conversely, Digital Training Components positively and significantly affect both Adaptive Competence and Digital Productivity. Adaptive Competence also has a positive impact on Digital Productivity and mediates the relationship between Digital Training Components and Digital Productivity. However, Adaptive Competence does not mediate the relationship between Intelligent Learning-Based Training and Digital Productivity. These results indicate that civil servants’ digital productivity is more effectively improved through practical, relevant, accessible, and well-evaluated digital training programs than through intelligent learning systems alone. Therefore, public institutions are encouraged to develop digital training initiatives that align with employees’ daily work demands while strengthening their adaptive competence in responding to technological changes.

This community service program aimed to improve students digital marketing competencies through training on the use of Google Trends at SMKN 1 Bawen. This program aims to address the limited ability of students to utilize data-based tools for market analysis, and was attended by 90 students. A participatory training approach was implemented, consisting of interactive lectures, demonstrations, hands-on practice, and evaluation activities. The results, based on observational assessments and participant feedback, indicate improvements in students’ digital skills, particularly in trend analysis, data interpretation, and the application of insights to academic projects. Most participants demonstrated increased engagement during training sessions and reported higher confidence in using data analysis tools. In addition, students showed emerging abilities to connect trend data with market opportunities and develop project ideas accordingly. Overall, the program contributed to enhancing students’ data literacy and promoting more effective use of digital tools in learning. These findings suggest that practice-based training can support the development of data-driven competencies in vocational education.

This community service program aims to strengthen the strategic role of Posyandu (Integrated Health Post) as a center for family health education through the innovation of its educational functions. Previously, Posyandu operations in the community tended to be limited to routine basic health services, such as weight monitoring and nutritional status recording, while the crucial aspect of health education remained suboptimal. This limitation has contributed to a lack of public understanding regarding healthy lifestyles and the prevention of chronic diseases or stunting. The methods implemented in this program included problem identification through observation, health socialization and education, specialized training for Posyandu cadres to enhance their communication capacity, and direct mentoring during activity implementation. The results demonstrated a significant increase in family knowledge regarding balanced nutrition and clean and healthy living behaviors (PHBS). Furthermore, Posyandu cadres became more active and skilled in delivering health information communicatively to the community. This program demonstrates that by strengthening its educational function, Posyandu can transform into an effective and sustainable family health learning center, significantly impacting the improvement of community health standards at the frontline level.

This study aims to analyze the implementation of the Reconnecting active learning method in Islamic Religious Education (PAI) for ninth-grade students at SMP Negeri 2 Sentani and to examine its role in enhancing students’ learning interest and engagement. This research employs a descriptive qualitative approach with a case study design to gain an in-depth understanding of the learning process. Data were collected through in-depth interviews with three PAI teachers, the school principal, and three students, and were further supported by classroom observations and documentation. The data analysis technique used the Miles and Huberman interactive model, which includes data reduction, data display, and conclusion drawing. The findings indicate that the implementation of the Reconnecting method has a positive impact on students’ learning experiences, particularly in increasing their interest, active participation, and ability to relate learning materials to real-life contexts. Students become more responsive and motivated during the learning process. However, several challenges were identified, including limited instructional time and varying levels of teacher readiness in applying the method effectively. Therefore, continuous teacher training and better time management are recommended to optimize the use of this method in PAI learning

The internship program of Universitas Mayjen Sungkono in Begaganlimo Village introduced a training initiative focused on the Development of an Adaptive Learning Platform Based on D-PACK as an effort to enhance the quality of digital learning in Begaganlimo elementary schools. This training was designed to assist teachers in strengthening their ability to design and utilize interactive learning media supported by artificial intelligence (AI). Through this socialization activity, teachers were not only introduced to the use of adaptive technology but also guided to apply it in teaching practices so that the learning process becomes more effective, easier to understand, and more engaging for students. In addition, the socialization activities provided a deeper understanding of how technology can be used and integrated into teaching. Teachers not only learned to operate AI-based applications or devices, but they also gained insight into the pedagogical principles within the DPACK framework, which ensures that technology is applied appropriately. As a result, learning is expected to become easier to understand, more engaging, and capable of increasing students’ participation and motivation in the classroom.

Abstract: Lesson Study is a collaborative process where teachers identify learning problems, plan improvements, implement instruction with one teacher teaching and others observing, evaluate, revise, re-teach, re-evaluate, and disseminate results. This method serves as a reference for HOTS-based classroom instruction aimed at enhancing learning motivation, student achievement, and critical thinking. The community service was conducted at the Tadris Social Sciences Study Program, FTIK, UIN Sayyid Ali Rahmatullah, and MI Podorejo, Sumber Gempol, Tulungagung, through planning, implementation, and reflection stages. This study employed a descriptive qualitative method with a sequential design covering preparation, micro-teaching training, school mentoring, Lesson Study implementation, and comprehensive final evaluation. Observations from this Lesson Study revealed that students demonstrated critical thinking and active participation in learning. This activity is expected to foster synergy among lecturers, teachers, and pre-service teachers in designing, implementing, and evaluating teaching-learning activities, making instructional methods more creative, innovative, and adaptive to technological advancements.

The rapid development of digital technology has significantly transformed social interaction and learning processes in rural communities. Uncontrolled smartphone usage has led to low digital literacy and heightened risks of social media misuse among children and adolescents. This community service program, through the Community Service Program (KKN), aimed to improve digital literacy and establish a Learning Center (Pondok Belajar) as an educational space within the village. The methods included observation, socialization, digital literacy training, and academic mentoring for children. The results demonstrated an increase in community awareness regarding the responsible use of smartphones and enhanced learning motivation among children through the Learning Center activities. This program illustrates that the synergy between digital education and learning support can be a concrete step toward building a smart and competitive village in the digital era. The initiative highlights the importance of combining technology education with community-based programs to foster a more informed and empowered population in rural areas.