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The Community Partnership Program (PKM) at Rumah Kopi Banjarsengon (RKB) aims to improve product quality and coffee marketing according to consumer needs. Key activities include production observation, assistance with laboratory testing for nutritional value and shelf life, food safety education, digital marketing training, and the delivery of a 5 kg roasting machine. These efforts have successfully increased production capacity and product quality standards through informative labels. Digital marketing has seen significant progress, with increased social media engagement and broader market reach. Consumer satisfaction surveys have risen from 80 to 97 on a scale of 100. Overall, this PKM program not only supports sustainable coffee business development but also contributes to improving partner competency and strengthening product competitiveness in both local and export markets. RKB is expected to consistently implement digital marketing strategies and product innovation for long-term success. Going forward, RKB is expected to continue implementing digital marketing strategies and product innovation to ensure sustainable business success.

Developmental Dysplasia of the Hip (DDH) in children often results in joint stiffness, muscle weakness, limited range of motion, and gait impairment following surgical management and immobilization. Early physiotherapy intervention is essential to restore hip function and walking ability. This study aimed to evaluate the effectiveness of active exercise on functional walking in outpatient children aged 3–5 years after DDH surgery. A case study design was conducted on three female patients who completed a five-week rehabilitation program consisting of ten active exercise sessions, including hip strengthening, mobility training, and balance exercises, combined with a home program. Outcome measures included hip range of motion, Manual Muscle Testing, and Modified McKay and Harris Hip Score assessed at baseline, mid-intervention, and post-intervention. The results showed improvements in hip range of motion, particularly in flexion, extension, abduction, adduction, and external rotation, while internal rotation improved but did not reach normal values. Muscle strength increased from grade 1–2 to grade 3 in most hip muscle groups. Functional walking scores improved from poor to good and excellent, with patients achieving independent, symmetrical gait without pain. These findings indicate that active exercise is an effective rehabilitation approach to reduce impairments and enhance functional walking in children after DDH surgery. The study highlights the importance of structured physiotherapy and caregiver involvement to optimize recovery and participation in daily activities.

Employee performance is an important factor in supporting the success of public services, especially in government agencies that deal directly with the public. Efforts to improve employee performance need to be supported by the implementation of good work discipline and the implementation of job training that is appropriate to employee needs. This study aims to analyze the effect of work discipline and job training on employee performance at the Regional Tax Collection Service Unit (UPPPD) of West Jakarta. This study uses a quantitative method, data collection techniques are carried out by distributing questionnaires to employees with a population of 78 respondents. Sampling uses a total sampling technique. Data analysis was carried out using SPSS version 26 through multiple linear regression tests, t-tests, F-tests, and coefficient of determination (R²) tests. The results of the study indicate that work discipline has a positive and significant effect on employee performance with a significance value of 0.000 <0.05 and a calculated t-value of 11.577. Job training also has a positive and significant effect on employee performance with a significance value of 0.000 <0.05 and a calculated t-value of 11.368. Simultaneously, work discipline and job training significantly influence employee performance, with a calculated F-value of 132.931 and a significance level of 0.000 < 0.05. The coefficient of determination indicates that these two variables explain 78.0% of the variation in employee performance (R² = 0.780), while the remaining 22.0% is influenced by other variables outside this study.

This community service activity was motivated by the limited capacity of village officials in providing government administration services, particularly in managing correspondence, archiving documents, and implementing standard operating procedures for services. This condition has resulted in the suboptimal quality of public services at the village level. The objective of this activity was to improve the capacity and competence of village officials in providing village government administration services to be more effective, efficient, and accountable. The method used was a participatory approach through the stages of problem identification, joint planning, regulation socialization, technical training, service simulations, and implementation assistance. The subjects of the activity were officials from Kadur Village, North Rupat District, Bengkalis Regency. The results of the activity showed an increase in the officials' understanding and skills in administrative management, the development of a more standardized administrative document format, and a growing collective awareness of the importance of orderly administration as part of good governance. In addition, internal leadership initiatives emerged that encouraged sustainable change within the village government environment. Overall, this activity had a positive impact on improving the quality of village administration services and was the first step towards a more professional transformation of village governance.

Effective communication through the SBAR (Situation, Background, Assessment, Recommendation) method is a crucial component in improving patient safety and the quality of nursing care. However, nursing students often fail to systematically implement SBAR communication during clinical practice. This community service project aims to improve students' SBAR communication competency through mentoring-based training with an experiential learning approach at RSU Haji Medan. The method used was a pretest–posttest design without a control group involving 15 nursing students. The intervention was carried out through the stages of reinforcing the SBAR concept, direct mentoring in the treatment room, and observation- and reflection-based evaluation. Competency was measured using an observation sheet covering the aspects of Situation, Background, Assessment, Recommendation, communication clarity, and self-confidence. The results showed an increase in the average score from 65.6 in the pretest to 83.8 in the posttest, with an average increase of 18.2 points and all participants experienced an increase (100%). These findings indicate that experiential learning-based mentoring is effective in improving students' SBAR communication competency. In addition to improving individual skills, this program also contributes to building a culture of professional communication and supporting patient safety in the hospital environment

This study evaluates the implementation of spot-check testing for road preservation works in Manado City, specifically on the Kairagi–Mapanget segment, the Manado City–Wori boundary, and the Liwas Terminal Access Road, to identify existing problems and assess the effectiveness of engineering-based mitigation efforts. The objective is to examine construction quality compliance with technical specifications and identify operational challenges encountered on site. The methodology encompasses the measurement of asphalt layer thickness and density via a core drill, the examination of contract documents, the analysis of laboratory test results, and the execution of field observations. The findings show that most samples meet the required standards, although several locations require corrective action. Challenges arise from weather conditions, heavy traffic, equipment limitations, and the need for adaptation to updated technical regulations. Operational mitigation strategies, staff training, the use of core drill technology, and digital documentation were found to enhance accuracy and efficiency in evaluation. The research demonstrates the value of stakeholder collaboration and capacity building in supporting quality control for road preservation works. A multidimensional approach is effective in resolving technical and operational complexities in urban road projects.

This study aims to determine the leadership strategies of school principals in improving the performance and discipline of teachers at Madrasah Ibtidaiyah Salafiyah Syafiiyah Hadirul Ulum Tasikrejo Pemalang, Central Java, as well as to identify the supporting and inhibiting factors when these leadership strategies are implemented. This study uses field research with a descriptive approach. The data collected was verbal data or words rather than numbers. The location of this research was at MI Salafiyah Syafi'iyah Hadirul Ulum Tasikrejo, Pemalang Regency. The informants in this study were the principal and classroom teachers. The data collection techniques used were observation, interviews, and documentation. The results of the study found by the researcher regarding the principal's strategy in improving teacher performance and discipline at MI Salafiyah Syafi'iyah Hadirul Ulum Tasikrejo, Pemalang Regency, are as follows: (a) conducting supervision (b) setting an example (c) motivating teaching staff (d) providing adequate facilities and infrastructure (e) providing opportunities to attend training or education (f) conducting evaluations. Meanwhile, the supporting and inhibiting factors are divided into several parts. First, there are two supporting factors, namely (a) a sense of togetherness and kinship within the school environment, and (b) the principal's commitment to improving school quality. Meanwhile, there are two inhibiting factors, namely (a) the suboptimal time management of teachers and (b) the work environment. Based on the results of this study, it can be concluded that the principal's leadership strategy plays a very important role in improving the performance and discipline of teachers at MI Salafiyah Syafi'iyah Hadirul Ulum Tasikrejo, Pemalang Regency. The implementation of appropriate strategies, supported by a sense of togetherness and the principal's commitment, can have a positive impact on teacher professionalism. However, there are still several inhibiting factors that need attention so that the implementation of leadership strategies can run more optimally.

The increasing complexity and scale of modern network traffic driven by IoT and cloud-based infrastructures have made accurate intrusion detection a critical challenge. Conventional network intrusion detection systems (NIDS) and many deep learning–based approaches struggle to reliably detect minority and stealthy attacks due to severe class imbalance and limited discrimination of subtle traffic patterns. To address these limitations, this study proposes a hybrid CNN–RBF–Attention framework for network intrusion detection. The proposed model integrates three complementary components: (i) a convolutional neural network for hierarchical feature extraction from network flow data, (ii) a radial basis function (RBF) network for localized nonlinear classification using prototype-based decision regions, and (iii) an attention mechanism that adaptively weights RBF activations to emphasize discriminative traffic patterns. SMOTE is applied exclusively to the training data to mitigate class imbalance. The framework is evaluated on the widely used CICIDS2017 and CICIDS2018 benchmark datasets in both binary and multiclass settings, using recall, precision, F1-score, confusion matrices, and ROC analysis. Experimental results demonstrate that the proposed hybrid model consistently outperforms standalone CNN and RBF baselines, particularly in terms of recall and F1-score. On the CICIDS2018 dataset, the model achieves 99.81% accuracy and 99.81% F1-score in binary classification, and 99.54% accuracy and 99.54% F1-score in multiclass classification. On CICIDS2017, it achieves 98.12% accuracy and 98.12% F1-score in binary classification, and 98.92% accuracy and 98.92% F1-score in multiclass classification. Confusion matrix and ROC analyses further show strong class separability and reliable performance in low–false-positive-rate regions, which is critical for real-world IDS deployment. These results confirm that combining deep hierarchical feature learning, localized prototype-based classification, and attention-guided refinement yields a robust, operationally reliable intrusion detection framework for highly imbalanced network environments.

The integration of Artificial Intelligence (AI) into precision agriculture has significantly improved plant disease recognition; however, many existing deep learning models remain computationally expensive and feature-redundant, limiting their deployment on low-power and edge devices. To address these limitations, this study proposes a lightweight framework for maize leaf disease recognition based on serial deep feature extraction, dimensionality reduction, and machine-learning–based classification. A pre-trained MobileNetV2 network is employed as a fixed feature extractor to obtain discriminative visual representations, while Principal Component Analysis (PCA) is applied to reduce feature dimensionality by approximately 76%, retaining 95% of the original variance and improving computational efficiency. The compressed features are subsequently classified using a Radial Basis Function Support Vector Machine (RBF-SVM), optimized via grid search and cross-validation. Experiments conducted on a four-class maize leaf disease dataset (Northern Leaf Blight, Common Rust, Gray Leaf Spot, and Healthy), with class imbalance handled during training, demonstrate that the proposed MobileNetV2–PCA–SVM pipeline achieves 97.58% accuracy, 96.60% precision, 96.59% recall, and 96.59% F1-score, outperforming the DenseNet201 + Bayesian-optimized SVM baseline (94.60%, 94.40%, 94.40%, and 94.40%, respectively). This improvement corresponds to a 2.98% accuracy gain, a 55% reduction in error rate, an 86% reduction in model parameters (20.31M to 2.75M), and an 85% reduction in model size (81 MB to 12 MB). These results indicate that the proposed framework provides a compact and efficient solution with strong potential for deployment in resource-constrained agricultural environments.

Students with deaf disabilities often have difficulty understanding verbal instruction, but have significant advantages in visual and motor aspects. This service activity aims to apply multisensory methods through Suminagashi craft art training (cloth marbling) to improve students' fine motor skills and creativity at SLB B Yakut Purwokerto. The implementation method uses a descriptive qualitative approach with a "see-learn-do" strategy involving 20 high school students. The learning process integrates visual stimulation through structured demonstrations and tactile stimulation through the exploration of material textures. The results of the activity showed that the multisensory approach was effective in bridging the barriers of deaf students with disabilities in communicating due to hearing limitations. Students demonstrate a high level of visual focus and are able to replicate marble motif making techniques with precision without relying on complex verbal explanations. The combination of cue instructions and direct touch experiences has been proven to minimize miscommunication and improve understanding of abstract concepts of the material. This activity recommends the use of visual-tactile strategies as an adaptive inclusive learning method to support the vocational independence of deaf people.          

This study presents a comprehensive comparative analysis of four traditional machine learning algorithms Decision Tree, Random Forest, K-Nearest Neighbors, and Support Vector Machine for Android malware detection using the preprocessed TUANDROMD dataset comprising 4,465 instances and 241 features representing both static and dynamic application characteristics. Motivated by the limitations of conventional signature-based and hybrid detection methods, especially in managing imbalanced datasets and detecting emerging malware variants, the study employed SMOTE to ensure balanced training data and fair model evaluation. The dataset was divided into 80% training and 20% testing subsets, and models were assessed using key performance metrics including accuracy, precision, recall, F1-score, and ROC AUC. The findings revealed that the proposed Random Forest model outperformed the other classifiers, achieving an accuracy of 0.993, precision of 0.992, recall of 1.000, F1-score of 0.996, and a near-perfect ROC AUC of 0.9998 surpassing state-of-the-art approaches. These results affirm the superior predictive capability, consistency, and robustness of the Random Forest algorithm in Android malware detection. The study concludes that base models, when integrated with class-balancing techniques, provide reliable and efficient malware detection across imbalanced datasets. For future research, the study recommends exploring advanced hybrid or ensemble frameworks that integrate Random Forest with deep learning architectures or other meta-heuristic optimization techniques to further enhance detection accuracy, adaptability, and resilience against rapidly evolving Android malware threats.

This study presents a Graph-Augmented Isolation Forest (GAIF), an unsupervised anomaly-detection framework for analyzing mobile user behavior. The proposed framework represents users and behavioral attributes as a user–feature bipartite graph, enabling the capture of relational dependencies that are not explicitly modeled in conventional vector-based approaches. Low-dimensional user representations are learned through Node2Vec and Graph Sample and Aggregate (GraphSAGE), and the resulting embeddings are subsequently processed by an Isolation Forest to produce anomaly scores. Experiments are conducted on a Mobile Device Usage and User Behavior dataset comprising 700 user profiles derived from application-level behavioral indicators. The dataset is treated as a behavioral abstraction rather than as a malware classification benchmark. A consistent 80:20 stratified train–test split is employed, with all learning-capable operations restricted to the training data to mitigate information leakage. Detection performance is evaluated post hoc using precision, recall, F1-score, and area under the curve (AUC) metrics. Under the evaluated setting, GAIF achieves an F1-score of 0.94 and an AUC of 0.97, demonstrating improved anomaly detection effectiveness relative to representative unsupervised baseline methods. These results are obtained on a static, proxy dataset and should not be interpreted as evidence of real-time deployment capability. Model interpretability is supported through post-hoc Uniform Manifold Approximation and Projection (UMAP) visualizations of the learned embeddings, providing structural insights into anomalous user behavior. Overall, the findings indicate that integrating graph-based representation learning with isolation-based anomaly scoring constitutes a computationally efficient approach for unsupervised mobile user behavior anomaly detection within the scope of this study.

This study aimed to analyze the effect of parent empowerment in optimizing tummy time on head control in infants aged 0–6 months at community health posts in Jatisampurna, Bekasi. Early motor development requires adequate sensory–motor stimulation, yet many parents have limited knowledge and practice of prone positioning activities. A quasi-experimental one-group pretest–posttest design was employed involving nine healthy infants selected through purposive sampling. Parents received education and practical training on tummy time and were instructed to implement a home program for two weeks with a minimum duration of 30 minutes per day. Head control was assessed before and after the intervention using the Head Control Scale across prone, supine, pull-to-sit, and supported sitting positions. The results demonstrated improvements in head control scores in all positions, with the most notable gains observed in infants who had lower baseline abilities. The findings indicate that repeated prone stimulation enhances postural control through increased neck and trunk muscle activation and improved sensory–motor integration. Parent empowerment also increased knowledge, adherence to home stimulation, and active participation in child development monitoring. This community-based intervention shows potential as a promotive and preventive strategy to support early motor development through family-centered approaches at primary health services.

Community service is one of the pillars of the Tri Dharma of Higher Education aimed at implementing academic knowledge in real community life. One form of educational community service is scientific writing training for students. This activity was motivated by the low ability of Madrasah Aliyah students to write scientific papers, particularly in understanding academic structure, scientific language, citation techniques, and reference writing. This community service activity aimed to improve the knowledge, skills, and academic attitudes of students at Madrasah Aliyah Nurul Falah Sangiang, Tangerang City, in writing scientific papers in accordance with academic standards. The method used was a participatory and educational approach through interactive lectures, discussions, and writing practice with mentoring. The results showed an improvement in students’ understanding of scientific writing concepts and structure, enhanced skills in developing outlines and introductory sections, and the growth of positive academic attitudes such as honesty and responsibility in academic writing. This activity contributes significantly to improving students’ academic literacy and supports the development of a sustainable academic culture within the madrasah environment.

Dementia is a progressive neurocognitive disorder often accompanied by behavioral and psychological symptoms such as agitation, anxiety, and depression. Pharmacological treatments provide only modest benefits while introducing significant risks, which highlights the need for safer, non-pharmacological alternatives. This literature review examines the role of virtual reality in dementia care, with a focus on its integration with therapies such as music, reminiscence, sensory stimulation, and cognitive training. Evidence from prior research suggests that virtual reality can enhance cognitive functions, reduce symptoms, and improve emotional well-being while also strengthening patient–caregiver interaction. However, challenges related to usability, accessibility, cost, and long-term effectiveness continue to limit adoption. Gaps in research, including limited cultural diversity, inconsistent reporting of intervention design, and a lack of large-scale longitudinal trials, emphasize the need for future work exploring cross-cultural feasibility and AI-driven personalization. Overall, virtual reality represents a promising and evolving non-pharmacological intervention that has the potential to transform dementia care by improving quality of life and reducing reliance on medication.

This study employed a quantitative method with a correlational research design to examine the relationship between family social support and self-confidence among Generation Z individuals born between 1997 and 2012. A total of 205 respondents participated in this study. Data were collected using two Likert-scale instruments: the Family Social Support Scale (60 items; Cronbach’s Alpha = 0.980) and the Self-Confidence Scale (58 items; Cronbach’s Alpha = 0.939), both demonstrating excellent reliability. Assumption testing indicated that normality and linearity were not met; therefore, the Spearman’s Rho non-parametric correlation test was applied. The results revealed a significant positive relationship between family social support and self-confidence (r = 0.429, p < .001), indicating that higher levels of perceived family support are associated with higher levels of self-confidence among Generation Z. Most respondents reported high levels of family social support (49%) and self-confidence (65%). Additionally, two items on the family social support scale and four items on the self-confidence scale were identified as having relatively lower scores within the moderate category, suggesting potential topics for future training interventions.  

Tawiri Village, Teluk Ambon District, possesses non-timber forest product (NTFP) potential, including patchouli (Pogostemon cablin Benth.) as a high-value essential oil producer. Patchouli cultivation development faces limitations in farmers' capacity regarding technical cultivation, post-harvest handling, and product downstream processing. This community service activity aims to enhance the Citra Aroma Forest Farmer Group (KTH) capacity in sustainable patchouli cultivation. Activities were conducted from September to November 2025 using a participatory knowledge transfer approach based on the Knowledge Transfer Framework, encompassing awareness, acquisition, application, and knowledge utilization-evaluation stages. Implementation included socialization, technical training, field demonstrations, and cultivation mentoring. Results demonstrate increased knowledge and skills among KTH members in seedling selection, land preparation, planting techniques, and patchouli maintenance. The main output is a Pocket Book on Sustainable Patchouli Cultivation SOP integrating scientific knowledge with local practices. This activity contributes to strengthening farmer group institutional capacity, developing NTFP-based entrepreneurship, and supporting the achievement of SDGs 1 and SDGs 8.

The phenomenon of student brawls has become a source of pride for those with violent tendencies, especially among students. As a result, the phenomenon of student brawls in Indonesia has recently become commonplace. In fact, these brawls have shifted from mere physical fights to violent acts connected directly to social media (live streaming). SMAN 1 Kersana, as a school that is concerned about this phenomenon, has taken anticipatory steps to prevent its students from becoming victims or perpetrators of violence or bullying. One of these measures is by holding In-House Training (IHT) attended by all teachers and staff at the school. After the IHT, there are also follow-ups as concrete steps from the implementation of IHT to develop programs for preventing and handling violence. An understanding of the laws related to acts of violence is mandatory for all parties involved. Furthermore, synergy between all parties, between the school, the school committee, and parents of students is one of the keys to preventing and handling violence at SMAN 1 Kersana.

Low financial literacy, weak sharia-based governance, and limited business legality remain key barriers to the sustainability of Micro, Small, and Medium Enterprises (MSMEs). These constraints reduce managerial capability, restrict access to formal financing, and hinder business growth. This study developed an integrated mentoring model combining financial literacy, sharia governance, and business legalization using a Participatory Action Research (PAR) approach. 25 MSMEs in Brebes Regency participated in four stages: needs assessment, training, mentoring, and evaluation. Data were collected through pre- and post-tests, bookkeeping observations, and legality checklists. Results showed significant improvements: financial literacy scores increased from 52 to 84 (61.5%), bookkeeping adoption rose from 20% to 88%, and Business Identification Number (NIB) ownership increased from 32% to 84%. Average monthly turnover also grew by 33%. These findings indicate that participatory and practice-based mentoring effectively enhances knowledge, behavior, and economic performance. The model offers a scalable strategy for strengthening sustainable and ethical MSME management.

Private vehicles are a frequently used mode of transportation because they are considered more practical. However, using private vehicles carries several risks, such as traffic accidents due to drivers losing focus on the road due to other activities, such as making calls on smartphones, drinking, or operating the radio. Approximately 90% of accidents are caused by human error. Convolutional Neural Network (CNN) is a type of neural network commonly used on image data. CNN is often used for image classification due to its high performance and accuracy. Therefore, this study aims to analyze the performance of CNN for the classification of distracted driving activities. The results show that the CNN model is able to effectively classify images of distracted driving activities, with an accuracy of approximately 99% across all datasets and across all input image size variations. Furthermore, the results of this study also show that differences in right-hand and left-hand drive datasets do not significantly affect model accuracy. Variations in input image size also do not significantly affect model accuracy, but do affect the training duration.