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The JKN Mobile application is an electronic-based service innovation introduced by BPJS Health to facilitate JKN participants in accessing healthcare services and managing their membership information. This study aims to explore the experiences and perspectives of outpatient patients regarding the effectiveness of the JKN Mobile online registration system at Baubau City Regional General Hospital. A qualitative phenomenological approach was employed, with informants selected through purposive sampling. Data were collected to capture the meanings of patients’ experiences in using the application. The findings show that most patients are satisfied with the practicality, speed, and flexibility of the online registration system. However, technical problems, particularly unstable internet connectivity, remain a major challenge. Furthermore, the online registration system has contributed to improved service efficiency and patient satisfaction. These findings indicate the importance of continuous evaluation and improvement of the JKN Mobile online registration system to enhance service quality and health information system development at Baubau City Regional General Hospital.

Optimizing human resource (HR) performance through a business process reengineering (BPR) approach is a crucial strategy in a competitive and dynamic industrial environment. This study qualitatively examines through a literature review how BPRs radically redesign business processes to improve productivity, quality, time efficiency, and reduce operational costs, with a focus on HR aspects such as recruitment, training, performance appraisal, and career development. The BPR theory by Michael Hammer and James Champy emphasizes the principles of results-based workflow reorganization, the integration of information technology such as ERP and AI, and the empowerment of HR to eliminate task redundancy. In the Indonesian industrial context, the integration of BPR with digital transformation supports Industry 4.0, where HR acts as a catalyst for innovation through adaptive skills and cross-functional collaboration. The study results show that BPR implementation strategies including as-is process analysis, to-be design, change management, and continuous evaluation increase employee motivation, retention, and sustainable competitive advantage. Case studies such as PT Telkom Indonesia and PT Cahaya Mega Group demonstrate efficiency increases of up to 100%. This approach not only streamlines operations but also builds an organization resilient to market and technological disruption.

This Community Intervention activity aims to optimize the identity of Ujung Piring Village through place branding, which includes creating a village logo, profile video, and designing a site plan for the village square. The main problems faced by the village are the lack of a strong visual identity to optimise its potential and structured planning of village space/facilities. The methods used in the village site plan development activity were observation, interviews, and focus group discussions (FGDs). The results of the activity showed that the village logo successfully represented local values and history symbolically; the profile video was an effective medium for public communication; and the village square site plan provided direction for the development of a functional, aesthetic public space that supports social interaction. This activity proves that place branding can strengthen village identity, increase community engagement, and open up opportunities for village economic development. Further evaluation and monitoring are needed to assess the long-term impact of the implementation of this work program.

The management of scientific works at the Faculty of Law, Universitas Jambi, is still carried out manually through physical archives and simple digital storage without an integrated information system. This condition causes limited access, slow information retrieval, and a high risk of document loss and damage. This study aims to develop a web-based scientific repository system to improve the efficiency, accessibility, and security of managing academic works. The system was developed using the Waterfall method, which consists of requirement analysis, system design, implementation, testing, and evaluation. Data collection was conducted through interviews, observations, and questionnaires involving librarians, lecturers, and students. The system was implemented using the Laravel framework with a MySQL relational database to support document storage, metadata management, access control, and search functionality. The results show that the developed repository system is able to facilitate the submission, verification, publication, and retrieval of scientific works effectively. User acceptance testing indicates that the system meets user needs in terms of usability, functionality, and accessibility. The implementation of this system contributes to improving academic information management and supports digital transformation at the Faculty of Law, Universitas Jambi.

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.  

The implementation of the One-Stop Integrated Service (PTSP) Website at the Ministry of Religious Affairs of Jambi City is part of the digital transformation of public services. This study aims to measure user satisfaction with the PTSP Website using the End User Computing Satisfaction (EUCS) method and the DeLone & McLean model. This research employed a quantitative approach using a survey method involving 100 respondents who are users of the PTSP Website. The EUCS variables consist of content, accuracy, format, ease of use, and timeliness, while the DeLone & McLean model includes system quality, information quality, and service quality. Data were analyzed using Partial Least Squares–Structural Equation Modeling (PLS-SEM) with SmartPLS 4.0 software. The results indicate that system quality, information quality, and service quality have a positive effect on user satisfaction with the PTSP Website. This study is expected to serve as an evaluation reference for improving the quality of digital public services.

The development of digital technology has encouraged the government to utilize social media as an effective means of public communication. The Ministry of Agriculture is one institution that actively uses Instagram to convey information, build a positive image, and interact with the public. This study aims to determine the Public Relations strategy in managing the Instagram social media account @kementerianpertanian. This study uses the Public Relations strategy theory from Cutlip and Center, which consists of four stages: problem identification, planning and programming, communication and action, and evaluation. The research method used is a descriptive qualitative approach, with data collection techniques through in-depth interviews and documentation of the Ministry of Agriculture's Public Relations team. The results show that the Ministry of Agriculture's Public Relations has implemented a planned communication strategy, starting from planning informative and educational content, utilizing Instagram features such as reels, stories, and live to increase interaction, to routine evaluation based on engagement data (likes, shares, comments, and saves). This strategy also adapts to social media trends and audience needs. Public Relations actively responds to user questions and comments to maintain two-way communication. However, challenges such as negative comments, limited resources, and changes in the Instagram algorithm are obstacles that still need to be overcome. Overall, the implemented strategy has successfully supported the improvement of the image and transparency of public information within the Ministry of Agriculture.

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 evaluates the Cageur Jasa Health Service Program at the Tanah Tinggi Public Health Center in Tangerang City, which was developed as an innovation to expand access to basic healthcare services through home visits. The program aims to improve service quality, strengthen family independence in maintaining health, and support the achievement of the 12 indicators of the Indonesia Healthy Program with a Family Approach (PIS-PK). The evaluation applies William N. Dunn’s six policy evaluation criteria effectiveness, efficiency, adequacy, equity, responsiveness, and appropriateness supported by theoretical frameworks from public administration, public policy, and health service innovation. This research employs a qualitative descriptive method, with data collected through observation, in-depth interviews with informants, and secondary data analysis. The findings indicate that the program’s implementation has not fully achieved its intended objectives, as evidenced by declining service coverage, several PIS-PK indicators remaining below 50%, increasing disease findings, and the persistence of maternal and infant mortality cases. Limitations in resources, coordination, infrastructure, and suboptimal promotive and preventive efforts further hinder the program’s effectiveness. Overall, the study concludes that although Cageur Jasa contributes to improving healthcare access, its implementation at the Tanah Tinggi Public Health Center remains suboptimal and requires continuous improvement.

Diabetes Mellitus is a chronic disease that requires early detection to prevent serious complications. This study aims to implement the Artificial Neural Network (ANN) algorithm with the Backpropagation method to predict the risk of diabetes. The dataset used is the Pima Indians Diabetes Dataset, consisting of 768 medical records with 8 feature attributes. This study employs the Multi-Layer Perceptron method with an architecture of 8 input neurons, two hidden layers, and 1 output neuron. Model evaluation is conducted using a Confusion Matrix to measure accuracy levels. The test results show that the model is capable of predicting diabetes diagnosis with an accuracy rate of 76.62%. Based on these results, it can be concluded that the Backpropagation algorithm is effective as an alternative method for early detection of diabetes, although further development is needed to improve the model's sensitivity to positive cases.  

The 2025 Pelita Warna Program is a form of participatory-based community service implemented at the Class II Children's Special Development Institution (LPKA) in Jakarta. This activity aims to design and implement relevant development programs for foster children using a contextual, psychosocial, and vocational skills approach. The main problems behind this activity include minimal access to development that suits the children's needs, limited infrastructure, and psychological challenges faced by foster children. The implementation method begins with observation, needs assessment, coordination with LPKA and partners, program development, and implementation and evaluation. The programs implemented include barbershop training, creative literacy, psychological assistance, and ready-to-work training. Evaluation is carried out formatively and summatively through qualitative and descriptive approaches, using indicators of involvement, attitude change, skill improvement, and foster children's work results. The results of the activity show a significant increase in the foster children's self-confidence, self-awareness, technical skills, and motivation to live independently. This program is considered successful in creating a safe and supportive space that encourages positive transformation among participants. In the future, this model has the potential to be replicated in other institutions as a form of humanistic and sustainable development

: 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.

This study discusses the role of the Kupang City Council in carrying out its supervisory function on gender-responsive maternal and child health policies, with a case study focus on Oebobo District. The background of this study stems from the high maternal and child mortality rates and the suboptimal implementation of Kupang City Regulation No. 7 of 2013. This study uses a qualitative approach with interview, observation, and documentation techniques, and involves informants from DPRD members, health workers, posyandu cadres, and community leaders. The results show that the DPRD, particularly through Commission IV, has carried out its supervisory function through regulatory support, budget allocation, program evaluation, and field visits. Some of the programs supervised include Free Nutritious Meals (MBG), strengthening Posyandu, and basic health services at Puskesmas. However, the effectiveness of supervision still faces various obstacles, including the lack of female representation in the DPRD, strong patriarchal culture, lack of socialization of local regulations, weak cross-sector coordination, and low community participation. The conclusion of this study confirms that the success of gender-responsive maternal and child health policies is greatly influenced by the capacity of the DPRD in oversight, political and budgetary support, and community involvement.

Recommender systems play a critical role in shaping user decisions across digital platforms; however, the increasing complexity of recommendation algorithms has raised serious concerns regarding transparency, trust, and accountability. This study focuses on enhancing the transparency of recommender systems by integrating Explainable Artificial Intelligence (XAI) techniques within a MovieLens-based recommendation framework. The primary problem addressed is the opacity of conventional recommendation models, which limits user understanding of why certain items are recommended and may reduce trust, perceived fairness, and system acceptance. Accordingly, the main objective of this research is to design and evaluate a hybrid explainable recommender system that balances predictive accuracy with human-understandable explanations. The proposed approach combines Matrix Factorization, feature-importance-aware neural networks, and knowledge graph embeddings to construct a robust recommendation model. To enhance explainability, multiple XAI strategies are integrated, including model-agnostic methods (LIME, SHAP, and CLIME), argumentation-based explanations, and context-aware personalized explanations. A comprehensive evaluation framework is employed, incorporating algorithmic metrics (accuracy, fidelity, robustness, counterfactual consistency, and fairness) alongside human-centered evaluations measuring trust, transparency, cognitive load, and perceived usefulness. Experimental results demonstrate that the knowledge graph–enhanced hybrid model achieves superior recommendation accuracy compared to baseline approaches. Moreover, context-aware explanations consistently outperform other methods in terms of fidelity, robustness, and user-perceived transparency, while argumentation-based explanations are found to be the most persuasive. CLIME offers a strong balance between technical stability and interpretability. The findings indicate that no single explainability technique is universally optimal; instead, hybrid and adaptive explanation strategies are most effective. In conclusion, this study confirms that human-centered, context-adaptive XAI significantly improves transparency and user trust in recommender systems, highlighting explainability as a fundamental component rather than an optional enhancement.

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.

As a high-value crop, potatoes necessitate balanced nutrient management for optimal growth and yield. This research aimed to assess how varying applications of NPK 20-20-10 fertilizer influenced potato growth, yield, tuber quality, agronomic efficiency, and economic viability within tropical climates. The experimental setup involved a randomized complete block design, incorporating four replications across seven distinct treatments: a control, a standard inorganic fertilization regimen, and NPK 20-20-10 applied at 0.50, 0.75, 1.00, 1.25, and 1.50 times the suggested dosage. The findings indicated that applying NPK 20-20-10 significantly enhanced several parameters, including plant height, branch count, tuber count, tuber weight, and overall yield components, when contrasted with the control group. Notably, the 1.25 times recommended dose demonstrated superior performance, leading to a 34.9% increase in tuber number and a 68.6% rise in tuber weight compared to the control. Agronomic effectiveness scores surpassed 100 for dosages ranging from 0.75 to 1.50, with the 1.25 dose registering the peak value. Economic evaluations confirmed the profitability of all NPK treatments, and the 1.25 dose yielded the most favorable R/C ratio and a net profit of IDR 29,053,400. Consequently, the recommended application for potato cultivation is 675 kg/ha of NPK 20-20-10, distributed in three equal parts at planting, four weeks post-planting, and six weeks post-planting. Thus, these results underscore that NPK 20-20-10, when applied at 1.25 times the recommended rate, presents an agronomically effective and economically sound strategy for sustainable potato farming in tropical settings.

The RIGEM community service program was carried out with the aim of strengthening character education for early childhood through the use of children’s songs in TK B. The background of this activity stems from concerns about the increasing exposure of children to adult songs and content, which may influence their behavior and moral values. Children’s songs were chosen as the main medium because they have simple melodies, easy-to-remember lyrics, and contain positive values that can stimulate children's cognitive, affective, and social development. This study employs a descriptive qualitative approach using the CIPP evaluation model (Context, Input, Process, Product) to assess the effectiveness of the program. Data were collected through observations, interviews with teachers and parents, and documentation of activities. The results show that the children’s songs introduced particularly those containing messages about politeness, cooperation, and responsibility were effective in improving children's understanding of moral values and encouraging positive behavioral changes. Children became more accustomed to using polite expressions, showing empathy, and cooperating during classroom activities. Teachers and parents played an important role in reinforcing these habits both at school and at home. Overall, the RIGEM program proved to be an effective, enjoyable, contextual, and easily implemented effort to foster character development in early childhood education.

This research aims to develop GeoQuest, an interactive learning media integrated with Artificial Intelligence (AI), to enhance the understanding of trigonometry concepts among Grade XI students at SMAN 1 Percut Sei Tuan. The development follows the Research and Development (R&D) approach using the ADDIE model, which consists of Analysis, Design, Development, Implementation, and Evaluation. The product integrates AI-based features such as adaptive practice questions, automated feedback, and dynamic visualizations of trigonometric graphs. Data were collected through expert validation, student response questionnaires, and learning outcome tests. The results show that the media meets the criteria of validity, practicality, and effectiveness. Material experts and media experts rated GeoQuest as "very valid," while students responded positively to its ease of use and engaging interface. Learning outcome tests indicate a significant improvement in students’ understanding of trigonometry after using the AI-based GeoQuest media. Thus, GeoQuest is proven to be a reliable and effective digital learning tool to support trigonometry learning.

A research about study of sandstone slope stability using the Bishop Simplified method in Uu Samarinda has been conducted. This study was conducted to analyze the rebound number values of sandstone slopes, evaluate their stability level, and calculate the safety factor using the Bishop method. The results showed that the rebound number values were 22.34 at point 1, 19.83 at point 2, and 18.07 at point 3. The Uniaxial Compressive Strength (UCS) values at the observation points were 1.90 MPa, 1.62 MPa, and 2.21 MPa, respectively. Geological Strength Index (GSI) evaluation indicated a rating of 80–85, demonstrating intact/massive rock structure, fresh and unweathered rock surfaces, and very good rock quality. Based on the Bishop method analysis, the slope factor of safety in 6.525  with a probability of failure is 0.000%, indicating that the sandstone slope in Ulu Samarinda is highly stable even under external pressures such as heavy rainfall or minor earthquakes. This study provides crucial information on the mechanical characteristics and stability of sandstone slopes in ulu Samarinda, which can serve as a reference for technical planning, geotechnical risk mitigation, and the sustainable development of safe areas.

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