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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.  

As the center for employee competency development, BSI Corporate University (BSU) plays a crucial role in strengthening BSI’s competitiveness within Indonesia’s rapidly growing Islamic banking industry. BSU’s performance from 2021 to 2023 experienced fluctuations, indicating the need for an evaluation of factors influencing employee performance. Previous studies have also shown inconsistent findings regarding the influence of Islamic Work Ethics and AKHLAK core values on performance, thereby creating a research gap. This study aims to examine the influence of Islamic Work Ethics and AKHLAK core values on employee performance at BSU. Data were collected through a quantitative survey of BSU employees, resulting in 60 valid responses, and analyzed using SmartPLS 4.1.1.2. The research employed a descriptive quantitative approach with Partial Least Squares Structural Equation Modeling (PLS-SEM) as the analytical technique. The findings indicate that Islamic Work Ethics and AKHLAK core values have a positive and significant influence on employee performance.

The rapid development of the sea transportation industry produces a massive and complex volume of transaction data, requiring strategic management to support managerial decision-making. This research aims to implement the Executive Information System on SeaPass in order to evaluate the performance of ship ticket sales. The research method uses data visualization with a two-level drill-down mechanism, which allows the presentation of information hierarchically from general summaries to specific details. The methodological stages include needs analysis, user interface (UI) design using Figma, front-end implementation with HTML, CSS, and JavaScript, database integration, and system testing through Black Box Testing. The results showed that the SIE implementation successfully integrated operational data, including schedules, ships, and manifests, into an interactive dashboard. The two-level drill-down feature provides the ability for executives to identify operational anomalies and market fluctuations in real-time. In conclusion, the system significantly enhances executive data analysis capabilities, transforming complex transaction data into accurate strategic information, thereby supporting more precise business decision-making and adaptive to the dynamics of the marine transportation market.

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.

Digital-based learning evaluation is an important effort to improve the effectiveness and efficiency of the assessment process. However, learning evaluation practices at MTsN 2 Kota Tangerang are still dominated by conventional methods and the limited use of simple digital applications. This community service activity aims to strengthen digital-based learning evaluation practices through training on the use of the Zep Quiz application for teachers. The activity employed a participatory and applicative approach, including observation, focus group discussions, training and mentoring, and program evaluation. The training was conducted in a hybrid format and focused on introducing, developing, and analyzing learning evaluations using Zep Quiz. The results indicate that teachers improved their understanding and skills in designing and operating digital-based learning evaluations independently.Teachers also demonstrated high enthusiasm and active participation throughout the activity. Although technical challenges such as varying levels of digital literacy and limited internet access were encountered, these issues were addressed through direct mentoring. Therefore, the Zep Quiz training was effective in strengthening digital-based learning evaluation practices at MTsN 2 Kota Tangerang.

This qualitative literature review examines how generative artificial intelligence (AI) competencies are cognitively transformed into digital social entrepreneurial intentions and social venture creation. Synthesizing recent interdisciplinary studies across artificial intelligence, entrepreneurial cognition, and social entrepreneurship, the review develops an integrative framework explaining the cognitive pathways linking AI adoption to social value–oriented digital entrepreneurship. Drawing on the Entrepreneurial Event Model and the Stimulus–Organism–Response perspective, the findings reveal that generative AI acts as a cognitive stimulus that enhances opportunity recognition, creative problem-solving, perceived feasibility, and perceived desirability of social ventures. The review further identifies key cognitive mechanisms through which AI competencies support ideation, opportunity evaluation, resource mobilization, and early-stage scaling of digital social enterprises. By positioning generative AI as a form of cognitive infrastructure rather than a mere technological tool, this study advances theoretical understanding of AI-enabled social entrepreneurship and offers insights for entrepreneurship education, policy design, and inclusive digital innovation ecosystems

The cigarette industry in Dawe District, Kudus, has been the backbone of the local economy for decades. However, this heavy dependence on a single industrial sector creates economic vulnerability, especially when production fluctuations or policy changes affect the tobacco industry. An economic empowerment program through agricultural entrepreneurship skills is highly relevant in this context. Modern agriculture using wick hydroponic technology and mini Nutrient Film Technique (NFT) offers an appropriate solution because it has characteristics that are suited to the geographic and socioeconomic conditions of the Dawe community. This PKM program has proven that economic empowerment through technology and skills transfer can be an effective strategy for increasing community income. Based on the results achieved, it is recommended to replicate similar programs in other areas with similar characteristics. Furthermore, ongoing monitoring and evaluation are necessary to ensure the sustainability of the businesses pioneered by participants. Collaboration with relevant agencies and microfinance institutions can strengthen participants' access to capital for broader business development.

This study aims to identify the factors causing delays in the issuance of the Certificate of Pratique (COP) for foreign vessels and to review the standard procedures applicable at the Banten Health Quarantine through the role of PT. Bahari Tirta Jaya Banten Branch. The COP is a critical document certifying that a vessel has met health requirements and is permitted to have contact with the shore (berthing/stevedoring). The research method employed is field research through direct observation at the port and library research involving laws, regulations, and relevant port literature. The research findings indicate that delays in COP issuance are caused by several primary factors, including: (1) incomplete ship administrative documents, (2) quarantine procedures requiring additional inspections, (3) poor ship hygiene conditions, (4) limited human resources within the quarantine office, and (5) technical constraints in the quarantine information system. These delays have a significant impact on ship berthing schedules, the efficiency of loading and unloading processes, and potential financial losses for shipping companies. This study recommends the need for improved coordination between ship agents and Health Quarantine authorities, evaluation of procedural efficiency, and strengthening of information system resources to ensure smooth services within the Banten Port area.

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.  

Tuberculosis (TB) remains one of the leading infectious diseases causing morbidity and mortality in Indonesia. Limited public health literacy regarding TB symptoms, prevention, and treatment adherence contributes to delayed diagnosis and poor treatment outcomes. This community service program aimed to improve public understanding and awareness of TB through interactive educational activities conducted at the Outpatient Unit and TB Clinic of UPT Puskesmas DTP Saketi, Pandeglang Regency. The activities involved 30 participants, including TB patients and their families. Interactive counseling sessions, audiovisual media presentations, and group discussions were used to engage participants actively. Evaluation results showed a 35% increase in participants’ knowledge scores, greater awareness of the importance of completing treatment, and improved communication between patients and healthcare workers. The implementation of interactive education proved effective in enhancing TB health literacy and supporting national TB elimination efforts at the primary healthcare level.

Posyandu (Integrated Health Post) is a public health facility that plays a vital role in providing health services for toddlers and pregnant women. However, data management and reporting often face challenges, such as limited access to information and errors in data recording. Therefore, this study aims to develop a Web-Based Posyandu Payolaumbu Service Information System using the CodeIgniter Framework to improve efficiency and accuracy in data management and reporting. In the development phase, a system requirements analysis and web-based application architecture design were conducted. The system implementation uses the CodeIgniter Framework as a framework to produce a faster, more efficient, and more reliable application. Proposed features include recording health data for toddlers and pregnant women, immunization schedules, weighing, and health reports. The results show that the Web-Based Posyandu Payolaumbu Service Information System can improve efficiency in recording and reporting health data. Users, including posyandu officers, midwives, and administrators, can easily access and manipulate data in real-time. Furthermore, this system helps improve service quality by providing more accurate and complete information on toddler health. In conclusion, the implementation of the Web-Based Posyandu Payolaumbu Service Information System using the CodeIgniter Framework provides significant benefits for data management and health services at Posyandu Payolaumbu. Suggestions for further development include maximizing system utilization, developing additional features, routine maintenance, and ongoing evaluation based on user feedback. With these steps, it is hoped that this system can contribute more effectively to improving the quality of health services at Posyandu and supporting comprehensive public health efforts.

Overburden stripping operations in the coal mining industry require optimal performance of loading and hauling equipment to achieve production efficiency. This study aims to evaluate the performance of loading and hauling equipment using the Match Factor method in overburden stripping operations at PT Bumi Artlantis Raya. The results indicate that the equipment combination achieved a Match Factor of 0.85, reflecting moderate compatibility with a potential efficiency improvement of 15%. The actual productivity of Excavator 4002 reached 137.02 bcm/hour (91.35% of the 150 bcm/hour target), while Excavator 4004 exceeded the target with a productivity of 195.73 bcm/hour (130.49% of the target). In contrast, dump truck productivity remained relatively low (Mercedes dump truck: 35.58 bcm/hour; Hino dump truck: 35.40 bcm/hour), primarily due to waiting time during loading and disposal activities. Statistical analysis reveals a strong negative correlation between cycle time and productivity (R² = 0.9929). The optimal cycle time to achieve a Match Factor of 0.80 is 969 seconds, corresponding to an optimal hauling distance of 5.38–6.725 km. Although mechanical availability and physical availability were high (94–100%), the use of availability and effective utilization were relatively low due to an imbalance between loading and hauling equipment. This study concludes that improving equipment coordination, increasing bucket fill factor, enhancing haul road conditions, and implementing preventive maintenance are essential to achieving more optimal operational efficiency in overburden stripping activities.

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.

Chronic Kidney Disease (CKD) is a heterogeneous disorder that gradually affects the structure and function of the kidneys, is difficult to recover, and causes the body to be unable to maintain metabolism and fail to maintain fluid and electrolyte balance, leading to increased urea levels. Chronic kidney disease data was obtained from Kaggle, in this study a comparison was made between two classification algorithms, namely Naïve Bayes Classifier (NBC) and Random Forest because it is not yet known what algorithm is best in classifying chronic kidney disease (CKD). Both algorithms are evaluated based on performance metrics such as accuracy, precision, recall, and confusion matrix. The results of the evaluation showed that in a dataset of 400 samples, the performance  of the Naïve Bayes Classifier (NBC) algorithm obtained an accuracy of 94%, while Random Forest had an accuracy of 93%. Then in the small dataset (158 data), Random Forest got a better accuracy score with 87% compared to the Naïve Bayes Classifier (NBC) of 78%. Based on the results of the evaluation, Random Forest has a more stable performance on small datasets, while Naïve Bayes Classifier (NBC) provides higher performance on larger datasets in the context of chronic kidney disease classification.

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.

Budget efficiency is an important issue in state financial management because it is directly related to government spending priorities and their impact on public service programs. Discussions about budget efficiency policies are widespread on social media platform X, generating diverse public responses, thus necessitating an automated approach to understand public opinion trends more quickly and objectively. This research aims to analyze the sentiment of Indonesian people toward budget efficiency policies and compare the performance of the Naïve Bayes and Support Vector Machine (SVM) algorithms in classifying sentiment. The research data used 10,909 Indonesian-language tweets sourced from a public dataset, which were then processed thru the preprocessing stages including cleaning, case folding, normalization, tokenization, stopword removal, and stemming. Sentiment labeling is performed automatically using the Indonesian Sentiment Lexicon (InSet) approach to categorize data into positive, negative, and neutral sentiments. Feature extraction was performed using Term Frequency–Inverse Document Frequency (TF-IDF), and then the data was divided into training and testing sets with an 80:20 ratio. Model performance evaluation was conducted using a confusion matrix and the metrics of accuracy, precision, recall, and F1-score. The research results show that sentiment distribution is dominated by negative sentiment at 56.78%, followed by positive sentiment at 37.40%, and neutral sentiment at 5.83%. In the classification stage, SVM performed best with an accuracy of 86%, while Naïve Bayes achieved an accuracy of 74%. These findings indicate that SVM is more optimal for sentiment classification on social media text data and can be utilized to more effectively support the analysis of public response to budget efficiency policies.

Diabetes Mellitus is a group of metabolic diseases characterized by hyperglycemia resulting from defects in insulin secretion, insulin action or both. Hyperglycemia is a medical condition in the form of an increase in glucose levels beyond normal limits which is a characteristic of several diseases, especially Diabetes Mellitus, in addition to various other conditions. Diabetes Mellitus is currently a global health threat. Classification is one of the techniques of data mining that can be used to help predict the results of the classification of types of diabetes using the naïve Bayes algorithm. Testing was carried out using 5 evaluation models including rapid miner with 3 options, namely use training set, 5 Fold Cross-Validation, 10 Fold Cross-Validation, and 2 other evaluation models, namely Microsoft Excel and Python. Testing data regarding Diabetes Mellitus has high accuracy in the excel evaluation model, which is 89.00% compared to other evaluation models. Meanwhile, the lowest accuracy is the Python evaluation model which obtains an accuracy of 86.36%. The Naïve Bayes algorithm can be said to be one of the most effective algorithms, both in terms of calculations and the final results, where the test can be used as a basis for diabetes mellitus considering the accuracy results are above 85%.

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.

Managing Regional Public Service Agency (Badan Layanan Umum Daerah/BLUD) hospitals requires planning and budgeting processes that are accountable, measurable, and aligned with service performance. In practice, BLUD planning is still constrained by fragmented applications (hospital information system/SIMRS, finance, human resources, e-office, and procurement), duplicate data entry, approval delays, and limited monitoring of process compliance. This study aims to analyze requirements and design a web-based BLUD planning information system using an Enterprise Application Integration (EAI) approach through middleware to improve cross-system interoperability, data consistency, and the timeliness of executive reporting. The study adopts the Design Science Research (DSR) framework, comprising problem identification, definition of solution objectives, artifact design and development, demonstration, evaluation, and communication/report writing. The proposed system includes a unit-based budget proposal module and item management, a role-based approval workflow (RBAC) with SLA tracking, a budget ceiling (pagu) master to benchmark proposals, audit trails and report exports, and an executive dashboard integrating budget perspectives, service indicators (e.g., bed occupancy rate/BOR and patient visits), and process compliance. It also provides an integration design via middleware (ESB/message broker) supported by a canonical data model (CDM) and traceable logging (trace_id/correlation_id). Evaluation using black-box testing and API contract testing indicates that the main planning workflow operates as intended and the integration interfaces are consistently defined, providing a foundation for staged implementation and further performance evaluation.

This qualitative literature review synthesizes interdisciplinary research on how employees’ technological sensing capabilities shape generative artificial intelligence (GenAI) capabilities and innovative work behavior. Drawing on dynamic capabilities theory, microfoundations of sensing, and human–AI interaction literature, the review integrates findings from management, information systems, and innovation studies. The synthesis reveals that technological sensing—employees’ ability to identify, interpret, and anticipate emerging technologies—does not directly translate into innovation, but operates through the development of distinct GenAI capabilities. In particular, GenAI evaluation capability consistently emerges as a stronger driver of innovative work behavior than GenAI usage capability alone, as it enables critical judgment, contextualization, and creative recombination of AI-generated outputs. The review further highlights contextual moderators such as leadership support, task complexity, and organizational climate. Overall, the study advances theory by positioning individual sensing as a microfoundation of AI-enabled innovation and offers implications for organizations seeking to leverage GenAI beyond efficiency gains.