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Advances in information technology have changed the way humans obtain and manage information, including in the world of education. School websites have become an important medium for conveying academic, administrative, and school activity information quickly and efficiently. However, not all educational institutions are able to optimize the functions of their websites. This study was conducted to determine user satisfaction with the website of SMP Negeri 5 Kota Jambi using the Webqual 4.0 model and Importance Performance Analysis (IPA). The research method used was a descriptive quantitative approach with data collection through the distribution of questionnaires to 291 respondents from a total population of 1,065 students. The analysis was conducted by measuring the three main dimensions of Webqual 4.0, namely usability quality, information quality, and service interaction quality, then using IPA to map service improvement priorities. The results showed that most users were satisfied with the quality of the website, particularly in  terms of ease of use and service interaction. However, the timeliness of information updates and the responsiveness of the display on mobile devices still needed improvement. Recommendations for improvement focused on the dimensions in the "Concentrate Here" quadrant of the IPA analysis.

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.

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.

In the era of globalization and intensifying market competition, strategic management has become a crucial instrument for organizations to maintain a competitive advantage. This study aims to comprehensively examine the role of strategic management in improving organizational performance based on previous research findings. The method used is a literature review with a qualitative-descriptive approach through the collection, evaluation, and synthesis of scientific articles from reputable national and international journals published between 2021 and 2026. The research findings indicate that the stages of strategic management, including strategy formulation, implementation, and evaluation, contribute significantly to improving organizational performance in both financial and non-financial aspects. Determinant factors such as strategic leadership, resource mobilization, and organizational culture alignment are proven to strengthen organizational effectiveness in facing environmental uncertainty. Furthermore, strategic management plays a vital role in building organizational resilience to adapt to dynamic external changes. The implications of this study emphasize the importance for managers and organizational leaders to implement strategic management practices consistently and integratedly to ensure long-term survival and success. The results of this literature synthesis provide a theoretical framework for practitioners in optimizing organizational strategic capabilities amidst complex global challenges.

Freshwater fish diseases significantly affect aquaculture productivity and economic sustainability, while accurate visual classification remains challenging due to interclass similarity and image variability. This study presents a comparative evaluation of three deep learning architectures—DenseNet201, ResNet50, and EfficientNetV2-S—using a stepwise optimization strategy combined with Gradient-weighted Class Activation Mapping (Grad-CAM) for freshwater fish disease classification. Models were trained through three phases: baseline, optimized, and fine-tuned. Performance was evaluated using accuracy, precision, recall, F1 score, Matthews correlation coefficient (MCC), Cohen’s kappa, and per-class ROC–AUC. Results show consistent performance improvement across all architectures, with EfficientNetV2-S achieving the highest accuracy (97.14%), followed by ResNet50 (96.11%) and DenseNet201 (94.40%). High ROC–AUC values (>0.98) indicate strong discriminative capability. Grad-CAM analysis confirms that all optimized models focus on biologically relevant lesion regions, enhancing model transparency and reliability.

Indramayu mango is a seasonal fruit that is highly favored due to its delicious taste and high nutritional content. However, high mango production is often not supported by adequate post-harvest facilities, particularly in terms of fruit ripeness classification. Currently, mango ripeness classification is still performed manually, which tends to be subjective and inconsistent. To address this issue, this study proposes a ripeness detection system for Indramayu mangoes by integrating the TGS2602 gas sensor and the YOLOv11 algorithm based on image processing. The TGS2602 sensor is used to detect ethylene gas emitted by ripe mangoes, while YOLOv11 is employed for visual image analysis of the fruit. This study aims to evaluate the system’s performance in classifying ripe and unripe mangoes, as well as analyze the integration between the gas sensor and the object detection model. The test results show that the TGS2602 sensor can detect increased ethylene gas concentration in ripe mangoes, while YOLOv11 demonstrates high accuracy in detecting mangoes based on visual images, with precision and recall close to 1.0. The system was also tested under various lighting conditions, including dark environments, and still performed well, although with a slight decrease in accuracy under low-light conditions.

Heart disease is the leading cause of death in Indonesia and globally, necessitating an early screening system that is both accurate and clinically trustworthy. Although XGBoost demonstrates high predictive performance, its black-box nature undermines clinical trust, while low recall risks missed diagnosis an unacceptable consequence in population screening, especially in middle-income countries with limited healthcare resources. This study aims to develop a sensitive, transparent, and implementation-ready heart disease screening framework through the integration of SHAP-based Explainable AI. The CDC's Indicators of Heart Disease dataset (319,795 samples) was processed according to WHO/CDC standards, followed by class imbalance handling, hyperparameter optimization using RandomizedSearchCV, evaluation based on metrics sensitive to minority classes (AUC, recall, F1-score, AUC-PR), and threshold tuning to maximize recall. The baseline model showed a very low recall of 12.18%. After optimization and threshold tuning at 0.10, the model achieved recall >96% (96.79%) with a G-mean of 0.7477, supported by SHAP interpretation stability and the ability to capture non-linear interactions between advanced age (AgeCategory_WHO) and poor general health (GenHealth). SHAP analysis confirmed the alignment of dominant features with medical evidence, and its visualizations provide transparent explanations for healthcare professionals indicating its potential implementation as an interpretable clinical decision support system.

The rapid growth of e-commerce in Indonesia, particularly the Shopee platform, has generated a large volume of user reviews on the Google Play Store, which can be analyzed to understand consumer sentiment. This study aims to compare the performance of the Support Vector Machine (SVM) and Random Forest (RF) algorithms in binary sentiment classification (positive and negative) on Shopee reviews, as well as to statistically test the significance of their differences using One-Way ANOVA. A total of 400,498 reviews were collected via web scraping, preprocessed through text normalization, tokenization, and Indonesian language stemming, and then feature-extracted using TF-IDF and Count Vectorizer. Evaluation results show that SVM achieved an accuracy of 91.77%, precision of 91.49%, recall of 91.77%, and F1-Score of 91.56%, while RF achieved an accuracy of 90.07%, precision of 91.68%, recall of 90.07%, and F1-Score of 90.55%. ANOVA confirmed that the performance difference between the two algorithms is statistically significant (p-value = 0.0007) with a large effect size (η² = 0.1815). Therefore, SVM is recommended as a more optimal and consistent algorithm for automated sentiment analysis of Indonesian e-commerce reviews, while also providing a replicable methodological framework for similar future research.

Hypertension is a major non-communicable disease, particularly challenging in regions with extensive service areas. Community health volunteers are essential for prevention and management through blood pressure measurement. Existing training programs focus primarily on knowledge, highlighting the need to integrate cognitive learning with small-group skills practice to enhance practical competencies and community-based hypertension control. A quasi-experimental design with a pretest–posttest design was conducted to evaluate the effectiveness of combined lecture and small-group training. Knowledge was assessed before and after training, while skills were evaluated post-intervention. Thirty volunteers from the Public Health Center Selakau participated. The results showed that knowledge of blood pressure measurement improved significantly, with pretest scores of 74.67 ± 16.34 rising to posttest scores of 90.00 ± 10.50 (p < 0.005). Posttest evaluation of practical skills showed a mean score of 80.93 ± 13.35, indicating proficient performance in most assessed items. Combined lecture and small-group training effectively enhanced both knowledge and practical skills of community health volunteers in blood pressure measurement. Integrating cognitive learning with hands-on practice strengthens theoretical understanding and field competencies, supporting more effective community-based hypertension control programs.

This study aims to analyze the influence of job satisfaction on employee performance at Warung Makan Ayam Penyet Pelem Asri, Boyolali. The study used a qualitative method with a case study approach. Data collection was conducted through in-depth interviews with employees and the business owner, direct observation of work activities, and relevant documentation. The results showed that the level of employee job satisfaction is relatively high, formed from a combination of structural, social, physical, and psychological factors. These factors include clear division of tasks, a regular work shift system, harmonious interpersonal relationships between employees, and the availability of adequate work facilities and compensation. High job satisfaction has been shown to encourage intrinsic motivation, discipline, and consistency in carrying out employees' duties. This is reflected in the ability of employees to provide fast, friendly, and consistent service, especially during busy operating hours. The research findings confirm that job satisfaction has a significant influence on employee performance, which in turn impacts customer satisfaction and business sustainability. Therefore, management needs to maintain and improve factors supporting job satisfaction to maintain service quality and business competitiveness.

The use of cosmetics, especially day creams, is increasing along with the high public interest in facial skin care. However, day cream products are still found to potentially contain hydroquinone, a skin whitening agent whose use is restricted because it can cause harmful side effects on skin health. This study aims to identify the presence and determine the levels of hydroquinone in day cream products circulating in District X, Sukoharjo Regency, and to assess their compliance with the regulations of the Food and Drug Monitoring Agency (BPOM). This study was a descriptive analytical study using a purposive sampling technique. A total of 15 day cream products were analyzed, consisting of 8 BPOM-registered products and 7 products not registered with BPOM. Qualitative analysis was conducted using color reaction tests with FeCl₃, Benedict’s, and o-phenanthroline reagents. Furthermore, quantitative analysis was performed using the High Performance Liquid Chromatography (HPLC) method to accurately and specifically confirm the presence and determine the levels of hydroquinone. The results showed that the color reaction test has limitations in specifically identifying hydroquinone. HPLC confirmation revealed the presence of hydroquinone in several day cream samples, with concentrations ranging from 0.024% to 0.150%. These findings indicate the need for stricter monitoring of day cream distribution to ensure the safety of cosmetic products for the wider public.

Best graduate selection is crucial for academic achievement and contributes to the accreditation value of the institution. Instituto Profissional de Canossa (IPDC) is a higher education institution founded by the Canossian Sisters in Timor-Leste. To improve the effectiveness of assessment and decision-making processes, an information system is needed to assist in selecting the best graduates based on multiple criteria. This research develops a web-based Decision Support System (DSS) using the Multi-Attribute Utility Theory (MAUT) method. MAUT is a multi-criteria decision-making method that evaluates alternatives based on their utility scores across several criteria. The study uses four main criteria: attendance, academic performance, ethics, and discipline. The system is implemented as a web application for universal access. The MAUT calculation results provide valid and accurate recommendations for the best graduates. System testing showed that the application successfully ranked candidates based on defined weights and criteria, providing objective and consistent selection results.

The success of information systems projects in the digital transformation era is often hindered by unhealthy team dynamics, even when technical aspects have been optimally met. This study aims to analyze the synergy between inclusive communication strategies and digital leadership styles in building team health and sustainable performance. The research method employed is a qualitative literature study, integrating variables such as digital leadership, psychological safety, and knowledge management. The results indicate that digital leadership serves as a primary catalyst in creating a psychologically safe work environment, which in turn enhances the creative self-efficacy of team members. Synergy is effectively established when leaders adopt transparent communication channels through digital collaboration tools to mitigate role conflict and technostress within hybrid work environments. Furthermore, knowledge coordination is proven to strengthen team cohesiveness through the conversion of personal knowledge into strategic organizational assets. The study concludes that the integration of empathic communication and adaptive leadership is the fundamental basis for the cognitive, psychological, and operational health of the team. This research produces a managerial synergy framework to mitigate the risk of project failure caused by human factors in the digital era.

The rapid growth of short-form social media platforms has increased the complexity of decision-making during the digital content planning stage. Content creators are required to evaluate the feasibility of content ideas and determine platform suitability prior to production, while most existing tools primarily focus on post-publication analytics. This study aims to design an Artificial Intelligence (AI)-enabled Decision Support System (DSS) to evaluate digital content ideas in the pre-production stage. Adopting a Design Science Research approach, the study develops a conceptual design artifact that integrates intrinsic content idea characteristics with cognitive and affective response modeling grounded in the Stimulus–Organism–Response (S-O-R) framework, alongside platform affordance mapping. The proposed artifact operationalizes a reflective evaluation mechanism that generates platform recommendation scores and idea enhancement suggestions without claiming deterministic or predictive performance modeling. Evaluation was conducted qualitatively through practitioner assessment to examine perceived usefulness, clarity of recommendations, and decision support contribution. The findings indicate that the developed artifact provides a structured reflective framework for early-stage content evaluation. Theoretically, this study extends the application of the S-O-R framework by operationalizing it as a design logic for a pre-production DSS artifact. Practically, the proposed system has the potential to support more systematic decision-making prior to content production.

Software Defined Network is a popular computer network concept today because of the ease of managing network traffic with the control plane. Massive internet usage makes web server services on SDN networks overloaded. There are many load balancing concepts to overcome this problem, one of which is implementing the K-NN algorithm. This study aims to maximize the performance of the K-NN algorithm on SDN networks by optimizing the K value using Grid Search Cross Validation, and adding server status selection logic based on the smallest disk if the server status calculated by K-NN has the same. All implementations of the load balancing concept in this study were created virtually using Open vSwitch and virtualbox. Testing was carried out using CPU, MEMORY, and DISK parameters sent by the server with the help of the psutils component. JMeter software was used for testing by sending data using the POST method. The data type is text/plain with a data size of 1MB, testing was carried out in stages with threads 100, 200, 300, 400. The test results showed that the performance of the K-NN algorithm was running optimally. There was no significant difference in the distribution of the load to the server, this made the optimization and addition of logic successful.

This research aims to develop a customized chatbot based on a local large language model (LLM) using Ollama Anything as a form of psychosocial support for Pencak Silat athletes. Mental toughness is a critical factor for Pencak Silat athletes, particularly when coping with competitive failure or sports-related injuries. Injuries sustained in Pencak Silat competitions often involve psychological consequences, including trauma, fear, anxiety, and disturbances in self-identity. To address these challenges, the proposed chatbot functions as a screen-integrated psychosocial support system for athletes. This research used an experimental method combined with Natural Language Processing (NLP) techniques was employed to construct a digital twin chatbot capable of simulating athlete-centered conversations. The Pencak Silat Athlete Chatbot is designed to assist athletes by providing responsive support when they experience defeat or performance setbacks during competitions. The research findings indicate that, although the chatbot is functional, its conversational responses remain relatively rigid, access times are prolonged, and further testing with Pencak Silat athletes in controlled settings is required. Overall, the development of the Pencak Silat Athlete Digital Twin Chatbot represents an ongoing effort to advance digital innovation and strengthen the ecosystem of sports achivements development in Indonesia.

The spread of hoax news in digital media is a serious problem because it can affect public opinion and social stability. This study aims to classify hoax news using the Support Vector Machine (SVM) algorithm. The dataset used is a hoax clarification dataset from the Ministry of Communication and Digital (Komdigi) of the Republic of Indonesia, totaling 1,872 data. The research process includes data collection, text pre-processing, feature extraction using TF-IDF, and classification using the SVM algorithm. Implementation was carried out using Google Colaboratory (Google Colab). Test results show that the SVM algorithm is able to provide good performance in classifying hoax news based on its topic with satisfactory accuracy, precision, recall, and F1-score values.

Tobacco is a leading commodity that forms the foundation of the rural economy, but its cultivation faces challenges in the form of labour intensity, significant capital requirements, and farmers' lack of understanding of systematic cost structures. This study aims to analyse the production cost structure and evaluate the economic efficiency of tobacco farming managed by the Batu Daun Farmer Group in Batuan Village, Sumenep Regency. The research method used a qualitative descriptive approach with data collection through in-depth interviews with the head of the farmer group, field observations, and analysis of financial documents as secondary data. The analysis focused on identifying fixed and variable costs, as well as evaluating economic performance using the Break Even Point (BEP) and Revenue-Cost Ratio (R/C) indicators. The results showed that the total production cost was IDR 28,597,500 (fixed costs of IDR 3,450,000 and variable costs of IDR 25,147,500) for the production of 2,800 kg of tobacco with a gross income of IDR 70,000,000. The R/C ratio value of 2.44 (>1) indicates that the business is operating efficiently and profitably, while the BEP of 215.4 kg shows that actual production far exceeds the break-even point, meaning that the business is in an economically safe zone. The results of the study conclude that the tobacco farming business of the Batu Daun Farmer Group is economically viable and efficient.

Sentiment analysis on social media is widely used to represent public perceptions of sports performance, particularly in international competitions. This study aims to analyze the sentiment of YouTube user comments regarding the performance of the Indonesian National Football Team during the FIFA World Cup 2026 Asian Qualifiers. The data were collected from user comments on videos related to the matches and analyzed using a machine learning–based sentiment analysis approach. Sentiment classification was performed using the Naive Bayes algorithm. The results indicate that the proposed approach is able to effectively identify public sentiment toward the national team’s performance during the qualification matches. The findings of this study are expected to provide insights into public perceptions and contribute to sentiment analysis research in the field of sports.

The fellow members of the organization must coordinate with each other and workztogether tozachieve thehgoals of the organization. Good communication from each member will definitely make the implementation of the organization run well, and vice versa. Activities that occur in the organization are supported by organizational communication, In organizational communication there are three sides of view that are of course different, namely communication from superiors to subordinates, communication between members and the last is communication that comes from members to their superiors and it can be seen from each point of view that communication has its own pattern. The delivery of messages or information from these patterns is known to affect the performance or work results of members in the organization. Therefore, the purpose of this study is specifically to find out and prove howzthe influencezof organizational comunication patterns on the performance of BEMP IKOM UNJ members for the 2024/2025 period. The research methodology is quantitative and uses a data collection method with a survey type. The results show that the independent variable in this study, namely organizational communication patterns, affects the dependent variable, namely member performance. It is known that the variable of organizational communication patterns has an effect of 31% on member performance. Then it can also be concludedzthat the influence of several variables that were not tested and then inserted into the form of this study was 69%.