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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.

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.

This research investigates thermal material properties and performance characteristics for high-speed vessel components subjected to extreme thermal stress during sustained high-speed operations. High-speed vessels including patrol boats, fast ferries, and naval craft experience elevated thermal loads from high-power density propulsion systems, aerodynamic heating, and sustained operational intensities creating demanding conditions for structural and mechanical components. Through qualitative analysis involving naval architects, materials engineers, high-speed vessel operators, and component manufacturers, this study examines how material thermal properties affect component durability, performance, and safety while identifying optimal material selections for critical applications. Results demonstrate that advanced thermal materials including high-temperature aluminum alloys, titanium alloys, ceramic composites, and thermal barrier coatings can extend component service life by 40-70%, improve thermal management effectiveness by 25-45%, and enhance operational reliability compared to conventional materials. Key implementation challenges include material cost premiums of 150-300%, manufacturing complexity, limited operating experience, qualification testing requirements, and supply chain constraints. Findings reveal that strategic thermal material selection for critical components represents essential enabling technology for high-speed vessel performance, reliability, and operational availability supporting defense, commercial, and emergency response applications requiring sustained high-speed capabilities. This research contributes to marine materials engineering literature by providing evidence-based frameworks for thermal material selection applicable to diverse high-speed vessel applications.

This research investigates intelligent cooling system design for main ship engines operating in tropical waters, integrating advanced machinery engineering with human factors to address thermal management challenges affecting engine performance, reliability, and crew operational effectiveness. Tropical maritime environments impose severe cooling demands through elevated seawater temperatures (28-32°C), high ambient conditions (28-35°C), and accelerated biofouling, reducing conventional cooling system effectiveness by 15-25% while increasing maintenance burdens and operational risks. Through qualitative analysis involving marine engineers, chief engineers with tropical operational experience, cooling system manufacturers, naval architects, automation specialists, and maritime training institutions, this study examines how intelligent cooling systems incorporating variable-speed pumps, adaptive control algorithms, predictive maintenance, and crew-centered interfaces can optimize thermal management while supporting effective human-machine collaboration. Results demonstrate that intelligent systems can reduce cooling energy consumption by 20-35%, improve temperature stability by 50-65%, extend maintenance intervals by 40-80%, and enhance crew situational awareness through intuitive monitoring interfaces, while requiring comprehensive training programs developing technical understanding and operational competencies. Key implementation challenges include control system complexity, sensor reliability in harsh marine environments, integration with existing engine management platforms, crew competency development requirements, and lifecycle cost justification. Findings reveal that successful intelligent cooling system implementation requires holistic sociotechnical approach addressing machinery engineering optimization, automation technology deployment, and human capability development through coordinated design and training strategies. This research contributes to marine engineering literature by providing integrated frameworks for intelligent system design incorporating machinery performance, automation capabilities, and human factors supporting operational excellence in tropical maritime operations.

The growth of the culinary industry requires small-scale food businesses to implement effective and efficient operational management. This study analyzes the optimization of operational management at Teen Canteen by examining the use of digital systems, staff performance, the implementation of Standard Operating Procedures (SOP), and operational constraints. A descriptive qualitative approach was employed, with data collected through semi-structured interviews and direct observation. The informant was purposively selected, namely the Head Bar, who is directly involved in daily operational activities. Data analysis was conducted using the Miles and Huberman model. The findings indicate that Teen Canteen’s operational management has not been optimally implemented due to the discontinuation of digital cashier systems, reliance on manual transaction and inventory records, and inconsistent SOP implementation. Although staff performance is generally adequate, issues related to discipline and service responsiveness remain. This study recommends reintroducing digital cashier systems and establishing written SOPs to improve operational effectiveness.

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%.

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.

Corruption remains a systemic challenge in Indonesia, particularly in the administration of government grant funding, undermining public trust, institutional integrity, and sustainable development. Despite the establishment of the Corruption Eradication Commission (Komisi Pemberantasan Korupsi, KPK) and other specialized bodies, law enforcement continues to face institutional, political, and cultural barriers. This study explores how Islamic criminal law can strengthen anti-corruption strategies by integrating empirical legal practices with normative religious principles. Using a normative-empirical socio-legal approach, the research combines case studies of KPK’s enforcement processes with doctrinal analysis of fiqh jināyah. Data were collected through legal document analysis, policy reviews, and qualitative evaluations of institutional reports and court rulings. Findings indicate that Islamic legal concepts such as khiyānah (breach of trust), ghulūl (misappropriation of public assets), amānah (trustworthiness), ʿadl (justice), and maṣlaḥah (public interest) provide a strong ethical foundation that complements positive law enforcement. While KPK has demonstrated effectiveness in investigation, prosecution, and prevention, its performance is constrained by political pressure, regulatory gaps, and limited resources. The study concludes that embedding Islamic ethical principles into governance, legal education, and public administration can enhance institutional accountability, reinforce preventive measures, and cultivate a culture of integrity. This normative convergence advances socio-legal pluralism and offers practical insights for value-based anti-corruption policy in Indonesia.

Gununganyar Reservoir in Tuban Regency is one of the reservoirs used as a source of livelihood for the local community. However, further development and utilization of Gununganyar Reservoir have yet to be undertaken. The development of a small-scale floating Photovoltaic Solar Power Plant (floating solar panels) at Gununganyar Reservoir, aimed at providing an alternative and renewable energy source for operating raw water pumps and for supporting the Gununganyar Nature Tourism (WAG) initiative, constitutes an innovative and strategic effort to enhance energy efficiency and environmental sustainability in rural areas. This study focuses on the design and performance assessment of a floating solar panel system constructed with processed bamboo as an alternative floating material replacing HDPE, while integrating local wisdom and environmental conservation principles to minimize the reservoir’s carbon footprint. The methodology employs a simple quantitative approach combined with a literature review of relevant studies. Based on the analysis, the design of this small-scale floating solar panel system utilizes a 170 Wp off-grid solar module mounted at a tilt angle of approximately 7,1°, capable of generating a peak power output exceeding 2,962 kWp and supported by a battery capacity of  328,33 Ah. The implementation of these floating solar panels is expected to serve as a model for reservoir development by promoting energy self-sufficiency, stimulating local economic growth, and contributing to the achievement of net zero emissions by 2060.

Mental health is a very important element in supporting students' academic success. As a group at a vulnerable age, students often face academic, social, and psychological pressures that can affect their mental health and academic performance. This study aims to examine the impact of mental health on the academic performance of students at the Faculty of Economics, Pasim National University. The method used in this study is a quantitative approach with data collection through questionnaires. The research subjects consisted of 123 students in their first semester, where the sample included 94 respondents selected using the Slovin formula. The data were analyzed using SPSS software, including validity, reliability, and normality tests, simple linear regression, coefficient of determination, and ANOVA. The findings of this study indicate that all measurement tools used are valid and reliable. Normality testing shows that the data have a normal distribution. The regression analysis results showed a significant positive effect of mental health on students' academic performance. An R-squared value of 0.410 indicates that mental health contributes 41% to the variation in students' academic results, while the remaining 59% is influenced by other factors. The results of the ANOVA also showed a significance level of 0.000 (<0,05). confirming the suitability of the regression model. Therefore, it can be concluded that mental health plays a significant role in improving student academic achievement. This research is expected to provide educational institutions with a basis for designing mental health support policies and programs to improve student academic quality.

Job satisfaction is one of the important factors that affect the quality of health services, especially in the context of care provided by nurses in hospitals. High job satisfaction can have a positive impact on motivation, performance, and nurse retention, thereby contributing to improved healthcare service quality. Conversely, low job satisfaction can lead to high turnover rates, which can disrupt service continuity and negatively impact patients. This study aims to analyze the relationship between years of service, employment status, and job satisfaction among nurses at RSUD Malingping 2025. The research method used is a quantitative descriptive research method. The population in this study consists of 83 nurses at RSUD Malingping, with a total sampling method applied to collect data from all 83 nurses. The analysis used the Chi-Square test. The results of the test showed that there is a significant relationship between length of service and job satisfaction among nurses (P = 0.007), as well as a significant relationship between employment status and job satisfaction among nurses (P = 0.000). The conclusion drawn is that there is a relationship between length of service and employment status with job satisfaction among nurses at RSUD Malingping in 2025. Recommendations for the hospital include paying more attention to the job satisfaction of nurses and other staff, so that healthcare services at RSUD Malingping can be more optimal.

Academic stress is a prevalent issue among Indonesian students, affecting both their academic performance and mental well-being. This study investigates the effectiveness of short educational videos on social media in improving students' knowledge about academic stress. A descriptive quantitative approach was used, with a sample of 35 Nursing students from the 2024 cohort at Malahayati University. The data was collected through questionnaires administered before and after students were exposed to 1-3 minute educational videos. The results showed a notable increase in the average knowledge score, from 82.71 to 85. The findings suggest that short videos, which are visually engaging and easy to access, are highly effective for Generation Z learners. Social media platforms, particularly TikTok and Instagram, can be valuable tools for early psychological intervention, providing students with accessible resources to manage academic stress on their own. This study highlights the potential of leveraging digital platforms to support student well-being and offers insights into innovative approaches for addressing mental health challenges in educational settings.

Exercise is a planned physical activity that not only contributes to physical fitness but also plays a significant role in maintaining and improving mental health. This study aims to explore the relationship between physical exercise and mental well-being among adolescents and university students with diverse backgrounds of physical activity. The research employs a qualitative approach using in-depth interviews with five informants, consisting of university students who engage in recreational exercise, recreational runners, middle- and long-distance runners, and a sprinter. Data collection was conducted in several public sports spaces in the city of Bandung, focusing on participants’ subjective experiences related to their psychological conditions before and after engaging in exercise. The findings indicate that all participants perceive exercise as an effective means of emotional regulation. Physical activity is consistently associated with improved mood, reduced stress, enhanced sleep quality, stable energy levels, and increased academic concentration and focus. In addition, exercise contributes to the development of self-confidence, self-efficacy, and self-control through structured routines. The social aspects of exercise, such as support from peers, communities, and coaches, were shown to strengthen the sustainability of physical activity while also providing essential emotional support for mental health. However, among competitive athletes, performance-related pressure was identified as a factor that may negatively affect psychological well-being, although this effect can be moderated by adequate social support. Overall, the findings of this study emphasize that exercise functions as an adaptive coping mechanism operating through biological, psychological, and social pathways, and highlight the importance of consistency and balance in physical activity to achieve optimal mental health benefits.  

The increasing demand for housing in tropical regions requires building materials that are fast to apply, environmentally friendly, and resilient to extreme climate conditions as well as disaster risks. Conventional interlocking bricks are often chosen for their ease of construction, yet they still face challenges such as moisture and early cracking. This study proposes the innovation of the Hybrid Living Green Brick, a combination of lightweight bricks made from rice husk ash and fly ash waste (FRCB) with a biological layer of cyanobacteria. FRCB improves compressive strength by approximately 30% with the addition of 5% rice husk ash, achieving 65 kg/cm², thereby meeting Class 50 requirements (≥50 kg/cm²) according to SNI-15-2094-2000. The incorporation of 3% cyanobacteria provides an additional though not significant strength improvement, while still within the Class 50 category. It also reduces brick weight by 4.3%, with further optimization potential through cyanobacteria integration, and lowers carbon emissions from the firing process. Cyanobacteria induce the formation of CaCO₃ layers that seal pores, reduce water absorption by an average of 10%, and provide self-healing properties for microcracks. Preliminary observations indicate that FRCB offers stable mechanical performance, while biological activity was observed on the 7th day with the formation of pale-white mineral layers continuing until the 28th day. This hybrid innovation shows potential to support sustainable and disaster-resilient tropical construction by combining the mechanical strength of waste-based materials with the biological durability of cyanobacteria against extreme climates. Despite challenges related to moisture control and production standardization, the Hybrid Living Green Brick concept opens new pathways for developing environmentally friendly construction materials that are more adaptive to disaster-prone tropical conditions.

This study aims to compare the performance of Multiple Linear Regression (MLR) and Random Forest Regression (RFR) in predicting student performance based on academic scores. Student performance is defined as the average of math scores, Reading Scores, and writing scores. This study uses a quantitative approach with a comparative design based on predictive modeling. The data used is secondary data from the Student Prediction dataset obtained through the Kaggle platform, which was processed using the Python programming language through the Google Colab platform. The analysis stages included the formation of performance variables, the separation of training and test data with a ratio of 80:20, model training, and evaluation using the Mean Squared Error (MSE), Mean Absolute Error (MAE), and coefficient of determination (R²) metrics. The results show that the Multiple Linear Regression model produced an MSE value of 2.74 × 10⁻²⁸, an MAE of 1.51 × 10⁻¹⁴, and an R² of 1.000. Meanwhile, Random Forest Regression produced an MSE of 0.296, an MAE of 0.375, and an R² of 0.998. These findings indicate that both models have a very high level of accuracy, but Multiple Linear Regression provides the best performance. This is due to the strong linear relationship between the input variables and the target variables formed directly from the combination of academic values. Thus, the linear regression model is proven to be more suitable for use in data structures that have simple linear relationships compared to ensemble-based models.

The rapid development of digital technology has led to an increased adoption of digital payment methods in online transaction-based businesses. However, in practice, failures and limitations in the implementation of digital payment systems still occur, potentially disrupting transaction processes and reducing customer convenience. Payment related obstacles may result in transaction cancellations and increase the risk of customer churn. This study aims to analyze the impact of failures and limitations in digital payment methods on customer churn using a classification-based approach. The data used in this research are secondary e-commerce customer data obtained from the Kaggle platform, including transaction information, payment methods, customer behavior, and historical transaction records. The research methodology consists of data preprocessing, time-based feature engineering, and classification modeling using logistic regression, decision tree, and random forest algorithms. Model performance is evaluated using accuracy, precision, recall, F1-score, and confusion matrix metrics. The results indicate that the decision tree model demonstrates superior capability in identifying churn customers compared to the other models, although it does not always achieve the highest accuracy. In addition to digital payment methods, other factors such as purchase value, transaction frequency, purchase timing patterns, and product return rates also influence customer churn. The findings highlight the importance of optimizing digital payment systems as part of customer experience enhancement strategies and customer retention efforts in online transaction–based businesses.

Customer churn is a strategic challenge for digital streaming platforms because it directly Impacts revenue and business sustainability. This study aims to analyze the factors influencing customer Churn and develop a churn prediction model using the Random Forest algorithm. The study uses a Quantitative approach with an explanatory design and utilizes secondary data from the Netflix Customer Churn and Engagement Dataset available on Kaggle. The dataset consists of 1,000 customer data with 16 Variables covering demographic characteristics, service usage behavior, financial condition, and customer Satisfaction level. The data was processed through preprocessing, one-hot encoding, and a 70:30 split Between training and test data. Model performance was evaluated using accuracy, precision, recall, F1 Score, and ROC-AUC metrics. The results show that the Random Forest model produces an accuracy of 53.7%, precision of 56.3%, recall of 63.6%, F1-score of 59.7%, and ROC-AUC of 0.534, indicating Moderate predictive ability and only slightly better than random classification. Feature importanceAn.evealed that user engagement levels, such as viewing duration and frequency of interactions, Were the most dominant factors influencing churn, followed by economic factors and customer satisfaction. The results of this study are expected to provide a basis for streaming platforms to design more effective Customer retention strategies.

This study aims to strengthen students’ networking competencies through the implementation of straight and cross LAN cable assembly training at SMKN 53 Jakarta. The background of this research is based on the need to enhance students’ practical skills in basic computer networking, particularly in understanding cable configurations and applying crimping techniques according to industry standards. Many students experience difficulties in differentiating wiring standards and applying correct crimping procedures, which impacts their readiness for industry practice. This research employed a practical training approach combined with demonstration and hands-on methods. The participants were students of the Computer and Network Engineering program. Data were collected through observation, performance assessment, and competency tests before and after the implementation. The findings indicate a significant improvement in students’ understanding of cable color standards (T568A and T568B), accuracy in assembling straight and cross cables, and testing results using LAN testers. Students demonstrated higher levels of technical accuracy, problem-solving skills, and work discipline after the intervention. The implementation of structured practical activities proved effective as a medium for strengthening networking competencies. The study implies that continuous practice-based learning aligned with industry standards is essential in vocational education to improve students’ technical readiness and employability in the networking field.

A student's learning success is largely determined by their academic evaluation. Estimating a student's final grade can assist educational institutions in conducting initial assessments of academic achievement. This study aims to analyze the performance of the Multiple Linear Regression (MLR) and Random Forest (RF) algorithms in predicting students' final grades using Google Colab. This research method uses a quantitative approach using secondary data that includes age, mid-term exam scores, final exam scores, and categorical variables as independent variables, with the final grade as the dependent variable. The research process is carried out through data preprocessing steps, dividing training and test data, model training, and performance evaluation using Mean Squared Error (MSE), Mean Absolute Error (MAE), and the coefficient of determination (R2). The results show that the Random Forest algorithm provides more accurate prediction accuracy compared to the Multiple Linear Regression algorithm, especially in identifying nonlinear relationships between variables. Therefore, the Random Forest algorithm is more recommended for predicting students' final grades with complex data characteristics.