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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 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.

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 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.

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.

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.

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.

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.

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.

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 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.

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.

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%.

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.

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.