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This study, entitled "Dynamics in Village Leadership (A Study of Village Apparatus Recruitment in Weseben Village, Malaka Regency)," aims to analyze the leadership style of village heads in the apparatus recruitment process, uncover the growing practices of patronage and clientelism, and examine their implications for the implementation of good governance principles at the village level. Theoretically, this research is based on village leadership theory, patronage-clientelism theory, and the concept of good governance, which emphasizes the values ​​of transparency, accountability, and public participation in village governance.The method used was a qualitative approach with a case study design. Data were collected through in-depth interviews with village heads, village officials, traditional leaders, members of the Village Consultative Body (BPD), and the community, and was supported by field observations and documentation studies. The results indicate that: (1) the leadership style of village heads in Weseben is dominant and personalistic, with the village head retaining full control over the apparatus recruitment process, (2) apparatus recruitment places greater emphasis on loyalty and personal closeness than on professional competence, (3) The practice of patronage and clientelism is strongly evident in the relationship between the village head as patron and village officials as clients, which is built through kinship ties and reciprocal interests, (4) decisions regarding the appointment of officials are often tinged with elements of nepotism and collusion to strengthen the village head's political support base, and (5) the recruitment system is not yet oriented towards the principle of a merit system, namely a selection system based on individual ability, achievement, and integrity, as implemented in professional governance.Thus, it can be concluded that the dynamics of village leadership in Weseben are still strongly influenced by the practice of patronage and clientelism, which hinders the realization of democratic, transparent, and accountable village governance.  

This study aims to explore the role of digital storytelling (DST) as a 21st-century learning strategy in strengthening character education. The rapid development of digital technology and the growing emphasis on student-centered learning have made DST a relevant pedagogical approach that integrates creativity, critical thinking, collaboration, and communication with values-based education. Using a simple systematic literature review (SLR) approach, this study analyzed eight peer-reviewed journal articles published between 2020 and 2024, obtained from international academic databases Scopus. Thematic content analysis was used to synthesize findings and map the contribution of DST to 21st-century learning competencies and character development. The results identified eight key themes: increased student engagement, improved collaboration and communication, enhanced critical thinking and decision-making, creativity and multimodal expression, self-reflection and identity building, empathy and social awareness, interdisciplinary and authentic learning, and the formation of moral values. These findings support constructivist and experiential learning theories and suggest that DST not only enhances cognitive skills but also facilitates affective and ethical development. The study concludes that DST can be a transformative instructional tool in modern education. Practical implications include integrating DST into curricula across disciplines and levels, and equipping educators with training in digital pedagogy and reflective teaching design. Further research is recommended to explore DST implementation in primary and secondary schools and to evaluate its impact using mixed methods.

The use of Search-Based Software Engineering (SBSE) for optimizing software architecture has evolved from fully automated to interactive approaches, integrating human expertise. However, current interactive tools face limitations: they typically support only single decision-makers, confine architects to passive roles, and induce significant cognitive fatigue from repetitive evaluations. These issues disconnect them from modern, team-based software development, where collaboration and consensus are crucial. To address these shortcomings, we propose "ArchEvolve," a novel framework designed to facilitate collaborative, multi-architect decision-making. ArchEvolve employs a cooperative coevolutionary model that concurrently evolves a population of candidate architectures and distinct populations representing each architect's unique preferences. This structure guides the search towards high-quality consensus solutions that accommodate diverse, often conflicting, stakeholder viewpoints. An integrated Artificial Neural Network (ANN) serves as a preference learning module, trained on explicit team feedback to act as a surrogate evaluator. This active learning cycle substantially reduces the number of required human interactions and alleviates user fatigue. Empirical evaluation on two industrial case studies (E-Commerce System and Healthcare Management System) compared ArchEvolve to a state-of-the-art interactive baseline. Results indicate that ArchEvolve achieves statistically significant improvements in both solution quality and consensus-building. The preference learning module demonstrated over 90% accuracy in predicting team ratings and reduced human evaluations by up to 46% without compromising final solution quality. ArchEvolve provides a practical, scalable framework supporting collaborative, consensus-driven architectural design, making interactive optimization a more viable and efficient tool for real-world software engineering teams by intelligently integrating cooperative coevolutionary search with a preference learning surrogate.

This study aims to analyze the factors influencing members’ decisions to take murabahah financing at BMT Mentari Umat Wangon. The research employed a quantitative approach with descriptive and verification methods. The population consisted of active members using murabahah financing, selected through purposive sampling. Independent variables included service quality, knowledge of Islamic products, trust, location, promotion, profit margin, and financing procedures, while the dependent variable was the members’ financing decision. Data were analyzed using multiple linear regression after validity, reliability, and classical assumption tests. The results show that service quality, Islamic product knowledge, trust, profit margin, and financing procedures significantly affect members’ decisions, whereas location and promotion have no significant effect. The most dominant factors are service quality and institutional trust. These findings support the Theory of Planned Behavior (TPB), which emphasizes that attitudes, trust, and perceived control are key determinants of financial decision-making. The study implies that BMT should enhance service quality, strengthen financial literacy related to Islamic products, and develop digital-based service systems to improve efficiency and competitiveness.

The construction industry faces major challenges in managing material inventory, particularly among start-up companies that still rely on paper-based manual records. Such practices often lead to data inconsistencies, delays in decision-making, and project inefficiencies. This study aims to analyze the implementation of a cloud-based and real-time inventory management system utilizing spreadsheets at PT X, a start-up contractor located in Karanganyar, Central Java. The research employs a qualitative case study approach, with data collected through direct observation, semi-structured interviews with finance staff, logistics administration staff, and the project manager, as well as documentation of material inflows and outflows. The findings reveal that the use of cloud-based spreadsheets enhances data transparency, facilitates real-time monitoring between field and office, and accelerates stock opname validation. The system also supports more responsive decision-making regarding material reordering and request postponements. Nevertheless, several obstacles remain, including limited digital literacy among staff, potential input errors, and reliance on internet connectivity. Theoretically, this research contributes to the literature on accounting information systems and inventory management in small-scale construction sectors. Practically, it demonstrates that low-cost cloud solutions can improve operational efficiency and serve as a foundation for developing more integrated systems in the future.

This study investigates the effectiveness of digital transformation in enhancing client acquisition, using a case study of a lamp business in Indonesia. Established in 2021, the company initially operated through traditional offline channels but expanded to online platforms in 2022, integrating digital tools including AI-based financial management systems to streamline operations and support decision-making. The research focuses on comparing offline and online strategies, examining key performance indicators such as revenue contribution, customer acquisition costs, conversion rates, and operational challenges encountered in both modes. In addition, the study explores whether the observed revenue growth following digitalization is primarily driven by online sales or whether it benefits from a hybrid approach that combines both online and offline channels. By analyzing these aspects, the study seeks to provide insights into the practical implications of digital transformation for small and medium-sized enterprises (SMEs), particularly in the Indonesian market. The findings are expected to clarify whether adopting digital strategies offers a measurable advantage over traditional methods in terms of efficiency, cost-effectiveness, and market reach. Ultimately, this research aims to inform SME owners and managers about best practices for leveraging digital tools to enhance client acquisition and drive sustainable business growth.

The rapid growth of open-source software (OSS) in machine learning (ML) has intensified the need for reliable, automated methods to assess project quality, particularly as OSS increasingly underpins critical applications in science, industry, and public infrastructure. This study evaluates the effectiveness of a diverse set of machine learning and deep learning (ML/DL) algorithms for classifying GitHub OSS ML projects as engineered or non-engineered using a SMOTE-enhanced and explainable modeling pipeline. The dataset used in this research includes both numerical and categorical attributes representing documentation, testing, architecture, community engagement, popularity, and repository activity. After handling missing values, standardizing numerical features, encoding categorical variables, and addressing the inherent class imbalance using the Synthetic Minority Oversampling Technique (SMOTE), seven different classifiers—K-Nearest Neighbors (KNN), Decision Tree (DT), Random Forest (RF), XGBoost (XGB), Logistic Regression (LR), Support Vector Machine (SVM), and a Deep Neural Network (DNN)—were trained and evaluated. Results show that LR (84%) and DNN (85%) outperform all other models, indicating that both linear and moderately deep non-linear architectures can effectively capture key quality indicators in OSS ML projects. Additional explainability analysis using SHAP reveals consistent feature importance across models, with documentation quality, unit testing practices, architectural clarity, and repository dynamics emerging as the strongest predictors. These findings demonstrate that automated, explainable ML/DL-based quality assessment is both feasible and effective, offering a practical pathway for improving OSS sustainability, guiding contributor decisions, and enhancing trust in ML-based systems that depend on open-source components.

The Heritage Center (BHP) is a government institution under the Ministry of Law and Human Rights of the Republic of Indonesia that has a strategic role in civil law, especially related to the management of heritage property. Rooted in colonial regulations and regulated in the Civil Code, BHP is authorized to represent and protect the legal interests of individuals whose whereabouts are unknown, immature, or legally incompetent. This study aims to analyze the implementation of BHP's duties in managing heritage assets based on the provisions of the Civil Code and identify supporting and inhibiting factors for its implementation. The method used is normative legal research with a legislative approach and literature study. The results of the study show that BHP's position is as a subject of public law that carries out private legal functions. BHP's authority includes the management of unmanaged legacies, acting as a guardian or guardian, and acting as a curator in bankruptcy cases. Despite having a strong legal basis, the effectiveness of the implementation of BHP's duties in the field has not been optimal. The obstacles faced include limited resources, lack of public understanding, and coordination between agencies that has not been maximized. Therefore, systematic improvement efforts are needed through institutional capacity building, legal socialization, and strengthening regulations and cross-sector synergy to support the effective and sustainable implementation of BHP's tasks.

The assessment of student achievement is one of the main indicators in measuring the effectiveness of the educational process in Islamic boarding schools. The assessment process that has been carried out manually tends to be subjective, so it has the potential to cause a discrepancy between the student's achievement and the results of the evaluation received. This research aims to develop a decision support system that is able to assess student achievement in an objective, measurable, and transparent manner by applying the PROMETHEE (Preference Ranking Organization Method for Enrichment Evaluation) method. This method is used because it has the ability to manage various assessment criteria simultaneously to produce fair and rational alternative rankings. The research approach used is quantitative descriptive by involving a number of students as research samples. The data was analyzed through the stages of determining weight, calculating preference values, and determining the values of leaving flow, entering flow, and net flow as the basis for determining the final ranking. The results of the study show that the PROMETHEE method can provide consistent and accurate ranking results, by placing students who have academic excellence, discipline, and positive personalities in the top position. These findings prove that the PROMETHEE method is effective in overcoming the subjectivity of the assessment and increasing the transparency of the evaluation process. Practically, the resulting system can help the pesantren in determining outstanding students more efficiently and based on data, as well as theoretically strengthen the application of multicriteria decision-making methods in the context of Islamic education.’

Mediation, as one of the alternative dispute resolution (ADR) mechanisms in civil cases, aims to provide efficient, timely, and non-confrontational solutions for disputing parties. Within the Indonesian legal system, mediation has been formally regulated through the Supreme Court Regulation (PERMA) No. 1 of 2016 concerning Mediation Procedures in Court. This study seeks to examine the implementation of civil dispute resolution through mediation from a legal perspective while also assessing its empirical effectiveness in district courts. The research employs an empirical juridical approach, with data collected through in-depth interviews with mediator judges, advocates, and disputing parties who have participated in the mediation process, supported by documentation studies of civil case decisions resolved through mediation. The findings reveal that, normatively, mediation has a sufficiently strong legal foundation as an alternative method of dispute resolution. However, its practical effectiveness remains constrained by several challenges, such as the limited understanding and legal awareness of disputing parties, time constraints faced by mediator judges, and the absence of an optimal supervisory mechanism for monitoring mediation practices. These factors contribute to the relatively low success rate of mediation in practice. Therefore, improvements are required in the implementation of regulations, the establishment of more effective monitoring systems, and the enhancement of human resource capacity, particularly mediator competence. Strengthening these aspects is expected to enable mediation to function more effectively as a fair, efficient, and accessible mechanism for resolving civil disputes in Indonesia.

Increasing rural communities’ understanding of national health policy and system is a strategic step toward improving access and equality in Indonesia’s health services. Health policy education plays a crucial role in ensuring that citizens understand their rights and responsibilities as National Health Insurance (JKN) participants, as well as the mechanisms of available healthcare services. This study aims to analyze the effectiveness of health policy education in rural areas, focusing on the improvement of health literacy, community participation in decision-making, and the role of health workers as facilitators. A descriptive quantitative method was applied through surveys and field observations in three villages of Sleman Regency, Yogyakarta. The findings reveal that 82% of respondents understood their JKN rights after the education program, compared to only 45% before. Major challenges included limited information media and low digital literacy among villagers. The study concludes that community education on national health policy effectively enhances public awareness of universal health coverage but requires continuous support through collaboration between local government, healthcare providers, and educational institutions.

The performance of investigators in resolving criminal cases is one of the key indicators of police effectiveness. However, the Semendawai Suku III Police Sector still faces challenges in monitoring case resolution due to the lack of an integrated reporting system and minimal documentation of investigators’ knowledge. This study aims to develop an interactive dashboard system based on Knowledge Management to assist in monitoring case resolution performance and support data- and knowledge-based decision-making processes. The research employs the Prototype method, involving several stages: needs analysis, system design, system development, testing, and refinement. The system was developed using the PHP programming language and MySQL database. The implementation results show that the dashboard can display data on criminal reports, case resolutions, and pending cases in an informative and integrated manner. In addition, the knowledge base feature functions as a medium for storing and sharing investigators’ experiences (lesson learned), allowing field knowledge to be reused by other officers when handling similar cases. Overall, the implementation of the interactive dashboard system based on Knowledge Management at the Semendawai Suku III Police Sector successfully improves work efficiency, strengthens the transparency of investigative performance, and builds a foundation for sustainable organizational learning within the police environment.

This study addresses the inefficiencies in the manual student payment administration system currently used at SMK Bina Insan Nusantara, which relies on handwritten ledgers and Microsoft Excel spreadsheets, leading to frequent data errors, delayed reporting, and difficulties in retrieving payment records. The research aims to design a web-based information system to streamline the recording, monitoring, and reporting of student payments. A descriptive research approach was employed, with data collected through direct observation, semi-structured interviews with administrative staff, and literature review. System analysis was conducted using the PIECES framework, and system design followed the Unified Modeling Language (UML) methodology. The proposed system features user authentication, student data management, real-time payment input, digital receipt generation, and automated reporting (daily, monthly, and yearly). Black-box testing confirmed that the system functions as intended, validating inputs, processing transactions accurately, and generating reliable outputs. Findings indicate that the web-based system significantly improves data accuracy, operational efficiency, accessibility, and service quality compared to the existing manual process. The implementation of this system enables school administrators to manage payments more effectively, provides students and parents with timely access to payment information, and supports faster, data-driven decision-making by school leadership and the foundation. Future enhancements could include mobile integration and online payment gateways.

Decision Support Systems (DSS) can become inaccurate when used with imprecise, incomplete, or dynamically changing data. Fuzzy logic techniques based on conventional methodologies may be strong at handling vagueness, but are unable to adapt their behavior in response to different data distributions on their own. This paper recommends the creation of an Adaptive Fuzzy Logic Integration Framework that dynamically updates membership functions and rule weights in response to data variation to enhance decision accuracy under uncertainty. The described framework combines Fuzzy Inference Systems (FIS) with learning-based parameter update concepts borrowed from adaptive optimisation. The model was simulated and executed on a hybrid algorithmic platform that included gradient-based parameter tuning and iterative feedback learning. Experimental tests were conducted on uncertainty-generated data sets to compare adaptive and conventional fuzzy models in terms of ISME (Root Mean Square Error), convergence stability, and decision accuracy. Previous results show that the adaptive model achieves a 21.4% increase in accuracy and a 28% improvement in convergence rate compared to non-adaptive fuzzy systems. Moreover, the model ensures stable performance even in the presence of random data perturbations, demonstrating its ability to handle uncertainty. This book incorporates a self-tuning fuzzy decision model that converts static inference structures to dynamic evolving decision engines. The outcomes establish a foundation for next-generation smart DSS for real-time optimization in uncertainty.

This study examines the changes in the authority of the Professional Disciplinary Council (Majelis Disiplin Profesi, MDP) under Law Number 17 of 2023 concerning Health and their implications for legal certainty for medical and health professionals. Although these changes are intended to improve the professional disciplinary system, they have resulted in the centralization of authority under the Ministry of Health, including the appointment of members, institutional formation, and the process of judicial review of MDP decisions. Furthermore, MDP recommendations can now serve as a basis for criminal investigations against medical and health personnel, which contradicts the original function of the MDP as an institution for enforcing ethics and professional discipline based on due process of ethics. This research employs a normative juridical method with a descriptive-analytical and case study approach, supported by expert interviews in health law. Theoretical frameworks used include the Theory of Legal Certainty, the Theory of Human Rights, and the Theory of Legal Protection. Findings indicate that the centralization of authority under the Ministry of Health has created a power imbalance in professional oversight. This has negative implications for legal protection, increasing the risk of conflict of interest, abuse of authority, and weakening legal certainty for medical and health professionals. Therefore, it is necessary to revise Law No. 17 of 2023 and Government Regulation No. 28 of 2024 to restore the independence of the MDP and ensure a proportional redistribution of authority within the health professional oversight system.

The issue of global food security is increasingly pressing amidst climate change, population growth, and environmental degradation. The agricultural sector, particularly rice production, faces threats from pests and diseases that reduce crop yields and farmer incomes. Climate change exacerbates pest attack patterns, increasing crop losses. In addition, excessive use of chemical pesticides leads to pest resistance and negative impacts on ecosystems and human health. This study used a descriptive method with a qualitative approach, and the study population included farmers who cultivate rice fields and farmer groups that have received Integrated Pest Management (IPM) in Makale District, Tana Toraja Regency, with a population of 325 families. Respondents were randomly selected at 15% of the total population, with a sample of 49 farmers consisting of three farmer groups. The results showed that the role of farmer groups in IPM implementation in Makale District includes extension and training (65.5%), facilitating access to information and resources (69%), decision-making (67.5%), and conflict management and IPM cooperation (66.5%). Therefore, it is necessary to strengthen the implementation of the rice farming system, with support from the Government and the Tana Toraja Regency Agriculture Service to optimize the development of rice farming businesses.

Background: Accurate coding of external cause in injury diagnoses is crucial to ensure the validity of medical records, support health policy decisions, and maintain the quality of morbidity reporting.  Objective: To determine the factors that influence the inaccuracy of external cause codes in injury diagnosis at Thalia Irham General Hospital, Panciro, Gowa Regency. Method: This study employed a descriptive qualitative approach using observation and in-depth interviews with outpatient coders handling injury cases. Result: The study found that only 36% of the medical record documents were coded accurately, 26% were inaccurately coded, and 36% lacked any external cause code. The main causes of inaccuracy included incomplete anamnesis, limited time, absence of specific standard operating procedures (SOPs), and the belief that external cause codes do not impact BPJS claims. Conclusion: The low level accuracy of external cause coding is caused by the lack of understanding of officers regarding the ICD-10 Chapter XX classification, the absence of a specific SOP for coding injuries, and the perception that external cause codes do not affect the claims system.

PT Care Spunbond faces significant challenges in managing production status reports due to its reliance on a manual, paper-based recording system. This results in non-real-time data input, slow data processing, and a high risk of data loss or corruption. To address these issues, this study aims to design a user-friendly, web-based dashboard for monitoring and reporting production status. This system is designed to enable fast data input, provide easy access for the Quality department, and provide secure and integrated data storage. The research method used is a descriptive approach with data collection techniques through observation, interviews, and literature studies. After that, a system analysis was conducted using the PIECES framework to identify existing problems, followed by the design of a UML (Unified Modeling Language)-based system. The results show that the proposed system can improve data processing efficiency, improve report accuracy, and ensure better production data security. With the implementation of this system, the company is expected to accelerate data-driven decision-making, increase transparency, and support efforts to continuously improve product quality.

This study presents the integration of a Large Language Model (LLM) Ollama with the OpenStreetMap (OSM) API within a Business Intelligence (BI) framework to develop an intelligent, location-based recommendation system. The system is designed to assist users in finding dining, leisure, and resting places through natural language interaction and contextual understanding. The LLM interprets user input semantically, transforms it into structured spatial queries, and retrieves relevant geospatial data from OSM. The data are then analyzed, categorized, and visualized using BI methods to enhance interpretability and decision-making. The system was implemented using Next.js, Leaflet.js, ensuring interactivity and scalability for web-based deployment. Technical evaluation focused on system accuracy, response time, and output consistency. Results demonstrate an average response time of 1.74 seconds, 80% accuracy, and 80% consistency, proving the model’s efficiency in producing relevant, context-aware recommendations. This integration highlights the potential of combining open geospatial data, local LLMs, and BI analytics to create intelligent, data-driven decision support systems applicable to tourism, urban planning, and spatial information management.

N the rapidly evolving digital era, decision-making has become a critical aspect across various fields, including education, where choices such as selecting an Islamic boarding school (pondok pesantren) are often influenced by complex and subjective factors. This study addresses the dilemma faced by Diyah, a junior high school student, in determining the best reasons for choosing Pondok Pesantren Darularafah Raya, highlighting the limitations of manual, personal-based processes that fail to systematically consider measurable criteria like educational quality, learning environment, facilities, discipline, and instilled religious values. The advancement of information technology provides a solution through Decision Support Systems (DSS), utilizing the ORESTE (Organization, Rangement Et Synthèse De Données Relationnelles) method, which effectively processes ordinal data to produce objective rankings based on subjective yet structured preferences. Unlike other methods such as SAW or AHP that rely on numerical data, ORESTE emphasizes relative preference weights, making it suitable for individual decision-making contexts like educational choices. The novelty of this research lies in applying ORESTE in a DSS focused on analyzing an individual's best reasons for selecting a pesantren, aiming to reduce subjective bias and enhance rationality. The primary objective is to develop a DSS using the ORESTE method to analyze and determine Diyah's optimal reasons for choosing the pesantren. Through this, the system is expected to accelerate evaluation processes, improve objectivity, and identify dominant factors influencing educational decisions. Findings from the implementation demonstrate accurate rankings that prioritize key criteria, leading to more efficient and data-driven outcomes. Implications include aiding students, schools, and educators in understanding influential factors, fostering objective assessment systems, and serving as a reference for future studies integrating MCDM methods with computer-based systems in personalized educational decision-making.