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This research investigates climate change adaptation frameworks for Indonesian port infrastructure and workforce safety through integrated risk management approaches addressing physical facility resilience and human resource protection. Through qualitative analysis involving 37 stakeholders including port authorities, terminal operators, marine engineers, climate scientists, occupational health specialists, and port workers, this study examines how climate threats including sea level rise, extreme weather, flooding, and heat stress affect both port operations and worker safety requiring coordinated adaptation strategies. Results demonstrate that integrated frameworks can reduce climate-related operational disruptions by 50-70%, decrease worker heat illness by 60-80%, improve emergency response effectiveness by 55-75%, and enhance infrastructure resilience by 45-65% when combining physical hardening with workforce protection measures. Key challenges include immediate infrastructure damage (ports already experiencing 3-8 annual flooding shutdowns), worker heat illness epidemic (150+ cases in 2023 with 300% increase), investment decision urgency ($15-25 billion infrastructure commitments 2024-2030), and organizational coordination across fragmented stakeholders. Findings reveal that successful climate adaptation requires holistic sociotechnical approaches treating ports as integrated human-infrastructure systems where worker safety and facility resilience prove inseparable, supporting Indonesia's maritime economic security and coastal community welfare through comprehensive climate risk management.

Community service at the Visi Iy Nike production unit was carried out using a descriptive qualitative and participatory approach to understand real conditions and challenges in the field. The activities began with direct observation of the production process to map workflows, identify operational obstacles, and evaluate the efficiency of resources, including labor, machinery, and production time. In addition, in-depth interviews and group discussions were conducted to collect information regarding production constraints and strategies applied by the operators. The data were analyzed descriptively to identify gaps between production targets and actual outcomes. Based on these findings, several interventions were implemented, including operator skill training, assistance in production planning, and the application of simple lean manufacturing principles to reduce waste and improve efficiency. The results showed that the average production efficiency reached 77%, supported by skilled operators, adequate raw materials, and flexible management practices. Routine documentation and systematic recording of production targets also contributed to better monitoring, evaluation, and faster decision-making processes.

High-dimensional clinical data exhibit complex and non-linear relationships among patient attributes, where outcomes are often influenced by feature interactions rather than isolated variables. However, many existing machine learning models prioritize predictive performance while providing limited interpretability and insufficient insight into interaction structures. This study aims to address this limitation by developing an interpretable and robust framework for feature interaction mining in clinical data. We propose a hybrid tree–neural modeling framework that explicitly captures and ranks feature interactions while maintaining stable predictive performance. Tree-based ensemble models are employed to identify non-linear interaction patterns, while neural representations enhance learning flexibility and generalization. The framework integrates interaction importance analysis, cross-validation–based stability assessment, and evaluation across multiple data splits to ensure robustness and interpretability. Experiments conducted on a real-world high-dimensional clinical dataset demonstrate that the proposed approach achieves consistent predictive performance, with AUC values ranging from 0.628 to 0.641 across five cross-validation folds (mean AUC ≈ 0.633). Performance remains stable under varying train–test splits, indicating strong generalizability. Interaction analysis reveals that a small number of dominant feature interactions—such as age combined with length of hospital stay and medication count combined with diagnostic information—consistently contribute to model predictions, appearing in over 80% of validation folds. Ablation studies further confirm that removing interaction-aware components leads to noticeable performance degradation, highlighting their importance.  In conclusion, this study demonstrates that explicit feature interaction modeling enhances interpretability, stability, and generalization in clinical prediction tasks. The proposed hybrid framework provides a reliable foundation for developing trustworthy and transparent clinical decision-support systems

Explainable artificial intelligence (XAI) has become a critical requirement in cybersecurity due to the high-stakes nature of security decision-making and the limitations of black-box learning models. This study investigates the construction of an explainable cybersecurity knowledge representation by leveraging standardized terminology from the NIST cybersecurity glossary. The primary problem addressed is the lack of transparent and semantically grounded reasoning mechanisms in existing AI-driven cybersecurity systems, which limits trust, accountability, and analyst adoption. To address this challenge, we propose a NIST-based semantic knowledge graph that embeds explainability directly into its ontology structure and reasoning process. The proposed framework systematically extracts definitional entities and relations from NIST glossary entries to construct a domain ontology and a multi-relational knowledge graph. A rule-based semantic relation extraction method is employed to ensure faithful, interpretable, and reproducible reasoning paths. The resulting knowledge graph contains over 3,000 cybersecurity concepts and approximately 27,000 semantic relations, covering hierarchical, associative, dependency, and mitigation semantics. Experimental evaluation demonstrates that the proposed approach achieves a high level of explainability, with 92.4% of reasoning outcomes being fully traceable and only 1.4% classified as non-traceable. Most explainable reasoning paths are limited to two or three hops, indicating an effective balance between inferential depth and human interpretability. Structural analysis further confirms the presence of meaningful hub concepts that support multi-hop semantic inference. These results confirm that ontology-driven, standard-based knowledge graphs provide a robust foundation for explainable cybersecurity intelligence. The study concludes that explainability-by-design, grounded in authoritative standards, offers a viable and trustworthy alternative to opaque AI models for cybersecurity applications.

This study explores the lives of Rohingya women residing in the Minaraya refugee camp, Padang Tiji Subdistrict, Pidie Regency, Aceh, through the lens of the Gender Agency Theory and Resilience Theory. It aims to understand how these women negotiate their roles within a patriarchal social structure and develop resilience amid economic, social, and cultural constraints. The findings reveal that Rohingya women live under a deeply entrenched patriarchal system in which men dominate decision-making processes, while women are confined to domestic responsibilities. However, their compliance should not be interpreted as mere submission; rather, it reflects a form of piety-based agency, a conscious act grounded in religious and moral values to maintain dignity and family harmony. Within the domestic sphere, women play an essential role in managing aid funds, distributing food, and regulating household expenditures. These actions demonstrate adaptive capacity and subtle role negotiation within the limits of gender norms. Spirituality serves as a significant source of strength; religious activities such as Qur'an recitations and collective prayers function as coping mechanisms for dealing with trauma and uncertainty. Furthermore, support from humanitarian organizations helps meet basic needs. However, the core of their resilience stems from family solidarity and deeply held religious values. Rohingya women in Aceh display strength through faith, social adaptation, and resource management, positioning themselves as active agents in sustaining dignity and survival amid adversity.

The phenomenon of mafqud (missing heirs) creates complex legal issues within Islamic inheritance law, particularly regarding the realization of justice and legal certainty in the distribution of estate assets. This study examines the legal position of mafqud as a temporary impediment in inheritance allocation and analyzes the procedural mechanisms applied by religious courts in determining mafqud status. Employing a normative juridical method, this research relies on primary legal materials, including the Qur’an, Hadith, classical fiqh references, and relevant judicial decisions, supported by secondary materials such as academic books and scholarly journals. The findings indicate that mafqud serves as a temporary barrier to inheritance distribution, requiring careful judicial assessment to ensure the protection of all heirs’ rights while remaining consistent with Islamic legal principles and the applicable national legal system. Therefore, structured and systematic procedures implemented by religious courts play an essential role in guaranteeing both justice and legal certainty in inheritance disputes involving mafqud.

Electricity Usage Control (P2TL) is a strategic program of PT PLN to reduce losses due to illegal electricity usage. This study aims to analyze the use of Geographic Information Systems (GIS) based on QGIS in supporting P2TL monitoring in Nabire Regency. The data used includes 54,939 customer coordinate points, collected through field surveys using GPS with a high level of accuracy. The method applied is descriptive spatial analysis with overlay techniques and spatial clustering. The results of the study show an uneven distribution of customers between districts, with the highest concentration in Nabire District (18,247 customers or 33.2%). The dominant tariff type is R1T (60.3%) with the largest power capacity of 1300 VA (49.5%). Spatial analysis identified 12 hotspots and 8 priority monitoring zones. The application of GIS increases the effectiveness of identifying vulnerable areas by 78% and reduces the duration of field inspections by 45%. The resulting thematic map visualization provides significant support in location-based decision making.

Efficient and accurate management of population data is a crucial aspect for government organizations, especially the Nabire Regency Women's Empowerment and Child Protection Office (DP3A). Currently, manual filing methods cause various obstacles, such as data errors, delays in public services, and difficulties in the reporting process. This study proposes the development of a web-based population data archiving information system using the Waterfall method. The system is designed to integrate and automate the process of managing sensitive data, including search, update, and report generation features. The results of the development show that the system is able to improve operational efficiency, data accuracy, and information security. In addition, this system supports transparency and accountability in the management of the DP3A program, including MSME and Family Planning data, while facilitating access to information for the public. Thus, this web-based archiving information system is expected to be a strategic solution in improving the quality of public services and supporting more appropriate decision-making within the DP3A environment of Nabire Regency.

This research is motivated by the problem of inefficient manual recording of employee attendance at the Central Papua Provincial Bawaslu, which often causes errors and inaccurate attendance data. The main goal of the research is to develop a PHP and MySQL-based digital attendance system that is able to record attendance automatically, real-time, and integrated. The research methods include needs analysis, database and interface design, program code implementation, and system testing. The results of the study show that the digital attendance system developed has succeeded in facilitating the recording of entry times, exit times, and recapitulation of employee attendance with a higher level of accuracy than manual methods. The implications of the implementation of this system are increased administrative efficiency, ease of monitoring, and the provision of more valid attendance data to support fast and appropriate decision-making within the Central Papua Provincial Bawaslu. Thus, this digital attendance system can be a practical solution in improving the quality of human resource management in government agencies.

Evidence is a fundamental element in the legal proof process that serves as the basis for judges to examine, assess, and decide cases in court. Along with the advancement of information technology, the form of evidence has evolved, including digital evidence such as screenshots of conversations through digital communication applications like WhatsApp. This study aims to analyze the legal position and evidentiary value of digital conversation screenshots within the Indonesian legal system. The research method uses a normative juridical approach by examining laws and regulations, legal doctrines, and relevant court decisions. The results indicate that screenshots of conversations have legal recognition as electronic evidence; however, their evidentiary strength is limited and requires support from other forms of evidence. Judges apply the principle of Unus Testis Nullus Testis, meaning that screenshots cannot stand alone as sole evidence and must be supported by additional legal proof to fulfill the requirements of valid, complete, and convincing legal evidence.

Career selection is an important process for students at Darussalam Gontor University (UNIDA) because it influences their academic development and future employment. However, many UNIDA students experience difficulties in determining suitable careers due to a lack of understanding of their psychological characteristics. This study aims to build a Decision Support System (DSS) for career recommendations for UNIDA students based on psychological test results using the Simple Additive Weighting (SAW) method. The psychological data used are non-clinical test results collected through a structured questionnaire from six respondents and converted into numerical scores. The research stages include determining criteria and weights, compiling a decision matrix, normalization process, calculating preference values, and ranking career alternatives using SAW. The career alternatives used consist of academics, corporate professionals, entrepreneurs, managers, and social/public services. The results show that the managerial career alternative obtained the highest preference value of 0.861, followed by entrepreneurs at 0.824, corporate professionals at 0.778, social/public services at 0.737, and academics at 0.703. These findings demonstrate that the SAW method is capable of providing objective and systematic career recommendations based on the psychological profiles of UNIDA students. This research is expected to assist UNIDA students and academics in making more informed career decisions tailored to individual characteristics

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.

This study examines the experience of access to health services and resilience strategies in adolescents with unplanned pregnancies in Makassar, Indonesia. Access to quality reproductive health services is important for adolescents' well-being, but they often face systemic barriers that affect access to health services and outcomes. The study used an Interpretative Phenomenological Analysis (IPA) approach on seventeen adolescent girls aged 15–19 years who had an unplanned pregnancy between June–November 2023. Data were collected through in-depth semi-structured interviews, recorded, verbatim transcribed, and analyzed by a six-stage science process. Two main themes were found: Navigating Closed Doors: Systematic Barriers to Care and Finding Light in Darkness: Resilience and Agency. Participants faced a variety of layered barriers, including a lack of information about health services and rights, geographical and economic constraints, age-based discrimination, parental notification obligations, fear of legal consequences especially related to abortion, family control over decisions, and limitations in adolescent-friendly services. Nonetheless, adolescents show resilience through seeking strategic help, resistance to pressure, spiritual and religious coping, peer support, gradual acceptance of maternal identity, educational sustainability, and positive meaningfulness of difficult experiences. These findings point to the need for a transformation of the health system that not only improves attitudes of health workers, but also addresses structural barriers such as confidentiality, age discrimination, and service availability, while strengthening agency and adolescent coping strategies.

The selection process for students eligible for the National Selection Based on Achievement (SNBP) requires objective and structured assessment because it involves various academic and non-academic criteria. This study aims to develop a Decision Support System (DSS) to determine the ranking of SNBP eligible students at SMAN 8 Surabaya using the Additive Ratio Assessment (ARAS) method. The ARAS method is used to evaluate student alternatives based on their report card scores for semesters 1-5, academic ability tests (TKA), academic achievements, non-academic achievements, discipline, organizational activity, and attendance through a normalization process to obtain relative Ki values. The results of the study show that the system is capable of producing objective student rankings with relative utility values (Ki) ranging from 95.15 to 89.38, where the highest value indicates the best alternative from all alternatives. The application of ARAS-based DSS can improve the efficiency, transparency, and consistency of the SNBP student selection process.

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.

This study aims to analyze the application of personnel management information systems at the West Sumba Regency Personnel and Human Resources Development Agency (BKPSDM). The main focus of the research is to assess how an integrated information system is able to produce up-to-date, accurate, and can support the decision-making process in personnel policy. An integrated information system is expected to facilitate employee data management, improve administrative efficiency, and strengthen transparency in human resource management. The research method uses a qualitative approach with case studies, through interviews with relevant officials, direct observation, and analysis of relevant documents. The results of the study show that the implementation of an integrated personnel management information system has a positive impact in the form of accelerating administrative processes, reducing data input errors, and facilitating access to information for interested parties. However, the research also found a number of challenges, such as the limitation of human resources trained in the use of technology systems and infrastructure that is not yet optimal. This study concludes that strengthening human resource capacity and increasing infrastructure support is a strategic step to optimize the personnel management information system. The research recommendations are expected to be a reference for the West Sumba Regency BKPSDM and other government agencies in developing a more effective and sustainable information system.

Background: Accurate detection of renal cysts on CT urography requires high diagnostic precision, while manual interpretation by radiologists is susceptible to inter-observer variability and potential delays in clinical decision-making. These challenges underscore the need for a reliable automated detection system to support radiological assessment. Objective: This study aims to develop and evaluate the performance of the Neo-ZasAI application based on the YOLOv8 algorithm for the automatic identification of renal cysts. Methods: Employing a Research and Development design using the ADDIE model, the study encompassed needs analysis, model design, software development, system implementation using 200 CT urography images, and diagnostic performance evaluation. Classification results generated by Neo-ZasAI were compared with radiologist readings through confusion matrix analysis and ROC–AUC assessment. Results: The findings indicate that Neo-ZasAI achieved an accuracy of 97,5%, sensitivity of 96%, specificity of 99%, positive predictive value of 98,9%, and negative predictive value of 96,1%. The ROC analysis yielded an AUC of 0.988 (p < 0.001), demonstrating excellent discriminative capability and high concordance with radiologist interpretations as the diagnostic gold standard. Conclusion: These results suggest that Neo-ZasAI is capable of performing rapid, consistent, and accurate renal cyst detection and is thus feasible for implementation as a clinical decision support system in radiology, with potential integration into PACS workflows and further development to enhance model generalizability.

This article explores inconsistencies in employee performance within small, project-oriented service sectors, where quality depends on daily tasks, punctuality, and adherence to procedures. Since skill development relies largely on informal workplace learning, differences in worker competencies often lead to rework, customer complaints, and safety issues. The study investigates how skills and competencies affect employee performance, illustrating how individual abilities translate into observable performance through the ability–motivation–opportunity (AMO) framework and experience-based learning. A qualitative case study at Rajasa Teknik included interviews and observations to examine project coordination, quality standards, and supervision practices. Results indicate that competencies and skills primarily influence ability, but sustained motivation- driven by realistic goals and fair feedback- along with ample opportunities such as smooth material flow, clear roles, and on-site decision support, enhance performance. The findings underline the importance of a cohesive work system (task division, quality control, communication), a consistent safety culture (K3), and straightforward, repetitive HR practices that facilitate tacit knowledge transfer into routines. Improvements suggested involve mapping core competencies, brief mentoring sessions, quality-focused standards, and evaluation systems to minimise service variation and promote sustainable performance in similar businesses. Overall, this research broadens the understanding of performance management in small enterprises and offers practical guidance for interventions.

Employee affective commitment is a critical factor for workforce retention and service quality in healthcare organizations, particularly among nurses who represent the largest professional group in hospitals. Kemayoran X Hospital has experienced persistently high nurse turnover rates, indicating potential deficiencies in affective commitment. This study aimed to examine the influence of perceived organizational support and organizational justice on nurses’ affective commitment, with work engagement serving as an intervening variable. A quantitative cross-sectional design was employed, involving 125 staff nurses at Kemayoran X Hospital selected through purposive sampling. Data were collected using structured self-administered questionnaires distributed via Google Forms and analyzed using Structural Equation Modeling–Partial Least Squares (SEM-PLS). The results demonstrated that perceived organizational support, organizational justice, and work engagement simultaneously exerted a significant effect on affective commitment. Furthermore, perceived organizational support and organizational justice showed significant positive effects on work engagement, which in turn significantly influenced affective commitment. Mediation analysis confirmed that work engagement partially mediated the relationships between perceived organizational support and affective commitment, as well as between organizational justice and affective commitment. These findings indicate that nurses who perceive fair treatment and strong organizational support are more likely to be engaged in their work and emotionally committed to their organization. In conclusion, strengthening organizational support systems, ensuring fairness in decision-making processes, and fostering work engagement are essential managerial strategies to enhance nurses’ affective commitment and reduce turnover in hospital settings.