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This study aims to analyze the marketing strategies of Islamic bank products based on sharia values implemented in Mojoarno Village (Ikhsanudin et al. 2024). The background of this research is the increasing development of Islamic banking in Indonesia which is not always followed by sucient understanding of sharia nancial products at the rural level (Qothrunnada et al. 2023). This condition requires Islamic banks to design marketing strategies that are not only focused on product promotion, but also on education, empowerment, and strengthening public trust (Damayanti 2023). This research uses a qualitative case study approach on an Islamic bank that actively conducts marketing activities around Mojoarno Village (Yin 2018). Data were collected through in-depth interviews with bank marketing sta , religious leaders, and customers, participatory observation in socialization and religious activities, as well as documentation of promotional materials and internal reports (Miles and Huberman 2014). The data were analyzed through data reduction, data display, and conclusion drawing with source and technique triangulation to ensure validity. The ndings show that marketing strategies integrated with sharia values—such as justice, transparency, and avoidance of riba, gharar, and maysir—combined with religious and cultural approaches through mosques and majelis taklim can increase public literacy and interest in Islamic bank products (Rahman, Aji, and Sopingi 2023). However, several challenges still remain, including low initial nancial literacy, strong informal nancial practices, and limited marketing resources in rural areas (Syifa, Nasution, and Inayah 2024). The implications of this research emphasize the importance of synergy between Islamic banks, religious leaders, and local communities to develop sustainable sharia-based marketing models in rural contexts.

The integration of Artificial Intelligence (AI) into Human Resource Management (HRM) is accelerating and reshaping how organizations attract, develop, manage, and retain talent. Despite abundant case examples and growing practitioner interest, academic findings remain fragmented regarding the antecedents (drivers), impediments (barriers), and organizational effects (outcomes) of AI-based HR transformation. This paper presents a PRISMA-guided systematic literature review of 112 peer-reviewed articles (2015–2025) to synthesize empirical and conceptual evidence on AI in HRM. Results identify three primary drivers: technological capability, strategic alignment, and a data-driven culture; three critical barriers: ethical concerns (bias, privacy, and transparency), skill and capability gaps, and resistance to change; and three outcome clusters: operational efficiency, enhanced employee experience, and elevated strategic HR contribution. We propose a socio-technical conceptual framework that models drivers moderated by barriers to outcomes, and we advance a research agenda focused on ethical governance, human–AI collaboration, capability measurement, and longitudinal evaluation. The review contributes to theory by integrating socio-technical and dynamic capability  perspectives and provides actionable guidance for HR leaders on responsible AI adoption.

Political participation is an important aspect in a democratic state structure, as well as a characteristic feature of political modernization. Political participation influences the legitimacy of society regarding the running of a government. One way of implementing political participation is through General Elections (Pemilu). The aim of this research is to determine the influence of presidential candidate debate broadcasts on multi-platform broadcast media on the political participation of FISIP UMJ student class of 2020. This research has an independent variable, namely presidential candidate debate broadcasts with the dimensions of frequency, attention and duration, and has a dependent variable, namely providing voting rights in elections, lobbying with officials, becoming a member of a political party. This research method uses a survey method by distributing questionnaires online to respondents via Google Form which aims to collect data from a sample of 2020 FISIP UMJ students who actively watch presidential candidate debates and know about political participation, with a total of 66 respondents selected. The data collection tool uses a questionnaire using a Likert Scale. The results of this research show that the presidential debate broadcast has an influence on political participation, which has a value of 0.736 or 73.6%, which means that the presidential debate broadcast influences political participation by 73.6% and the rest is influenced by other factors.

This study aims to determine the effectiveness of the Jakarta Smart Card Plus (KJP Plus) Program at the Junior High School (SMP) level in South Jakarta in 2024. The program is an initiative by the Provincial Government of DKI Jakarta to ensure access to education for underprivileged families. This research employs Sutrisno’s (2007) program effectiveness theory, which includes five key indicators: program understanding, target accuracy, timeliness, goal achievement, and real change. The research method used is a descriptive qualitative approach, with data collection techniques including interviews, observations, and documentation involving informants from the South Jakarta Region I Education Sub-Department, school principals, students, and parents of KJP Plus beneficiaries. The results of the study indicate: (1) Understanding of the program is still uneven, particularly among parents who lack knowledge about the mechanism and use of KJP Plus; (2) Target accuracy is not optimal, as some recipients are economically capable, such as those who own cars or fall into higher welfare deciles; (3) The timeliness of fund distribution is generally good, although there are still some administrative delays; (4) Goal achievement is evident through reduced school dropout rates and increased educational participation, but not evenly across all areas; and (5) Real change is felt by most beneficiaries through easier access to education and provision of school supplies, although misuse of funds for non-educational purposes is still present. Overall, the effectiveness of the KJP Plus program at the SMP level in South Jakarta is deemed suboptimal, highlighting the need for improved data accuracy, stricter fund usage monitoring, and broader program socialization.

The issue of street vendors (PKL) in Sidoarjo Regency, particularly in the Gading Fajar area, has become a significant concern as it relates to public order, the use of public space, and the economic dynamics of small communities. To address these challenges, the Sidoarjo Regency Government enacted Regional Regulation Number 3 of 2016 as the legal basis for structuring and empowering PKL. This study examines how the regulation is implemented in practice and evaluates its effectiveness using a qualitative descriptive method through interviews, observations, and documentation, analyzed with Leo Agustino’s policy evaluation model, which assesses five key aspects: administrative resources, institutional coordination, infrastructure and technology, financial support, and regulatory adequacy. The findings show that the implementation of the regulation has not yet reached optimal performance, as limited socialization, insufficient personnel, weak coordination among agencies, inadequate supporting facilities, and low compliance with zoning rules hinder the achievement of policy objectives. These issues are reflected in the continued presence of PKL operating in non-designated areas despite clear regulatory provisions. Therefore, strengthening institutional capacity, improving facilities, enhancing enforcement consistency, and developing more operational technical guidelines are essential to ensure more effective and sustainable management and empowerment of street vendors in Sidoarjo Regency.

This research examines the strengthening of inter-agency collaboration through an integrated implementation model in the social rehabilitation program for people with physical disabilities at UPT Bina Daksa Pasuruan, East Java, Indonesia. The social rehabilitation program for persons with disabilities faces significant challenges in coordination, resource allocation, and service integration among multiple stakeholders, including government agencies, non-governmental organisations, and community groups. This study employs a qualitative case study design, using in-depth interviews, focus group discussions, and document analysis to collect data from program implementers, beneficiaries, and related stakeholders. The findings reveal that effective inter-agency collaboration requires five key elements: shared vision and goals, clear communication channels, adequate mechanisms for resource sharing, strong leadership commitment, and continuous monitoring and evaluation systems. The integrated implementation model developed in this study emphasises horizontal and vertical coordination, participatory planning, capacity building initiatives, and community empowerment strategies. The results demonstrate that strengthening inter-agency collaboration through this integrated model significantly improves program effectiveness, service quality, and beneficiary satisfaction. This research contributes to the theoretical understanding of collaborative governance in disability services and provides practical recommendations for policymakers and practitioners in developing countries

There is a mismatch between the policy of Contextual teaching and learning (CT&L) and learning in reading skills and the practice in the Indonesian higher education context. This is a case study that examines the beliefs of Indonesian higher education English lecturers about CT&L in English reading lessons. Context plays a pivotal role in English teaching, especially in reading, since it can connect teaching materials with students’ real-life context. The interviews revealed that teachers believe teaching English reading skills should be contextualized. However, these practices were not always easy because there were constraints in the form of decontextualized textbooks and uncontextualized teaching. The findings of this research would offer a recommendation for policy makers, English teachers in higher education context and future researchers interested in how to make English language learning in reading more contextual

This study is motivated by the high frequency and significant impacts of flooding in Indonesia, particularly the recurrent flooding of the Kali Lamong River in Gresik Regency, which causes substantial socio-economic losses each year. Although disaster management is normatively regulated by national policies, its effectiveness depends largely on collaborative governance among multiple actors. This study aims to analyze the practice of collaborative governance in flood mitigation in Gresik Regency using the model of Weber, Lovrich, and Gaffney (2005), which includes vertical, horizontal, and partnership dimensions, and to identify the key challenges that hinder its implementation. A descriptive qualitative approach was employed in flood-affected areas along the Kali Lamong River, involving BPBD, DPUTR, sub-district governments, village authorities, and local communities through interviews, observations, and document analysis. The findings reveal that collaboration has been established and functions relatively well, particularly in hierarchical coordination, division of roles among government agencies, strengthening of Destana, KENCANA, and SPAB programs, as well as CSR involvement and community participation all contributing to reduced flood duration. However, the implementation of collaborative governance remains constrained by slow land acquisition and infrastructure development, a predominantly reactive orientation, suboptimal early-warning systems between upstream and downstream areas, and uneven support from the private sector and academia. The study concludes that strengthening collaborative mechanisms and accelerating the resolution of structural barriers are essential to achieve more sustainable flood mitigation efforts in Gresik.

This article examines the limitation of transparency within the Indonesian House of Representatives (DPR) during the formulation of the Job Creation Act (Law No. 11 of 2020) and its implications for legislative performance and public participation. Transparency represents a fundamental requirement in a democratic legal system as it ensures accountability, public oversight, and the legitimacy of legal products. However, the legislative process of the Job Creation Act demonstrated significant procedural issues, including inconsistent draft versions, restricted access to essential documents, accelerated deliberation, and the marginalization of meaningful public participation. This study highlights how these limitations hinder the public’s constitutional rights, weaken legislative oversight, and create asymmetrical power relations that enable elite dominance in policymaking. The lack of transparency also led to procedural defects acknowledged by the Constitutional Court, reflecting a systemic decline in democratic legislative practices. Using a normative juridical method supported by legislative analysis and doctrinal studies, this paper argues that the absence of transparency not only reduces the quality of participation but also erodes the legitimacy and accountability of the DPR. The findings emphasize the urgent need for open access to legislative documents, inclusive public consultation, and strengthened accountability mechanisms to ensure democratic and lawful policy making.  

This study aims to explore the philosophy of Islamic education grounded in tolerance by revisiting the influential ideas of Gus Dur and assessing their significance for the younger generation in contemporary society. Adopting a qualitative descriptive approach through document analysis, in-depth interviews, and thematic interpretation, the research highlights that values such as tolerance, humanism, intellectual freedom, and respect for diversity are essential foundations for shaping modern Islamic education. The findings indicate that incorporating these principles into educational curricula not only enriches the learning experience but also strengthens inclusive and participatory teaching practices, fostering an environment that encourages open dialogue, mutual understanding, and moderation. Furthermore, the study emphasizes that systematic integration of Gus Dur’s educational philosophy can contribute to the development of students’ critical thinking, ethical awareness, and social responsibility, ensuring that Islamic education remains relevant and responsive to contemporary societal challenges. Overall, the research concludes that implementing these values consistently is crucial for advancing a modern, tolerant, and pluralistic approach to Islamic education that benefits both individuals and society as a whole.

The rapid growth of social media in Indonesia has transformed marketing communication strategies, particularly in producing creative promotional content. Instagram Reels has emerged as an effective platform for building brand awareness through short and engaging videos. This study was conducted during an internship at PT Waskita Fim Perkasa Realti and focuses on the production process of Instagram Reels content for Vasaka Solterra. The first objective is to examine the workflow of promotional video production across three stages, pre-production, production, and post-production. The second objective is to identify challenges faced by the Marketing Communication Division. Using qualitative methods, including observation, interviews, participation, and literature review. This study found that pre-production involves idea development, content research, and storyboard creation. The production stage includes smartphone-based video shooting and voice-over recording to ensure message clarity. Post-production centers on editing using CapCut and applying a consistent visual identity before publication. The study also identifies several obstacles, such as limited human resources, minimal equipment availability, and coordination delays with external partners. These findings highlight the need for increased production personnel, improved interdepartmental collaboration, and enhanced equipment support to optimize the overall content production process.

Recommender systems play a critical role in shaping user decisions across digital platforms; however, the increasing complexity of recommendation algorithms has raised serious concerns regarding transparency, trust, and accountability. This study focuses on enhancing the transparency of recommender systems by integrating Explainable Artificial Intelligence (XAI) techniques within a MovieLens-based recommendation framework. The primary problem addressed is the opacity of conventional recommendation models, which limits user understanding of why certain items are recommended and may reduce trust, perceived fairness, and system acceptance. Accordingly, the main objective of this research is to design and evaluate a hybrid explainable recommender system that balances predictive accuracy with human-understandable explanations. The proposed approach combines Matrix Factorization, feature-importance-aware neural networks, and knowledge graph embeddings to construct a robust recommendation model. To enhance explainability, multiple XAI strategies are integrated, including model-agnostic methods (LIME, SHAP, and CLIME), argumentation-based explanations, and context-aware personalized explanations. A comprehensive evaluation framework is employed, incorporating algorithmic metrics (accuracy, fidelity, robustness, counterfactual consistency, and fairness) alongside human-centered evaluations measuring trust, transparency, cognitive load, and perceived usefulness. Experimental results demonstrate that the knowledge graph–enhanced hybrid model achieves superior recommendation accuracy compared to baseline approaches. Moreover, context-aware explanations consistently outperform other methods in terms of fidelity, robustness, and user-perceived transparency, while argumentation-based explanations are found to be the most persuasive. CLIME offers a strong balance between technical stability and interpretability. The findings indicate that no single explainability technique is universally optimal; instead, hybrid and adaptive explanation strategies are most effective. In conclusion, this study confirms that human-centered, context-adaptive XAI significantly improves transparency and user trust in recommender systems, highlighting explainability as a fundamental component rather than an optional enhancement.

Diseases in primary health services exhibit complex spatial-temporal dynamics due to urbanization and population mobility. Conventional surveillance approaches are difficult to capture these patterns adaptively. Machine learning (ML) based on spatio-temporal modeling offers a solution with the ability to detect disease clusters automatically and with high precision. Research Objectives: This research aims to develop a machine learning model to detect disease hotspots from primary service data in Indonesia, with a focus on improving prediction accuracy, interpretability, and relevance of health policies. Methodology: The primary service dataset for 2024 (5,343 entries) was analyzed using three ML models Gradient Boosting Machine (GBM), Temporal Random Forest (TRF), and Multi-EigenSpot with spatial (village) and temporal (week, month) features. Performance evaluation includes predictive (AUC, F1-score) and spatial (Moran's I, Spatio-Temporal Correlation Index) metrics. Results: The results showed that Multi-EigenSpot achieved the best performance (AUC=0.91; F1=0.86), with the detection of dominant hotspots in Sungai Asam and Beringin Villages. Moran's I value of 0.63 indicates a strong spatial autocorrelation, while STCI=0.57 indicates moderate temporal stability. Conclusions: ML-based spatio-temporal models are effective in identifying hidden disease patterns and have the potential to be integrated into national digital surveillance systems. This approach supports precision public health by providing a scientific basis for real-time location- and time-based intervention policies.

End-to-end autonomous driving has emerged as a promising paradigm in which deep neural networks directly map raw visual inputs to continuous control actions. Despite its effectiveness, this approach suffers from limited transparency, posing significant challenges for deployment in safety-critical driving scenarios. This study addresses the lack of interpretability in vision-based end-to-end autonomous driving systems and aims to analyze model decision-making behavior under critical conditions such as sharp steering maneuvers and abrupt control transitions. To this end, an explainable end-to-end autonomous driving framework is proposed, combining a convolutional neural network trained via imitation learning with gradient-based visual attribution techniques, including Grad-CAM. The model predicts continuous steering, throttle, and braking commands directly from front-facing camera images, while explainability mechanisms are applied to reveal input regions influencing each control decision. Model performance is evaluated using both prediction accuracy and safety-oriented behavioral metrics. Experimental results show that the proposed explainable model achieves lower control prediction errors compared to a baseline end-to-end CNN, reducing steering mean squared error from 0.034 to 0.031, throttle error from 0.021 to 0.019, and brake error from 0.018 to 0.016. Moreover, safety-oriented analysis indicates improved driving stability, with steering variance reduced from 0.087 to 0.072 and abrupt control changes decreased from 14.6 to 10.3 events. Visual explanations consistently highlight road surfaces and lane-related structures during complex maneuvers, indicating reliance on semantically meaningful cues. In conclusion, the results demonstrate that integrating explainability into end-to-end autonomous driving not only preserves predictive performance but also correlates with smoother and more stable driving behavior. This framework contributes to the development of transparent and trustworthy autonomous driving systems suitable for safety-critical applications

Abstract: The rapid diffusion of Generative Artificial Intelligence (AI) has intensified public debate regarding its benefits, risks, and societal implications. This study investigates public sentiment and thematic structures surrounding Generative AI by analyzing Twitter discourse as a representation of large-scale, real-time public perception. The research addresses two main problems: how public sentiment toward Generative AI is distributed and what dominant themes shape this perception. Accordingly, the objective is to map both emotional polarity and thematic narratives embedded in social media conversations. A computational mixed-methods approach was employed using a dataset of 12,470 tweets collected on 17 December 2024. Sentiment classification was conducted using a transformer-based DistilBERT model, while semantic representations were generated with Sentence-BERT. Topic modeling was performed using BERTopic, integrating HDBSCAN clustering and class-based TF-IDF to extract coherent and interpretable topics. Human-in-the-loop validation supported the interpretive robustness of topic labeling. The findings reveal that public sentiment toward Generative AI is predominantly positive (41.8%), particularly in relation to productivity enhancement, education, and creative applications. Neutral sentiment (31.4%) reflects informational discourse, while negative sentiment (26.8%) centers on ethical concerns, privacy risks, misinformation, and AI hallucinations. Seven dominant topics were identified, with clear topic–sentiment alignment showing optimism in utility-driven themes and skepticism in ethics- and risk-related discussions. In conclusion, public perception of Generative AI is dualistic—characterized by strong enthusiasm alongside persistent caution. These results provide empirical insights for AI governance, responsible innovation, and future research on socio-technical impacts of Generative AI. *    

: The rapid expansion of cybersecurity standards and threat intelligence frameworks has led to significant semantic fragmentation among security terminologies, hindering effective information retrieval and interoperability across systems. Traditional keyword-based search approaches are inadequate for capturing the contextual meaning of security terms, particularly within formal frameworks such as NIST, MITRE ATT&CK, and CWE. This study addresses this challenge by proposing CyberBERT, a transformer-based semantic search framework designed to align cybersecurity terminologies through deep contextual representation and ontology-driven reasoning. Research Objectives: The primary objective of this research is to develop a semantic retrieval model capable of understanding conceptual relationships between security terms beyond lexical similarity. Methodology: The proposed methodology fine-tunes a BERT-based model on the NIST Glossary corpus using a combination of masked language modeling and triplet loss objectives to generate discriminative semantic embeddings. These embeddings are further aligned with cybersecurity ontologies, including MITRE ATT&CK and CWE, to enhance semantic consistency and explainability. Semantic retrieval is performed using cosine similarity within a 768-dimensional embedding space and evaluated using Mean Reciprocal Rank (MRR) and Precision@K metrics. Results: Experimental results demonstrate that CyberBERT achieves an MRR of 0.832, outperforming domain-adapted baselines such as SecureBERT and CyBERT. The integration of ontology alignment improves semantic accuracy by over 6%, while robustness evaluations confirm resilience against adversarial linguistic perturbations. Visualization using t-SNE reveals coherent semantic clustering aligned with the five core NIST Cybersecurity Framework functions. Conclusions: In conclusion, CyberBERT effectively bridges semantic gaps across cybersecurity terminologies by combining transformer-based contextual learning with ontological reasoning. The framework offers a robust, interpretable, and scalable solution for semantic search, supporting improved interoperability and knowledge discovery in cybersecurity operations and standards harmonization.

Low Birth Weight (LBW) remains a critical global health issue that significantly contributes to neonatal morbidity and mortality, particularly in developing countries such as Indonesia. The main challenge in addressing LBW lies in its complex and multifactorial risk profile, which involves biological, social, environmental, and healthcare-related determinants. This study aims to analyze and synthesize the risk factors associated with LBW based on recent scientific literature. A literature review method was applied by searching articles from Portal Garuda, DOAJ, PubMed, and Google Scholar published between 2020 and 2025 using relevant keywords. The findings indicate that maternal age, interpregnancy interval, nutritional status, anemia, preeclampsia, infections, socioeconomic conditions, environmental exposure, and the quality of antenatal care are significant determinants of LBW. The synthesis of evidence confirms that LBW is influenced by the interaction of multiple individual and healthcare system factors rather than a single cause. In conclusion, this study highlights the urgent need to strengthen antenatal care services, improve maternal nutritional status, control maternal diseases during pregnancy, and implement community-based promotive and preventive strategies as key efforts to reduce the incidence of LBW.

Abortion remains a complex reproductive health issue due to the interplay of multiple interrelated determinants. This study aimed to map the factors influencing the incidence of abortion based on recent scientific evidence. A literature review design was applied by analyzing ten selected articles published within the last five years and retrieved from major scientific databases. The selection process was conducted systematically through title, abstract, and full-text screening based on predefined inclusion criteria. The extracted data included study characteristics, type of abortion, examined determinants, and key conclusions. The synthesized findings indicate that abortion incidence is shaped by a combination of biological, social, and healthcare system related factors. Clinical determinants such as maternal age, endocrine disorders, uterine anatomical abnormalities, obstetric history, anemia, and hypertension play a substantial role in spontaneous and recurrent miscarriage. In contrast, structural factors including income level, contraceptive access, and legal regulations predominantly influence induced abortion. The discussion highlights that abortion should not be viewed as an isolated clinical event, but rather as the cumulative outcome of risks operating across multiple levels of influence. In conclusion, abortion represents a multifactorial phenomenon that requires comprehensive prevention strategies extending beyond medical interventions alone. These strategies should also address healthcare accessibility and broader social conditions. This review contributes to a deeper understanding of the complexity of abortion determinants and provides an evidence-based reference for developing more effective preventive approaches in the future.

A research about study of sandstone slope stability using the Bishop Simplified method in Uu Samarinda has been conducted. This study was conducted to analyze the rebound number values of sandstone slopes, evaluate their stability level, and calculate the safety factor using the Bishop method. The results showed that the rebound number values were 22.34 at point 1, 19.83 at point 2, and 18.07 at point 3. The Uniaxial Compressive Strength (UCS) values at the observation points were 1.90 MPa, 1.62 MPa, and 2.21 MPa, respectively. Geological Strength Index (GSI) evaluation indicated a rating of 80–85, demonstrating intact/massive rock structure, fresh and unweathered rock surfaces, and very good rock quality. Based on the Bishop method analysis, the slope factor of safety in 6.525  with a probability of failure is 0.000%, indicating that the sandstone slope in Ulu Samarinda is highly stable even under external pressures such as heavy rainfall or minor earthquakes. This study provides crucial information on the mechanical characteristics and stability of sandstone slopes in ulu Samarinda, which can serve as a reference for technical planning, geotechnical risk mitigation, and the sustainable development of safe areas.

This study aims to determine the relationship between learning motivation and learning outcomes in SMAW welding engineering among eleventh-grade students at SMKN 2 Solok. This research employed a quantitative method with a correlational approach. The population consisted of all students from classes XI TPM 1 and XI TPM 2, totaling 51 students, using a total sampling technique. Learning motivation data were collected through a validated and reliable questionnaire, while learning outcome data were obtained from post-test scores in the SMAW welding subject. Data were analyzed using the Pearson Product Moment correlation test with the assistance of SPSS software. The results showed a correlation coefficient of r = 0.783 with a significance value of 0.000 < 0.01, indicating a strong, positive, and statistically significant relationship between learning motivation and students’ learning outcomes. These findings suggest that higher learning motivation leads to better learning outcomes in SMAW welding engineering. Therefore, learning motivation plays an important role in improving students’ academic performance. This study is expected to provide useful insights for teachers and schools in developing instructional strategies that enhance students’ motivation and learning outcomes.