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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.

During the coal stockpiling process, the quality of coal may increase or decrease due to direct exposure to open environmental conditions, which can lead to changes in its characteristics. The longer the coal is stored in an open area, the more it undergoes changes caused by rainfall, heat, and air exposure, resulting in an increase in moisture content and ash content, while the calorific value decreases. Therefore, this research was conducted to determine the optimal coal stockpiling duration at the ROM coal stockpile to ensure that the calorific value does not significantly decrease. Coal sampling was carried out every two days from the initial time of stockpiling. After a two-month stockpiling period, the final coal quality results showed a total moisture of 13.89% (ar), inherent moisture of 15.95% (ad), ash content of 4.59% (ad), volatile matter of 40.3% (ad), and fixed carbon of 39.16% (ad). Based on these results, it can be concluded that the recommended storage duration for MCV-HS type coal at the ROM coal stockpile is 154 days. The laboratory analysis results obtained during the research indicate that the longer the coal is stored, the higher the moisture content and ash content become, while the calorific value continues to decrease. This occurs due to water absorption and oxidation reactions that take place during the coal storage period in the ROM coal stockpile.

This study discusses the introduction of digital signature patterns using the Backpropagation method on Artificial Neural Network (JST) to identify a person's characteristics and potential. The increasing use of digital identities demands a verification system that is more secure, accurate, and adaptive to the variations of each individual's signature. The main problem faced in the signature recognition system is the low level of accuracy when the visual features of the signature have similarities between users, both in terms of shape, size, and stroke pressure. In addition, variations of signatures made by the same individual are also a challenge in the identification process. As a solution, this study implements Principal Component Analysis (PCA) to extract important features from the signature image before the training process using JST. PCA is used to reduce the data dimension so that the learning process becomes more efficient and optimal. A total of 80 signature images were used in this study, consisting of 60 training data and 20 test data. The results showed that the system was able to achieve an accuracy level of 92.5%. These findings prove that the combination of PCA and JST methods is effective in recognizing digital signature patterns and has the potential to be applied to digital security-based biometric identification systems.

This study aims to analyze the effectiveness of technical personnel management in handling main engine failures during extreme weather conditions at sea. The main focus of this study is to assess the extent to which technical competence, communication, coordination, and preparedness of technical personnel contribute to the effectiveness of damage management. The method used is a descriptive quantitative approach with data collection through a closed-ended questionnaire based on a Likert scale. A total of 100 respondents who are ship engineering officers currently studying at a maritime campus were sampled. The results of the analysis show that the four independent variables (technical competence, communication, coordination, and preparedness) simultaneously have a significant effect on the effectiveness of handling main engine failures. From the results of the multiple linear regression test, the coefficient of determination (R²) value of 0.897 indicates that 89.7% of the variation in damage management effectiveness can be explained by these four variables. This finding indicates that good technical personnel management plays a significant role in reducing the risk of engine system failure during extreme weather.

Sweet corn is a horticultural commodity that is widely consumed in Indonesia. This study evaluated the effectiveness of NPK 18-18-18 fertilizer on the vegetative growth, yield components, and relative agronomic effectiveness (RAE) of sweet corn (Zea mays saccharata). Field experiments were conducted using fertilizer dosages of 0,50, 0,75, 1,00, and 1,50 NPK, with a control treatment for comparison. The application of NPK 18-18-18 significantly increased plant height, stem diameter, leaf number, ear length, ear diameter, biomass weight, ear weight with husk, ear weight without husk, plot yield, and overall productivity compared with the control. Mean values across treatments ranged from 68,94–205,72 cm for plant height, 7,41–20,47 mm for stem diameter, 6,01–13,00 leaves per plant, 15,41–20,89 cm for ear length, and 36,05–49,65 mm for ear diameter. Biomass weight ranged from 0,12–0,34 kg, ear weight with husk from 0,13–0,34 kg, and ear weight without husk from 0,12–0,28 kg. Plot yield varied between 7,91–25,46 kg, corresponding to productivity levels of 5,02–16,16 t/ha. RAE analysis indicated that fertilizer application was effective at dosages of 0,75, 1,00, and 1,50 NPK, with the highest effectiveness observed at 1,50 NPK (118%). Notably, the 0,75 NPK dosage achieved an RAE value of 101%, demonstrating that lower fertilizer input can enhance yield while reducing production costs and mitigating fertilizer scarcity. These findings suggest that NPK 18-18-18 fertilizer, when applied at an optimal dosage, can be effectively utilized in sweet corn cultivation to improve growth and productivity while ensuring efficient nutrient management.

As a high-value crop, potatoes necessitate balanced nutrient management for optimal growth and yield. This research aimed to assess how varying applications of NPK 20-20-10 fertilizer influenced potato growth, yield, tuber quality, agronomic efficiency, and economic viability within tropical climates. The experimental setup involved a randomized complete block design, incorporating four replications across seven distinct treatments: a control, a standard inorganic fertilization regimen, and NPK 20-20-10 applied at 0.50, 0.75, 1.00, 1.25, and 1.50 times the suggested dosage. The findings indicated that applying NPK 20-20-10 significantly enhanced several parameters, including plant height, branch count, tuber count, tuber weight, and overall yield components, when contrasted with the control group. Notably, the 1.25 times recommended dose demonstrated superior performance, leading to a 34.9% increase in tuber number and a 68.6% rise in tuber weight compared to the control. Agronomic effectiveness scores surpassed 100 for dosages ranging from 0.75 to 1.50, with the 1.25 dose registering the peak value. Economic evaluations confirmed the profitability of all NPK treatments, and the 1.25 dose yielded the most favorable R/C ratio and a net profit of IDR 29,053,400. Consequently, the recommended application for potato cultivation is 675 kg/ha of NPK 20-20-10, distributed in three equal parts at planting, four weeks post-planting, and six weeks post-planting. Thus, these results underscore that NPK 20-20-10, when applied at 1.25 times the recommended rate, presents an agronomically effective and economically sound strategy for sustainable potato farming in tropical settings.

This study examines the bioethical and Sharia dilemmas arising from the production of slaughter-free meat through animal cell culture as a modern food innovation. Concerns regarding the halal status of the cell source, the use of culture media such as Foetal Bovine Serum, and ethical issues related to animal welfare necessitate an in-depth analysis of the halal status and moral implications of this technology. The research method employs a literature study with a descriptive qualitative approach, reviewing journals, books, and contemporary fatwas related to cultured meat, bioethics, and Islamic law. The results indicate that the Sharia aspect heavily depends on the cell source, the medium used, and the culture process, while the bioethical perspective highlights animal welfare, scientific transparency, and the moral responsibility of researchers. Furthermore, the acceptance among Muslim communities is significantly influenced by trust in halal certification bodies and the availability of transparent information. This study affirms the need for biotechnology-based halal standardization and the development of fully halal media and supporting materials to ensure the widespread acceptance of cultured meat in the future.

This study examines the role of teachers as Islamic counselors in addressing various psychological problems experienced by students, such as learning anxiety, academic stress, low self-confidence, difficulty controlling emotions, and social problems such as bullying. Teachers serve not only as instructors, but also as murabbi, mursyid, and moral role models in guiding students' spiritual and emotional development. The study used a qualitative method with a library study approach, where data were obtained through relevant literature related to Islamic counseling, Islamic education, and student psychology. The results of the study indicate that the implementation of Islamic counseling through strategies such as mau'izhah hasanah, tazkiyah an-nafs, prayer and dhikr, empathy development, and collaboration with guidance and counseling teachers and parents has been proven to reduce students' anxiety and stress levels, increase learning motivation, strengthen morals and character, and provide spiritual peace. Thus, teachers play a strategic role in creating a holistic educational environment and supporting students' psychological well-being. In addition, the implementation of Islamic counseling also plays a role in building students' mental resilience in facing academic and social pressures. In the long term, this will strengthen the Islamic values ​​applied in students, while creating a more resilient and noble generation.

This research examines money politics as a root problem in Indonesia’s democratic system, focusing on the 2019 and 2024 general elections. Money politics refers to the practice of distributing cash or goods by candidates, campaign teams, or volunteers to influence voters’ political choices. This study employs a qualitative method with a literature study approach to analyze several cases that occurred across different regions in Indonesia. The findings reveal that money politics was widespread during both elections, with the main modus operandi involving the distribution of cash, basic goods, and facilities. This practice not only violates the principles of free and fair elections but also undermines citizens’ dignity, weakens popular sovereignty, and serves as a major driver of political corruption. Contributing factors include power ambition, vulnerable economic conditions, low political education, weak oversight, and entrenched transactional political culture. To address this issue, the research highlights the importance of political party reform and strengthening democratic education, particularly through civic education programs. These efforts aim to increase political awareness among citizens and improve the overall quality of Indonesia’s democracy.

This study used a qualitative approach with a Systematic Literature Review (SLR) as the data collection technique. This study examined the institutional factors causing long dwelling times at Tanjung Emas Port and mitigation efforts. The analysis revealed that the main root of the problem lies in the lengthy administrative and goods inspection (customs) processes, particularly in the red, yellow, and green inspection lanes. Obstacles include the lack of data integration (such as PIB and SPPB dates) between the Semarang Container Terminal (TPKS) and Customs, as well as incomplete documents by service users. A significant impact was felt on imports, where dwelling times were longer due to complicated quarantine and customs inspections, while exports were relatively unaffected.