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Economic forecasting is becoming increasingly important year after year, especially during crises such as the pandemic of COVID-19 and the Russia-Ukraine war. Its development can be seen from the use of basic statistical models to the increasingly widespread use of machine learning technology. Economic forecasting plays an important role in helping to formulate policies and is also a reliable tool for researchers in dealing with uncertainty. Global crises, such as inflationary pressures due to the pandemic and supply chain disruptions from the Russia-Ukraine conflict, have prompted increased research in this field in an effort to anticipate economic shocks and emphasize the urgency of forecasting to prepare strategies for dealing with future uncertainty. This literature review uses the Scopus database with 2561 publications from 2020 to 2025, analyzed using R Studio with a bibliometrix approach (specifically biblioshiny) and VOSviewer to map relevant thematic connections. This analysis shows that economic forecasting is greatly influenced by market uncertainty and geopolitical factors, and at the same time influences public policy formulation and financial stability. Research contributions from Indonesia are still limited, with only 40 documents, thus emphasizing the need to strengthen economic forecasting studies in Indonesia to support monetary policy and national financial stability.

This study aims to analyze the influence of macroeconomic variables in the form of the dollar exchange rate, inflation, and Gross Domestic Product (GDP) on the Composite Stock Price Index (JCI) in Indonesia for the period 2010–2024. The research method used is a quantitative approach with multiple linear regression analysis using time series data obtained from Bank Indonesia, the Central Statistics Agency (BPS), and the Indonesia Stock Exchange (IDX). The data analysis technique was carried out through classical assumption tests and hypothesis testing to determine the relationship between variables. The results of the study show that partially GDP has a significant effect on the JCI, while inflation and the dollar exchange rate tend not to have a significant effect. However, simultaneously these three variables have a significant influence on the JCI. These findings show that macroeconomic stability is very important in maintaining the performance of the capital market in Indonesia and can be a reference for investors in making investment decisions. In addition, the results of the study confirm that national economic growth is the main indicator that market participants pay attention to in assessing investment prospects. Therefore, the government needs to maintain economic stability through effective and sustainable fiscal and monetary policies.

The development of digital technology has led to an increase in the use of short video-based entertainment applications, including the Melolo application. However, the free version still has various complaints, such as inconsistent subtitles, unintuitive navigation, force close glitches, and unstable advertisements, so user satisfaction analysis is needed. This study aims to measure the level of satisfaction of users of the free version of the Melolo application using the End User Computing Satisfaction (EUCS) method, which covers five variables, namely content, accuracy, format, ease of use, and timeliness. Data was collected through an online questionnaire of 385 Melolo app users in Jambi City and analyzed using Structural Equation Modeling (SEM) with the help of SmartPLS 4. The results showed an R-Square value of 0.546, indicating that the model was able to explain 54.6% of the changes in user satisfaction levels. The variables of content and timeliness were found to have a significant effect on user satisfaction, while accuracy, format, and ease of use had no significant effect. These results indicate that content quality and system timeliness are the main factors in increasing user satisfaction. Therefore, Melolo app developers are advised to maintain content quality and improve system performance and stability to optimize the user experience.

This study focuses on analyzing the welfare index in the ASEAN region (covering six major countries) by comparing two perspectives: objective welfare (Human Development Index/HDI) and subjective welfare (World Happiness Index). Using a balanced panel dataset from 2015–2023, the research applies different econometric approaches for each model, namely the Random Effect Model (REM) for HDI analysis and the Common Effect Model (CEM) for happiness analysis. Empirical findings indicate a striking welfare paradox across the six sample countries. In the objective dimension (HDI), economic stability (GDP) and governance free from corruption (CPI) are proven to be the main positive and significant drivers, while government expenditure (GovExp) shows no meaningful impact, suggesting budget inefficiency. Conversely, in the subjective welfare model, the Easterlin Paradox emerges, as GDP and the corruption index have no significant effect on the happiness index. The happiness levels in these six countries tend to be more influenced by government expenditure. This study concludes that strong economic fundamentals and clean governance free from corruption are essential to building a high quality of human life, whereas citizens’ life satisfaction is more determined by the direct presence of the state through public spending.

The increasing demand for interactive web applications has encouraged the adoption of server-driven approaches such as Livewire as an alternative to building Single Page Applications (SPAs) without complex client-side JavaScript. However, the performance implications of this approach compared to conventional methods remain insufficiently explored. This study presents an empirical comparison between Laravel Blade with AJAX and Livewire in an academic attendance system scenario. Performance evaluation was conducted using k6 on the same web server, complemented by manual browser-based testing to observe actual communication patterns. The results indicate that Livewire exhibits approximately 2.7× higher average response time and up to 6× greater bandwidth consumption than Laravel Blade, primarily due to its snapshot mechanism and state synchronization process. Conversely, Livewire demonstrates better stability, reflected by lower maximum response times and a 0% error rate. These findings highlight a clear trade-off between resource efficiency and development convenience, where Livewire favors stability and developer productivity, while Laravel Blade provides superior efficiency in terms of latency and bandwidth usage.

This study discusses the progress of science in Andalusia during the rule of the Umayyad Daulah II, which served as an essential bridge between the Islamic world and the West. The purpose of this research is to examine the factors that stimulated scientific advancement, the fields of knowledge that developed, and the intellectual values inherited for modern life. This study employs a qualitative approach with a library research method by analyzing various historical sources, academic journals, and scholarly works related to Islamic civilization in Andalusia. The results show that scientific progress in Andalusia under the Umayyad Daulah II emerged from political stability, social tolerance, religious enthusiasm, and government support for education and research. Knowledge flourished in diverse disciplines such as medicine, astronomy, mathematics, philosophy, art, and architecture. Andalusia became a leading center for education and translation, transmitting Islamic knowledge to Europe and contributing greatly to the rise of the Renaissance. In conclusion, the intellectual achievements of Andalusia demonstrate that the harmony between faith, justice, and openness produces a just and progressive civilization. These scientific and moral values remain relevant for guiding the development of a modern civilization grounded in ethics and humanity.

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.

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

This study examines how a Maqasid al-Shariah framework can provide a holistic and ethically grounded foundation for regulating fintech and digital assets across Muslim jurisdictions. Drawing upon classical Islamic legal sources and contemporary fintech literature, the research employs a qualitative library-based methodology to analyze how principles such as ḥifẓ al-māl (protection of wealth), maslahah (public welfare), and harm prevention can guide effective oversight of emerging technologies. Findings indicate that rapid digital financial innovation—particularly involving AI-driven platforms, blockchain systems, P2P lending, crypto-assets, and digital banking—poses significant regulatory challenges related to Shariah compliance, cybersecurity, financial stability, and consumer protection. Muslim jurisdictions face fragmented regulatory structures, inconsistent Shariah interpretations, and limited digital literacy, which hinder the development of cohesive governance frameworks. Integrating Maqasid al-Shariah provides an ethical compass to balance innovation with justice, transparency, accountability, and socio-economic welfare. The study highlights the need for harmonized cross-border standards, robust Shariah governance systems, AI ethics protocols, and regulatory sandboxes tailored to Islamic fintech. Ultimately, the Maqasid framework offers a dynamic and future-ready model for guiding digital finance ecosystems towards ethical resilience, social justice, and sustainable development.

The purpose of this study is to identify the concept of economic empowerment for Muslims in the Qur'an and to understand its role in life. The threat of poverty and powerlessness can undermine faith, morals, security stability, and create social dissatisfaction. Therefore, through verses in the Qur'an, the researcher will discuss the economic empowerment of Muslims from a Qur'anic perspective. The research method used is the maudlu'i interpretation of economics, which includes the following steps: first, collecting verses relevant to the economic issues to be analyzed, both based on keywords and the content of the verses in general and specifically. Second, interpreting these verses by following the sequence of verses in the Mushaf or based on the chronology of the revelation of the surahs. Third, the interpretation model applied is maudlu'i by linking them to the real conditions of the economy. In this study, researchers will examine data contained in the Qur'an, tafsir, hadith, as well as books and journals related to the meaning of economic empowerment of Muslims. The concept of economic empowerment of Muslims from the perspective of the Qur'an is to free individuals from injustice, empower human potential, and encourage individuals to care for others, towards happiness in this world and the hereafter. The role of economic empowerment of Muslims in life includes: contributing to eliminating the practice of usury, human trafficking, monopoly of wealth, and power that triggers poverty, hunger, and fear. In addition, it also motivates individual Muslims to be strong, resilient, persistent, and strive earnestly and not give up.

Partial Denture (GTSL) is an ideal option for patients with missing teeth, with various materials such as thermoplastic nylon (flexible denture), acrylic resin, and metal. Heat-cured acrylic resin and thermoplastic nylon are the two most commonly used materials for denture bases. Denture bases must have sufficient mechanical strength to withstand chewing forces. Acrylic resin, made from polymethyl methacrylate, has become the gold standard in prosthodontics due to its affordability, ease of fabrication, and satisfactory functional results. However, acrylic resin tends to be rigid, brittle, and may cause soft tissue irritation. As an alternative, thermoplastic nylon offers high physical strength, resistance to heat and chemicals, as well as plastic and flexible properties. However, thermoplastic nylon tends to absorb water, change color, and is difficult to repair. Thermoplastic nylon-based dentures provide comfort, aesthetics, and good marginal adaptation, with less soft tissue irritation compared to acrylic resin dentures. Although both materials have comparable characteristics in terms of retention, stability, and durability, flexible dentures are more attractive to patients who prioritize comfort and aesthetics.

This study examines human facial proportions through an anthropometric approach based on the Phi value of the Golden Ratio (φ = 1.618) to understand the relationship between mathematical proportionality and visual aesthetics. The analysis focuses on two public figures, Jin and V of BTS, who are widely recognized as visual representatives within contemporary popular culture. Using two-dimensional anthropometric measurements, four primary facial aspects were evaluated: the facial length-to-width ratio, the proportional relationship between the forehead, nose, and chin, the eye–mouth–chin vertical proportion, and the alignment of eye width with interocular distance. Each measurement was compared to the Phi value to assess its degree of proportional conformity. The findings indicate that most facial ratios observed in both Jin and V closely approximate or match the Golden Ratio, with V exhibiting the highest precision in vertical proportional measurements, while Jin demonstrates more consistent overall proportional stability across multiple facial components. These results support the use of the Phi Golden Ratio as a mathematical indicator in assessing facial proportion and aesthetic harmony. Nevertheless, the study acknowledges that visual aesthetics remain inherently subjective and are influenced by cultural context and individual perception. Thus, anthropometric analysis based on the Golden Ratio can serve as a complementary tool in understanding human facial aesthetics.  

Mine disposal materials such as tailings, overburden, and waste rocks are critical components in mining operations that require comprehensive understanding of their geotechnical properties to ensure stability and safety of storage facilities. This literature review aims to analyze the role of particle gradation and mineralogical composition in determining shear strength and compressibility of mine disposal materials, with particular focus on nickel mining. A sistematic literature review method was employed by analyzing 30 scientific publications from 2019-2025 obtained from various academic databases. The review findings indicate that particle size distribution (gradation) has significant influence on shear strength and compressibility, where materials with coarser gradation and higher coefficient of uniformity (Cu) exhibit greater shear strength and lower compressibility. Mineralogy, particularly clay mineral content, increases cohesion and microporosity but also increases compressibility under loose conditions. Studies on nickel mine waste demonstrate that ferronickel slag possesses favorable drainage characteristics suitable for rockfill material, while tailings require strict gradation control. In conclusion, comprehensive characterization integrating gradation parameters (Cu, Cc, D50) with mineralogical analysis (XRD, XRF) is essential for predicting mechanical behavior of mine disposal materials and designing safe storage facilities.

This study aims to analyze and understand the development of the financial sector in Indonesia through a literature review covering four main aspects: banking, financial digitalization, fiscal reforms, and financial literacy, as well as their impact on economic growth and public welfare. This literature review summarizes various findings and theories regarding the dynamics of the financial sector in efforts to maintain economic stability in Indonesia. The research findings indicate that the development of the banking sector plays a significant role in driving economic growth, particularly in expanding access to financing and improving the ease of fund distribution. In addition, digitalization strengthens the growth of financial technology by expanding access to financial services. Ongoing development of technology policies aims to address challenges in financial digitalization to make it more effective and secure. Furthermore, well-targeted fiscal reforms aim to improve productivity and economic stability. Meanwhile, the public also needs to cultivate wiser financial behavior to maximize the utilization of financial services as part of enhancing financial literacy. In conclusion, the positive impact of the advancement of the financial sector benefits economic growth and public welfare in Indonesia. This study emphasizes the importance of fostering collaboration between government policies, digital innovation, strengthening financial institutions, and helping the public enhance their capabilities to create a more inclusive and sustainable financial ecosystem.

The administration of population services in Rumbai Timur District continues to face challenges following the regional expansion that required residents to update their civil documents. The SIPENDUDUK website was introduced to simplify digital submissions; however, its implementation has not been fully effective. A portion of the community, particularly older adults and individuals with low digital literacy, still struggles to navigate service flows and upload required documents. This study aims to evaluate the service quality of the SIPENDUDUK website in improving the effectiveness of civil administration services. A descriptive qualitative method was employed through observations and interviews with officers and service users. The findings indicate that although the website has facilitated administrative processes, its accessibility has not yet reached all user groups due to varying technological capabilities. The system shows adequate efficiency and stability, although several technical issues remain. Service security is maintained through controlled access and a simplified interface. User support is provided through direct assistance from the local office, including manual guidance and mobile outreach services. These results highlight that the success of digital services depends not only on system quality but also on user readiness. This evaluation is expected to contribute to improving the effectiveness, inclusiveness, and overall quality of population administration services in Rumbai Timur District.

This study aims to examine the development of Islam during the Umayyad Dynasty as one of the important periods in the history of Islamic civilization. The main focus of the discussion is directed at the process of the emergence of Muawiyah ibn Abi Sufyan as the first caliph of the Umayyad Dynasty and his efforts to establish dynastic power after the Islamic Civil War (Fitnah Kubra). The Umayyad Dynasty played a strategic role in the transformation of the Islamic government system from an elective caliphate to a hereditary monarchy, while simultaneously expanding the territory of Islam to various regions. This study uses a literature review method with a historiographic approach, namely examining and analyzing relevant classical and modern historical sources. The results show that during the Umayyad Dynasty, Islam experienced significant developments in the aspects of politics, government administration, military, and culture. However, internal political dynamics, conflicts over the legitimacy of power, and dissatisfaction of certain groups also contributed to weakening the stability of the dynasty. This study also describes the process of decline of the Umayyad Dynasty until its eventual collapse and replacement by the Abbasid Dynasty.

The banking sector is a strategic pillar that supports national economic stability and relies heavily on public trust. To maintain this legitimacy, banks are required to implement Corporate Social Responsibility (CSR), which is not only a moral obligation but also a legal duty as regulated in several laws such as Law No. 40 of 2007 on Limited Liability Companies and Law No. 21 of 2011 on the Financial Services Authority (OJK). This study aims to analyze the responsibility of OJK in managing Corporate Social Responsibility (CSR) funds based on the principles of Good Governance and to examine the role of banking institutions in maintaining public trust through transparent and accountable Corporate Social Responsibility (CSR) practices. This research employs a normative juridical approach by reviewing relevant legislation, literature, and regulatory documents. The results show that OJK holds normative, institutional, and legal responsibilities in supervising Corporate Social Responsibility (CSR) implementation to ensure compliance with the principles of transparency, accountability, independence, responsibility, and fairness. Meanwhile, banking institutions play a crucial role in ensuring that Corporate Social Responsibility (CSR) becomes an integral part of their sustainability strategy rather than a mere administrative formality. The application of Good Corporate Governance (GCG) has a positive impact on increasing public trust, as transparency and accountability in Corporate Social Responsibility (CSR) management strengthen the social legitimacy of banking institutions. Therefore, synergy between OJK and the banking sector in enhancing Corporate Social Responsibility (CSR) governance is the key to achieving an ethical and sustainable financial system.

The National Food Assistance Program (Non-Cash Food Assistance/BPNT) is one of the social policy instruments designed by the Indonesian government to enhance food security and improve the welfare of poor households. This research aims to analyze the effectiveness of this program by assessing its accessibility, targeting accuracy, commodity quality, and its impact on household expenditure and stability of food consumption.The research methodology uses a descriptive-analytical approach, combining secondary data from official government reports and academic literature with primary data in the form of a limited survey of beneficiaries.The results of the analysis indicate that the program is quite effective in increasing poor households' access to staple foods, primarily through the more transparent and flexible non-cash distribution mechanism. However, several constraints are still found, including inaccurate targeting of beneficiaries, variation in food quality at the e-warung level, and the limited digital literacy of some households.Nevertheless, the program is proven to be able to reduce the burden of food expenditure and improve consumption quality, thereby contributing positively to the welfare of poor households. This research suggests improving the validation of beneficiary data, supervising distribution, and expanding food and digital education for the community to further optimize the program's effectiveness.