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Global economic uncertainty and exchange rate fluctuations pose significant challenges to monetary stability in Indonesia, particularly in maintaining a controlled inflation rate. This study aims to analyze the transmission mechanism of the rupiah exchange rate to the inflation rate in Indonesia from 2015 to 2024. The method used in this study is library research by collecting, reviewing, and synthesizing data from various scientific literature, official central bank reports, and related journal articles published over the past decade. The research findings indicate that rupiah depreciation has a significant influence on rising inflation through the imported inflation channel, where currency depreciation increases the cost of raw materials for industries dependent on foreign markets. Furthermore, the findings reveal that the effectiveness of this transmission is influenced by public expectations and monetary policy taken by Bank Indonesia through adjustments to the benchmark interest rate. The implications of this study emphasize the importance of synergy between a stable exchange rate policy and controlling the supply of domestic goods to minimize the impact of external shocks on public purchasing power. The government and monetary authorities are advised to continue strengthening foreign exchange reserves and encouraging the use of local currencies in international transactions to reduce dependence on the United States dollar and maintain national price stability.

The adoption of holocratic leadership systems has become a crucial strategy for startups navigating market volatility, which demands organizational agility through the elimination of traditional hierarchies. This study aims to analyze the transformation of the Human Resource (HR) department's role within holocratic systems and its impact on HR efficiency in startup environments. Employing an integrative literature review approach, this research synthesizes various findings from reputable global and national journals published within the last five years to map the shifts in managerial functions. The results indicate that in structures devoid of conventional managers, the HR role transforms from a controlling authority into a system architect and governance facilitator that maintains compliance with the organizational constitution. HR efficiency is achieved through clear authority distribution within dynamic roles, transparent peer-to-peer feedback mechanisms, and the removal of slow bureaucratic coordination costs. The contribution of this research lies in the formulation of the "HR-as-a-Platform" conceptual model, which shifts the focus of management practices from personal supervision to systemic infrastructure optimization. The practical implications provide a guide for startup founders to re-engineer HR functions to support individual autonomy without sacrificing operational stability. This study confirms that the success of non-hierarchical organizations relies heavily on the integration of radical autonomy and robust digital protocols facilitated by the newly defined HR function.

The proliferation of online gambling promotional comments on Indonesian social media has become a serious issue requiring fast and accurate automated handling. This study aims to implement a Hybrid Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) method to classify online gambling comments and compare its performance with standalone RNN and LSTM models. The research utilized a dataset of 10,230 comments subjected to comprehensive preprocessing stages, including the normalization of non-standard language using a slang dictionary. Testing was conducted across three data-splitting scenarios: 90:10, 80:20, and 70:30. Experimental results demonstrate that the standalone LSTM model achieved the highest average accuracy of 97.45%. However, the Hybrid RNN–LSTM model showed significant superiority in terms of performance stability, yielding the lowest standard deviation (0.0027) and the smallest Coefficient of Variation (0.28%) across all scenarios. These findings indicate that while the LSTM architecture is highly effective at capturing short-text context, the Hybrid approach provides better robustness against fluctuations in data proportions, making it highly relevant for implementation as an automated detection system on social media.

Introduction: Appendectomy is a relatively safe surgical procedure. One important aspect of this stage is maintaining the patient's mental stability, especially before administering anesthesia. However, anesthesia often causes anxiety in patients. Mindfulness-based cognitive therapy has been shown to significantly improve quality of life by minimizing anxiety. Objective: This study analyzed the effect of Mindfulness-based cognitive therapy (MCT) in reducing pre-anesthetic anxiety in appendectomy patients. Methods: This study used a case study design with a nursing care process approach. The subjects in this case study were two patients who met the inclusion criteria. The instrument used was an observation sheet according to the Indonesian Society for Anesthesia and Clinical Trials (SLKI). Results: The results of the case study showed that both patients experienced pre-anesthetic anxiety. After receiving Mindfulness-based cognitive therapy, pre-anesthetic anxiety decreased. Conclusion: This case study demonstrated that pre-anesthetic anxiety decreased after Mindfulness-based cognitive therapy.

This study aims to analyze the impact of the geopolitical conflict between the United States, Israel, and Iran in 2026 on global macroeconomic stability and climate change dynamics. Using a qualitative approach through a Systematic Literature Review (SLR), this research synthesizes findings from various international journal articles, reports, and academic sources. The results indicate that the conflict has significantly disrupted global energy markets, leading to a sharp increase in oil and gas prices. This energy shock has triggered global inflationary pressures, reduced purchasing power, and increased economic uncertainty across both developed and developing countries. Furthermore, monetary tightening policies implemented to control inflation have created trade-offs with economic growth, increasing the risk of global recession. On the fiscal side, government interventions such as energy subsidies have helped mitigate short-term impacts but have raised concerns about long-term fiscal sustainability. In addition, the energy crisis has slowed the transition toward renewable energy, thereby exacerbating climate change risks. The study highlights the interconnectedness between geopolitical conflict, macroeconomic instability, and environmental sustainability. Therefore, coordinated global policies and integrated economic strategies are essential to address these multidimensional challenges effectively.

Software Defect Prediction (SDP) aims to identify defective modules early in the software development lifecycle to improve software quality and reduce maintenance costs. However, SDP datasets commonly suffer from high dimensionality, feature redundancy, and class imbalance, which can degrade model performance and stability. This study proposes a hybrid feature selection framework to address these challenges and enhance prediction performance. The proposed approach integrates Combined Correlation and Mutual Information (CONMI), which combines the Pearson Correlation Coefficient (PCC) and Mutual Information (MI) to capture both linear and nonlinear feature relevance. The selected features are further refined through Top-K selection, correlation-based filtering to reduce multicollinearity, and Backward Elimination (BE) to obtain an optimal feature subset. To address class imbalance, SMOTE-Tomek is applied by combining over-sampling and data cleaning techniques. Experiments are conducted on twelve NASA MDP datasets using Logistic Regression (LR) and Naïve Bayes (NB) classifiers. The results show that the proposed framework consistently achieves the best performance, with Logistic Regression combined with SMOTE-Tomek obtaining the highest average AUC of 0.7923 ± 0.0714, while NB achieves 0.7554 ± 0.0580. Statistical analysis using a paired t-test indicates that the proposed method significantly outperforms MI+SMOTE-Tomek and BE+SMOTE-Tomek for Logistic Regression, whereas no significant differences are observed for NB. In addition to improving overall classification performance (AUC), the proposed approach also enhances minority class detection, as reflected in improved Recall and F1-score. Overall, the proposed hybrid framework provides an effective and reliable solution for software defect prediction, particularly for high-dimensional and imbalanced datasets.

Slope stability studies are crucial in determining the sustainability and operational safety of mining activities. This research was conducted to analyze the correlation between rock porosity and Unconfined Compressive Strength (UCS) results, as well as the corrleations between Point Load Index (PLI) and Unconfoned Compressive Strength (UCS) results. These two correlations form the primary components of the study. Three types of rock were tested claystone, coal, and siltstone. The results of the study are as follows for the correlation between porosity and Unconfoned Compressive Strength (UCS), claystone yielded a coefficient of determination (R2) of 0.32 is classified as weak correlation, with the linear regression equation Y=-0.11X+6.98. Coal yielded an (R2) of 0.56 is classified a moderate correlation, with the equation Y=-1.98X+103.77. Siltstone yielded an (R2) of 0.86 is classified a strong correlation, with the equation Y=-0.38X+20.02. Regarding the correlation between Point Load Index (PLI) and Unconfoned Compressive Strength (UCS), claystone achieved an (R2) of 0.12 is classified a very weak correlation, with the linear regression equation Y=8.91X. Coal achieved an (R2) of -0.27 is classified a very weak correlation, with the equation Y=27.87X. Lastly, siltstone achieved an (R2) of -0.45 is classified a very weak correlation, with the linear regression equation Y=24,90X.

Electroencephalography (EEG) signals are highly susceptible to noise and artifacts, which can degrade analysis accuracy, particularly in Autism Spectrum Disorder (ASD) studies. Therefore, effective preprocessing is required to improve signal quality prior to further analysis. This study proposes an integrated EEG preprocessing pipeline that combines a Finite Impulse Response (FIR) band-pass filter (0.5–70 Hz) with notch filtering and detrending, followed by temporal denoising using the Stationary Wavelet Transform (SWT) with the Daubechies 4 mother wavelet and spatial filtering based on SPHARA. This dual-domain approach is designed to address both temporal and spatial noise in multichannel EEG signals. Experimental results demonstrate that the proposed FIR combined with SWT and SPHARA pipeline consistently outperforms single-domain preprocessing methods, achieving a maximum Signal-to-Noise Ratio (SNR) of 31.93 dB. The proposed method also produces the lowest Mean Absolute Error (MAE) (16.81 µV) and Standard Deviation (SD) (0.75 µV), indicating high signal stability with minimal amplitude distortion. Root Mean Square Error (RMSE) values remain stable within the range of 29.5–592.3 µV, with a minimum RMSE of 29.5 µV, demonstrating effective noise suppression while preserving signal energy. These results confirm that integrating temporal and spatial preprocessing significantly improves EEG signal quality and supports more reliable EEG analysis for ASD-related studies.

Traditional artworks, amidst the tide of modernization and digitalization, face the challenge of being stigmatized as static relics of the past. However, in the midst of increasingly modern civilization, traditional artworks are perceived to possess philosophical values ​​for the younger generation. The meanings contained in traditional songs possess a social perspective lacking in most traditional songs composed in the modern era. The purpose of this article is to examine the lyrics of Ki Nartosabdo's song "Lumbung Desa" philosophically and sociologically, applying Roland Barthes's semiotic theory. The research method used is a qualitative descriptive approach to analyze semiotics, comprising the significance of denotation, connotation, and myth.The analysis shows that the denotation in the lyrics of "Lumbung Desa" depicts agricultural activities occurring in rural communities. Connotatively, the lyrics interpret the values ​​of human spirituality towards God, integrity in work, and food self-sufficiency. From a mythical perspective, the lyrics symbolize the values ​​of harmony in social life and mutual cooperation as a solid foundation for building national stability and progress. Further analysis of the song lyrics' relevance in the modern era shows that the meaning of "Lumbung" has transformed into financial and digital independence, while the value of "Rukun" points to the application of ethics in the use of digital media in the modern era. This article concludes that preserving traditional songs is a strategy to revive culture as a manifestation of material progress that impacts the well-being of modern society.

The global rise in life expectancy toward the 100-year threshold has accelerated the emergence of the silver economy, significantly affecting organizational leadership structures, particularly in terms of succession stagnation risks and potential loss of institutional knowledge. This study aims to develop a comprehensive longevity leadership strategy to manage senior leaders and optimize leadership succession in the context of demographic transition. The research employs an integrative literature review with thematic analysis of reputable international and national journal articles published within the last five years. The findings reveal that effective longevity leadership strategies emphasize repositioning senior leaders from operational roles to strategic mentors through knowledge-sharing mechanisms and phased retirement approaches. Furthermore, successful succession in an aging workforce depends on role flexibility and the establishment of new psychological contracts that recognize and value the wisdom of senior leaders. This study contributes theoretically by proposing a dynamic succession model that bridges intergenerational gaps. Practically, it offers guidance for human resource practitioners in designing age-inclusive retention strategies and leadership transition policies.Overall, the proposed strategy supports organizational stability while fostering sustainable innovation through the optimal utilization of senior human capital.

The relocation of the Social Welfare Shelter Environment (Lingkungan Pondok Sosial/Liponsos) in Sidoarjo represents a strategic effort to address social and spatial issues characterized by overcapacity, limited facilities, and an inadequate living environment that does not fully support the physical and psychological needs of people with social welfare problems (PMKS). These conditions indicate that the challenges faced by Liponsos are not merely social in nature but are also closely related to the quality of the built environment, which significantly influences the rehabilitation and recovery process of its residents. Therefore, a human-centered design approach is required in planning social service facilities. This study aims to analyze the application of humanistic architecture principles in the planned relocation of the Sidoarjo Liponsos as a foundation for creating a more livable, dignified, and user-oriented environment. The research employs a literature review method by collecting data from relevant books, journals, regulations, and previous studies. The analysis is conducted using descriptive and content analysis to examine the relationship between humanistic architectural principles and the needs of residents within social welfare facilities. The results indicate that the humanistic architectural approach is highly relevant to the relocation of Liponsos, particularly through spatial zoning based on levels of privacy and security, the provision of green open spaces and communal areas as media for social interaction and therapeutic activities, and the design of self-development spaces that support rehabilitation and empowerment. The implementation of these principles is expected to create a built environment that not only fulfills basic functional requirements but also supports psychological recovery, social stability, and sustainable improvement in the quality of life of PMKS residents.  

Multicollinearity is one of the common issues in multiple linear regression that can lead to instability in the estimation of regression coefficients. This study aims to examine the impact of multicollinearity on regression models and to evaluate the use of Ridge Regression as an alternative estimation method. The study employs simulated data consisting of 1,000 observations, including one dependent variable and four independent variables designed to exhibit high correlation. The analysis begins with model estimation using the Ordinary Least Squares (OLS) method, followed by multicollinearity testing using the Variance Inflation Factor (VIF). The OLS results indicate that most independent variables significantly influence the dependent variable, with a coefficient of determination (R²) of 0.9863. However, the high VIF values reveal the presence of strong multicollinearity in the model. To address this issue, Ridge Regression is applied, with the optimal penalty parameter determined through cross-validation, yielding a lambda value of 4.201589. The results show that the regression coefficients in the Ridge model undergo shrinkage, resulting in greater stability compared to the OLS estimates. Model evaluation indicates that the Mean Squared Error (MSE) for the OLS model is 24.77, whereas the Ridge model produces an MSE of 29.72. Although the Ridge model exhibits a slightly higher MSE, it effectively mitigates the impact of multicollinearity and provides more stable parameter estimates.

The performance of asphalt mixtures is strongly influenced by the composition of their constituent materials, particularly aggregate size and mixing temperature during production. In many tropical and subtropical regions, asphalt pavements frequently experience rutting, reduced stability, and changes in viscoelastic properties due to high environmental temperatures and heavy traffic loads. These conditions can significantly affect pavement durability, making it essential to produce asphalt mixtures that meet established technical standards. This study aims to analyze the effect of variations in aggregate size distribution and mixing temperature on the Marshall characteristics of Asphalt Concrete Wearing Course (AC-WC) mixtures. The research employs the Marshall test method to evaluate the load-bearing capacity and stability of hot asphalt mixtures and to assess their compliance with ASTM/SNI standards. The results indicate that mixtures with standard aggregate gradation achieve stability values of 985 kg at 120°C, 1055 kg at 140°C, and 1107 kg at 160°C. As mixing temperature increases, flow values decrease, while the Marshall Quotient (MQ) increases, indicating improved stiffness. Higher temperatures also enhance compaction, reducing VIM and VMA while increasing VFA. Conversely, non-standard aggregate gradations result in several parameters failing to meet ASTM/SNI requirements, confirming that standard gradation produces superior asphalt performance.

This study aims to determine the effect of adding Buton Asphalt on the mechanical properties of Asphalt Concrete – Wearing Course (AC-WC) mixtures based on Marshall test parameters. The variations of asphalt content used were 0%, 2%, and 5%. Each variation was tested using the Marshall method to obtain the values of Stability, Flow, Void in Mix (VIM), Void in Mineral Aggregate (VMA), Void Filled with Asphalt (VFA), and Marshall Quotient (MQ). The results show that the addition of Buton Asphalt affects the improvement of the mixture characteristics. The highest stability value was obtained at 5% Buton Asphalt content, which was 1,074 kg, while the lowest flow value was also found at 5%, which was 2.48 mm, indicating that the mixture becomes stiffer and more stable. The VIM value decreased from 4.7% at 0% content to 4.2% at 5% content, indicating that the air voids in the mixture became smaller. The VMA value also decreased from 16.2% at 0%, 15.8% at 2%, and 15.1% at 5%, while the VFA value increased from 71% to 72.18%. The highest Marshall Quotient (MQ) value was obtained at 5% Buton Asphalt content, which was 433 kg/mm, indicating that the mixture has better stiffness and resistance to deformation. Based on these results, it can be concluded that the addition of 5% Buton Asphalt provides the best mixture characteristics for the AC-WC layer.

High dependence on the United States Dollar (USD) in international transactions has long been a challenge for economic stability in the Southeast Asian region, especially amidst global exchange rate fluctuations and geopolitical tensions. This study aims to analyze the effectiveness of Local Currency Settlement (LCS) cooperation in supporting intra-ASEAN trade stability. The main focus of this study is how local currency mechanisms can mitigate exchange rate risks and strengthen regional economic integration as part of a de-dollarization strategy. The research method used is descriptive qualitative with a literature review approach, relying on secondary data from central bank reports, ASEAN policy documents, and relevant academic literature. The results show that the implementation of the LCS framework, particularly in countries such as Indonesia, Malaysia, and Thailand, has provided more efficient transaction alternatives by reducing double conversion costs. However, its effectiveness still faces challenges such as low awareness among business actors, limited local currency liquidity compared to the USD, and the need for broader cross-border digital payment system integration. These findings imply the need for strengthened synergy between central banks in the ASEAN region and increased literacy for the private sector so that the economic stability benefits of LCS can be optimally achieved. This strategy not only strengthens monetary sovereignty but also encourages a more resilient ASEAN economic integration against external shocks.

This study aims to analyze how human development governance is implemented by the Government of South Sulawesi Province in reducing regional disparities, identify institutional factors influencing implementation quality, and examine its implications for human development outcomes. The study employs a qualitative approach using a case study design. Data were collected through interviews with key informants from the Regional Development Planning Agency (Bappeda) and relevant Regional Apparatus Organizations, analysis of regional planning documents such as RPJMD and RKPD, and statistical data from the Central Bureau of Statistics related to the Human Development Index (HDI), poverty, and employment. Data were analyzed thematically using triangulation to ensure validity of findings. The results show that the Human Development Index has been positioned as a key performance indicator in regional development planning documents and prioritized in education and health sector policies. Although overall HDI achievement in South Sulawesi is categorized as high, significant disparities remain across regencies and municipalities, particularly in education and standard of living. A poverty rate of 8.06 percent and the increasing trend of the Gini Ratio indicate that distribution of welfare has not been fully equitable. Furthermore, the dominance of the informal sector in the employment structure reflects challenges related to job quality, income stability, and limited social protection coverage. These findings suggest that the main challenge of human development in South Sulawesi lies not only in improving aggregate indicators but also in strengthening bureaucratic capacity, cross-sectoral coordination, and policy implementation consistency to ensure more inclusive and equitable development across regions.

Object detection plays a crucial role in intelligent transportation systems, particularly for outdoor traffic monitoring applications that require accurate and real-time performance under limited computational resources. Recent developments in YOLO-based architectures have introduced multiple model variants; however, their practical performance under constrained training conditions remains insufficiently explored. This study presents a comparative evaluation of YOLOv5, YOLOv7, and YOLOv8 for outdoor traffic object detection using a real-world dataset and identical experimental settings. The main objective of this research is to analyze the robustness and detection quality of different YOLO variants when trained with a limited number of epochs, reflecting practical deployment scenarios. All models were trained and evaluated using the same dataset, preprocessing pipeline, and hardware configuration to ensure a fair comparison. Performance evaluation was conducted using multiple metrics, including precision, recall, mAP@50, Precision–Recall curves, area under the curve (AUC), and peak F1-score. Experimental results indicate that YOLOv5 outperformed YOLOv7 and YOLOv8 in terms of overall detection stability and robustness. The merged Precision–Recall analysis shows that YOLOv5 achieved a higher effective AUC and superior mAP@50, reflecting better global detection performance. In addition, YOLOv5 exhibited a higher peak F1-score, indicating a more balanced trade-off between precision and recall. In contrast, YOLOv7 and YOLOv8 showed performance degradation under limited training conditions despite their more advanced architectures. These findings suggest that YOLOv5 remains a reliable and efficient solution for outdoor traffic object detection, particularly in resource-constrained environments. The study highlights the importance of comprehensive evaluation metrics and practical experimental settings when selecting object detection models for real-world applications.

Object detection plays a crucial role in intelligent transportation systems, particularly for outdoor traffic monitoring applications that require accurate and real-time performance under limited computational resources. Recent developments in YOLO-based architectures have introduced multiple model variants; however, their practical performance under constrained training conditions remains insufficiently explored. This study presents a comparative evaluation of YOLOv5, YOLOv7, and YOLOv8 for outdoor traffic object detection using a real-world dataset and identical experimental settings. The main objective of this research is to analyze the robustness and detection quality of different YOLO variants when trained with a limited number of epochs, reflecting practical deployment scenarios. All models were trained and evaluated using the same dataset, preprocessing pipeline, and hardware configuration to ensure a fair comparison. Performance evaluation was conducted using multiple metrics, including precision, recall, mAP@50, Precision–Recall curves, area under the curve (AUC), and peak F1-score. Experimental results indicate that YOLOv5 outperformed YOLOv7 and YOLOv8 in terms of overall detection stability and robustness. The merged Precision–Recall analysis shows that YOLOv5 achieved a higher effective AUC and superior mAP@50, reflecting better global detection performance. In addition, YOLOv5 exhibited a higher peak F1-score, indicating a more balanced trade-off between precision and recall. In contrast, YOLOv7 and YOLOv8 showed performance degradation under limited training conditions despite their more advanced architectures. These findings suggest that YOLOv5 remains a reliable and efficient solution for outdoor traffic object detection, particularly in resource-constrained environments. The study highlights the importance of comprehensive evaluation metrics and practical experimental settings when selecting object detection models for real-world applications.

Gillnet fisheries play an important role in supporting the local economy and the welfare of coastal communities. However, fishermen still face challenges related to unequal access to livelihood assets and high vulnerability to external factors such as seasonal changes, catch fluctuations, market pressures, and environmental conditions. This study aimed to analyze the level of vulnerability, identify livelihood assets, and formulate strategies for sustainable livelihood development among gillnet fishing communities. The study was conducted in Margasari and Muara Gading Mas Villages in September 2025 using a mixed-method approach, combining questionnaires, in-depth interviews, and field observations. Samples were selected through purposive sampling, and data were analyzed using descriptive quantitative and qualitative methods supported by a Likert scale. The results showed that fishermen’s vulnerability is relatively high (54.5%), affecting income stability. Social (81.4%) and natural (78.7%) assets are relatively strong, while human (38.6%), physic (48.3%), and financial (43.20%) assets remain relatively low. Limited education, lack of training, and restricted access to financial resources are the main constraints, leading to low adaptive capacity and limited livelihood diversification. Recommended strategies include improving human resource capacity, strengthening access to financial capital, and developing livelihood diversification based on local potential. These efforts are expected to enhance household economic resilience and support sustainable livelihoods among gillnet fishing communities.

Drug misuse continues to be a complicated social and legal issue that has an impact on societal stability, public health, and personal wellbeing. Many criminal justice systems have moved away from punitive tactics in favor of rehabilitation-focused ones in recent years, especially for drug users who are frequently viewed as sufferers of addiction rather than serious criminals. The National Narcotics Agency (BNN) in Indonesia is implementing rehabilitation programs and restorative justice processes as a result of this change. This study investigates the efficacy of restorative justice in drug rehabilitation at Jambi Province's National Narcotics Agency and evaluates its applicability from the standpoint of Islamic law, specifically the framework of maqāṣid al-sharīʿah. The study uses a case study design and a qualitative methodology. Participant observation, document analysis, and in-depth interviews with BNN officials, rehabilitation counselors, medical staff, and ex-drug users were used to gather data. The results show that an integrated evaluation system that assesses drug users' physical, psychological, and social states in order to determine their eligibility for recovery is used to institutionally apply restorative justice principles. Combining medical care, psychological counseling, and social reintegration programs, the rehabilitation programs greatly aid in participants' recuperation, enhance psychological stability, and fortify familial ties. Additionally, by promoting individual responsibility, family support, and community involvement, restorative justice-based rehabilitation lowers the risk of recidivism. From the standpoint of Islamic legal philosophy, these actions are consistent with the goals of maqāṣid al-sharīʿah, specifically the defense of human dignity, life (ḥifḍ al-nafs), and intellect (ḥifḍ al-ʿaql). According to the study's findings, restorative justice-based rehabilitation is a compassionate and successful method of treating drug dependency while encouraging social reintegration and long-term recovery.