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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.

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.

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.

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.

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.

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.

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.

The increasing sophistication of malware has rendered traditional signature-based detection methods insufficient, necessitating behavior-driven and adaptive analytical frameworks. This study presents a sequential deep learning framework that models system-level API call sequences as structured linguistic representations for behavioral malware detection. Unlike conventional comparative studies, this work systematically evaluates recurrent and attention-based architectures under controlled experimental conditions, with a particular focus on generalization performance and overfitting mitigation. Two neural architectures, a Long Short-Term Memory (LSTM) network and a Transformer-based attention model, are trained on publicly available API call sequence data for binary classification of malicious and benign executables. Beyond standard accuracy metrics, the study further examines model stability, convergence behavior, and the impact of long-range dependency modeling on detection robustness. Experimental results demonstrate that the Transformer architecture achieves superior performance, attaining 95.54% classification accuracy and consistent improvements in precision, recall, and F1-score, indicating a stronger ability to capture complex behavioral dependencies. These findings highlight the effectiveness of attention mechanisms in behavioral malware modeling and provide empirical evidence that NLP-inspired architectures offer a robust and scalable approach for real-world cybersecurity applications.

This study aims to analyze the impact of inflation on the profitability of Micro, Small, and Medium Enterprises (MSMEs) operating in traditional markets. Inflation influences key business aspects, including rising production costs, declining consumer purchasing power, and instability in input prices, all of which can disrupt business performance. The research employed a quantitative approach using survey data collected from MSME actors to assess these effects. The findings reveal that inflation has a significant negative impact on MSME profitability, particularly through the reduction of profit margins. This occurs as businesses face higher raw material costs while simultaneously experiencing a decline in sales volume due to weakened consumer demand. As a result, many MSMEs struggle to maintain financial stability and sustain their operations under inflationary pressure. These findings highlight the need for adaptive strategies among MSMEs, such as cost efficiency and pricing adjustments. Additionally, the study offers important policy implications for the government to support MSMEs through targeted interventions, including price stabilization measures and financial assistance programs, in order to maintain business resilience and economic sustainability.

This community service activity aims to improve the effectiveness of clean water distribution management through the implementation of a Water Level Control (WLC) system as an automatic control for water pumps and three-phase motors at a regional drinking water supply company in Lamongan. The methods employed include field observation, problem identification, system design and implementation, as well as periodic evaluation of system performance. The activity focuses on the water treatment unit by conducting control and monitoring of operational parameters such as frequency (Hz), pump pressure, and flow stability to ensure optimal water distribution according to consumer needs. The results indicate that the implementation of the WLC system enhances operational efficiency, minimizes human error, and supports more structured and sustainable equipment maintenance. In addition, this activity contributes to improving the knowledge and technical skills of personnel in operating, monitoring, and maintaining automated control systems. In managing automation-based technology in a professional and independent manner, human resource capacity is strengthened, while the quality of clean water services is also improved through this community service program.

Ankle sprain is one of the most common musculoskeletal injuries in sports activities and often results in pain, limited range of motion, muscle weakness, and decreased functional ability. Appropriate physiotherapy management is essential to accelerate recovery and restore ankle function. This study aims to analyze the effectiveness of combining electrotherapy modalities and exercise therapy in the rehabilitation of bilateral grade II ankle sprain caused by sports injury. The research used a case study approach with physiotherapy interventions conducted through several treatment sessions. The interventions consisted of electrotherapy modalities combined with therapeutic exercises designed to reduce pain, improve joint mobility, and increase muscle strength and functional stability of the ankle. Clinical evaluation was carried out through observation of pain levels, range of motion, and functional ability during the therapy period. The findings indicated a gradual improvement in the patient’s condition, including decreased pain, increased ankle range of motion, improved muscle strength, and better functional performance during daily activities. These results suggest that the integration of electrotherapy and exercise therapy can provide a comprehensive rehabilitation approach for ankle sprain injuries. The combined intervention not only supports pain reduction but also enhances joint stability and functional recovery, enabling patients to return safely to their physical activities and reducing the risk of recurrent injury.

The era of digital disruption has given birth to the phenomenon of post-truth and massive disinformation, which triggers sharp political polarization on social media, thereby threatening democratic stability and social integration in Indonesia. This study aims to analyze the effectiveness of implementing Islamic political ethics (Siyasah Syar’iyah) and the transformation of Tabayyun values as instruments for mitigating information disorder in the digital public sphere. The rationale for this research is the urgent need for a robust ethical framework to complement technical-secular digital literacy, which has been deemed insufficient in curbing emotional sentiments and religious hoaxes. The research method employed is qualitative with a library research approach, integrating various contemporary scientific references from the last five years (2020-2025). The research findings indicate that Islamic political ethics offers a holistic solution through the reconstruction of transcendental values in cyberspace activities. The main finding (novelty) of this research is the "Cyber Activism Based on Amar Ma’ruf Nahi Munkar" model, which synergizes the role of Islamic Religious Education (PAI), revelation-based media literacy, and moderate-characterized digital leadership. This model transforms netizens from mere information consumers into agents of "Digital Piety" who actively produce counter-narratives to create a civilized and integrated digital ecosystem.

Spinal anesthesia is one of the regional anesthesia methods that is often chosen in various surgical procedures, especially in the area of the lower extremities, pelvis, and lower abdomen. The most common complication encountered in this technique is the occurrence of hypotension, which can affect tissue perfusion and hemodynamic stability of the patient. This study aims to examine the incidence of hypotension in spinal anesthesia, including mechanisms, risk factors, and management. The method used in this study is literature review with a scientific source search process conducted through various databases PUBMED, Sciencedirect, Researchgate, and Google Scholar with a publication year range of 2016 to 2026. The results showed that hypotension arises as a consequence of an obstacle in the sympathetic nervous system, which provokes dilation of blood vessels as well as reduced venous return. Risk factors include age, body mass index, patient position, block height, duration of surgery, fluid status, Injection Point, and type of anesthetic drug used. Management is carried out by administering fluids, vasopressors, as well as strict hemodynamic monitoring. A good understanding of the risk factors and mechanisms of hypotension is expected to improve patient safety during spinal anesthesia..