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This study investigates the performance of Internet of Things (IoT)-based monitoring systems using a mobile hotspot and IoT sensors for temperature and humidity data transmission. The research is based on the IoT concept, which enables electronic devices to communicate and exchange data through internet networks without direct human intervention. System performance was evaluated using standard Quality of Service (QoS) parameters, including throughput, packet loss, delay, and jitter. The experimental setup utilized a NodeMCU ESP32 microcontroller and a DHT22 sensor, with measurements conducted at various transmission distances through wireless communication media. The objective was to determine the reliability of hotspot connectivity and sensor communication in supporting IoT applications. The results indicate that the optimal performance was achieved at a distance of 20 meters using a 40-lambda variation. Furthermore, the communication signal between the ESP32 device and the mobile hotspot remained detectable up to a maximum distance of 32 meters. These findings demonstrate the effectiveness of the proposed IoT system for environmental monitoring applications within specific transmission ranges.

Travertine is a non-marine carbonate rock precipitated from calcium- and bicarbonate-rich waters, commonly associated with hot springs, streams, and lakes in tectonically active regions. This study presents a systematic literature review of travertine deposit characteristics, encompassing mineralogical composition, geochemical signatures, petrographic textures, morphological types, isotopic properties, biotic–abiotic controls on formation, early diagenesis, and petrophysical implications. The synthesis indicates that travertine is predominantly composed of calcite, with aragonite forming under conditions of elevated temperature, high Mg/Ca ratios, and rapid CO₂ degassing. Travertines display exceptional textural diversity, ranging from abiotic crystalline crusts to microbially mediated fabrics such as shrub, peloidal, and stromatolitic structures. Major morphologies include fissure ridges, mounds, terraces, cascades, and slopes, strongly governed by tectonic setting, topography, hydrology, and substrate geology. Stable isotopes (δ¹³C and δ¹⁸O), strontium isotopes, trace elements, and REE patterns effectively constrain fluid provenance and distinguish thermogene from meteogene travertines. Microbial activity plays a crucial role in biomineralization, influencing precipitation rates, crystal morphology, and lamination patterns. Early diagenetic processes occurring during active deposition may significantly modify primary fabrics and geochemical signals. Travertine petrophysical properties exhibit distinctive porosity–velocity relationships, making them valuable analogues for subsurface carbonate reservoirs. This review highlights that travertines record complex interactions among physical, chemical, biological, and geological processes and hold significant value for paleoenvironmental reconstruction and carbonate reservoir studies.

Cervical cancer is one of the most common cancers worldwide and is largely caused by Human Papillomavirus (HPV) infection, particularly types 16 and 18. The development of this cancer is influenced by the E6 and E7 oncoproteins, which disrupt the function of p53 and Rb proteins, as well as the involvement of cellular signaling pathways such as PI3K/AKT/mTOR and EGFR. Limitations of conventional therapies, such as resistance and side effects, drive the need for more targeted and selective therapeutic agents. This study aims to analyze the main molecular targets of cervical cancer and the medicinal chemistry approaches used in its therapeutic development. The method involved a review of 12 open access articles using molecular docking, molecular dynamics, QSAR, and network pharmacology approaches. The findings identified two main target groups: viral targets comprising the HPV E6 and E7 oncoproteins, and cellular targets including the PI3K/AKT/mTOR pathway, EGFR, PARP1, CDK1, and VEGFR. Various medicinal chemistry strategies, from structure-based and ligand-based drug design to multi-target network pharmacology approaches, proved effective in identifying potential inhibitor candidates. Bioactive compounds such as quercetin, gossypol, bixin, and rutin demonstrated stable interactions with these targets. However, further experimental validation is needed to support the development of clinically effective and safe therapies.

Desa Motu is an area that experiences limited internet access due to the unavailability of conventional ISP services and weak cellular signal coverage. This study aims to develop a community-based RT/RW Net network system to provide internet access by utilizing Starlink as the main ISP and distributing connectivity through networking devices such as MikroTik routers, access points, switches, and fiber optic media. Network testing was conducted by measuring download and upload speeds using network testing ap-plications, evaluating connection stability through latency (ping) measurements, and assessing signal coverage at several user locations. The results show that the implemented RT/RW Net network is able to provide a stable internet connection with consistent speeds at different testing times, as well as optimal signal distribution across multiple measurement points. The authentication system using vouchers, PPPoE, hotspot login, and MAC Binding functions properly, and the free educational access feature also operates effectively. These findings indicate that the RT/RW Net–based community network model can serve as an affordable and sustainable solution for expanding internet access in rural areas.

This research explores hedonistic lifestyle patterns as a social reality that grows within peer interactions, both in adolescence and adulthood. The investigation applies a qualitative descriptive literature review by examining national scholarly publications issued from 2016 to 2026. The findings indicate that hedonistic tendencies should not be interpreted merely as individual choices to seek enjoyment. Instead, they emerge from social processes shaped by interaction, peer influence, and shared norms that develop within a group. In this context, peer circles function as influential socialization agents, encouraging conformity so individuals can be accepted and avoid social exclusion. The study also identifies differences in underlying motivations across life stages. Among adolescents, hedonistic behavior is closely linked to identity exploration and self-definition. For adults, however, it is more associated with maintaining self-image, signaling social standing, and displaying markers of achievement. Overall, environmental dynamics, the expansion of consumer-oriented culture, and intensive exposure to social media play significant roles in strengthening this phenomenon in Indonesia.

Intellectual property rights (IPR) are an important legal tool for trademark ownership, protecting business quality, and protecting a company's economic interests. Consumers build trust in trademarks because they signal distinct product quality and reflect a positive and consistent corporate image. Trademark protection is regulated by national law under Trademark Law No. 20 of 2016, which provides legal certainty for trademark owners. This law stipulates that a trademark is only valid if it has distinctive elements, is not imitative, and has been officially registered with an authorized institution. The "first come, first served" principle in Indonesian trademark law can be interpreted as a mechanism that grants rights to the first party to file a valid application. However, the application of this principle in practice often raises complex legal issues, particularly when a trademark has already gained widespread public recognition prior to its formal registration. This study focuses on evaluating this legal protection through a normative legal research method by examining applicable laws, regulations, and court decisions related to trademark disputes in Indonesia. The Geprek Bensu dispute has attracted public attention because it highlights the conflict between legal provisions regarding trademark ownership and public perception. This case demonstrates that the existing legal framework still requires further refinement to balance the interests of trademark registrants with those of parties who have built public reputation through prior commercial use. Therefore, legal reform and consistent law enforcement are essential to ensure fair and comprehensive trademark protection in Indonesia.

This study was conducted to evaluate the impact of financial performance, capital structure, and good corporate governance on entities. The approach used is quantitative with a causal associative method. The research observations utilize secondary data sourced from the financial statements of entities listed on the stock exchange during the 2020–2023 period. The research sample was determined using a purposive sampling technique based on predefined criteria, totaling 160 observations. The analytical method employed is multiple linear regression, preceded by classical assumption tests. The results reveal that financial performance and good corporate governance have a positive and significant effect on the quality of financial statements, while capital structure has a significant negative effect. Simultaneously, the three independent variables are proven to significantly affect the quality of financial statements, with a coefficient of determination of 68%. These findings support agency theory and signaling theory in explaining the financial reporting behavior of entities. The implications of this study indicate that improving financial performance and implementing good corporate governance can enhance the quality of financial statements. Furthermore, optimal management of capital structure is also necessary to reduce the risk of financial statement manipulation.

Population growth and socio-economic activities increase traffic volume, affecting the performance of the Raden Kusno – A. Djaelani – Sujarwo signalized intersection. This study aims to analyze the intersection’s current performance, estimate its condition over the next five years, and formulate alternative treatment strategies. The research data include geometric characteristics, signal timing, vehicle speed, and traffic volume obtained from CCTV recording over a three-day observation period from moning to evening, as well as population and vehicle data for projection. The intersection performance was analyzed using the PKJI 2023 approach and VISSIM simulation. Under current conditions, the intersection operates at LOS E with delays of 45,12 seconds (PKJI 2023) dan 60,56 seconds (VISSIM). In the five-year projection, delays increase to 48,97 seconds with LOS E (PKJI 2023) and 131,29 seconds with LOS F (VISSIM). Modifying the signal from four to three phases with a 70-second cycle improves the current condition to LOS C, with delays of 24,50 seconds (PKJI 2023) and 29,43 seconds (VISSIM). For the five-year projection, adding a continuous left-turn lane results in LOS D with 27,04 seconds (PKJI 2023) and LOS C with 32,01 seconds (VISSIM).

This study analyzes the effect of capital structure and firm growth on firm value with dividend policy as a mediating variable in property and real estate companies listed on the Indonesia Stock Exchange for the period 2019-2024. The volatility of the property sector influenced by global and domestic economic conditions encourages the importance of understanding firm value formation mechanisms. The research method uses a quantitative approach with purposive sampling technique resulting in 66 observations from 11 companies during the research period. Data analysis uses Partial Least Squares-Structural Equation Modeling through SmartPLS application to test relationships between variables. The results show that capital structure and firm growth have no direct significant effect on firm value, but have a significant negative effect on dividend policy. Dividend policy has a significant positive effect on firm value and is able to fully mediate the effect of capital structure and firm growth on firm value with Variance Accounted For values of 151.6% and 90.4% respectively. These findings confirm the importance of dividend policy as a credible signaling mechanism regarding the company's ability to generate sustainable cash flows in creating value for shareholders amid the volatility of Indonesia's property sector.  

The rapid growth of the Internet of Things (IoT) has driven numerous innovations in wireless communications that not only demand technical efficiency but also raise philosophical questions about the nature of scientific knowledge. One such innovation is Physical Layer Network Coding (PLNC), a communication technique that utilizes signal interference as a source of information to enhance system performance. This paper examines the philosophical dimensions of science within PLNC, focusing on three fundamental aspects: ontology, epistemology, and axiology. Ontologically, PLNC represents a new paradigm in wireless communication that reinterprets interference not merely as noise but as an opportunity. Epistemologically, knowledge of PLNC is derived through scientific methods such as mathematical modeling, experimentation, and simulation—yielding intersubjective and verifiable truths. Axiologically, PLNC holds practical value in terms of energy efficiency, data reliability, and contributions to the sustainability of IoT ecosystems, while also raising ethical considerations regarding privacy and information security. Thus, this study demonstrates that the development of PLNC cannot be separated from philosophical reflection, emphasizing the profound interconnection between technological advancement, scientific methodology, and human values.

This study aims to design and develop a functionally efficient and operationally effective Internet of Things (IoT)-based air quality monitoring system for radiology departments. The system utilises a DHT22 sensor integrated with an ESP32 microcontroller to monitor the temperature and humidity of diagnostic rooms in real time, and to display the data via the UdaraKu mobile application. The research method employed a quantitative experimental approach focused on measuring system performance, specifically the accuracy of the temperature and humidity sensors. The research model used was the Research and Development (R&D) method, aimed at transforming conventional air quality monitoring in radiology into a real-time digital system based on IoT. The research results indicate that the IoT-based monitoring system is capable of maintaining room temperature and humidity stability within the ideal range, namely 22–24°C and 50–60% RH, in accordance with international standards. This improvement in environmental stability has a direct impact on reducing noise in digital radiography images, as evidenced by an increase in the Signal-to-Noise Ratio (SNR). Instrument validation demonstrated a high level of reliability with a Cronbach’s Alpha value of 0.848, reinforcing the reliability of the data and the system. Overall, the IoT-based air quality monitoring system has proven effective in controlling noise in digital radiography images, improving the quality of diagnostic services, and supporting patient safety principles and operational efficiency within radiology departments.

This study examines the influence of earnings quality and dividend policy on firm value at PT Alkindo Naratama Tbk during the period 2014–2024. Firm value is measured using the Price to Book Value (PBV), earnings quality is proxied by the ratio of operating cash flow to net income (QOE), and dividend policy is measured using the Dividend Payout Ratio (DPR). This research adopts a quantitative approach with an associative causal design using secondary data obtained from the company’s quarterly financial reports over eleven years, resulting in 44 observations. The analysis method applied is multiple linear regression. The findings reveal that earnings quality has a positive and significant impact on firm value. Dividend policy also shows a positive and significant effect on firm value. Simultaneously, earnings quality and dividend policy significantly influence firm value. The coefficient of determination indicates that a large proportion of firm value variation can be explained by these two variables. These results support signaling theory, which suggests that high earnings quality and stable dividend distribution provide positive signals to investors and increase market confidence in the company. The study contributes to financial management literature by highlighting the importance of financial performance indicators in determining firm value.

Background: In the competitive healthcare landscape, where human resources are pivotal to organizational success, affective commitment defined as emotional attachment to the workplace is essential for nurse retention and service quality. Satya Negara Hospital in North Jakarta exemplifies the challenges faced by Indonesian healthcare institutions, with a notably high nurse turnover intention of 30% in 2024, signaling low affective commitment and underscoring the urgency to identify its drivers. While person-job fit and career development are established antecedents of commitment, their combined influence within Indonesia’s nursing context, along with the potential moderating role of meritocracy, remains underexplored.. Methods:  Using a quantitative, cross sectional design, data were collected via a validated questionnaire from all 108 nurses at the hospital and analyzed using multiple regression.. Results: The results revealed that person-job fit, career development, and meritocracy collectively explain 96.6% of the variance in affective commitment. Furthermore, when meritocracy was tested as a moderating variable, the explanatory power of the model increased significantly, with the adjusted R² value rising from 96.6% to 98.5%. This indicates that the presence of a meritocratic system substantially amplifies the positive effects of both person-job fit and career development on commitment.. Conclusion: The study concludes that a synergistic combination of job fit, growth opportunities, and a merit-based system is fundamental to fostering nurses’ emotional attachment. These findings contribute to organizational and psychological theory integration and offer practical human resource strategies for enhancing nurse commitment and reducing turnover in healthcare settings.

Generalized pustular psoriasis (GPP) is a severe inflammatory form of psoriasis characterized by widespread sterile pustular eruptions. Pro-inflammatory mediators released during chronic inflammation may contribute to comorbidities, including type 2 diabetes mellitus (T2DM). A 65-year-old woman presented with pustular eruptions over almost the entire body for seven days, accompanied by burning sensation and pruritus. Some pustules coalesced forming “lakes of pus,” while others appeared as erythematous plaques with coarse white scales. The patient had a history of T2DM. Based on clinical findings, the patient was diagnosed with GPP. She was hospitalized for seven days and followed up weekly until week 10. Inflammatory mediators such as Th-1, TNF-α, IL-6, IL-7, IL-8, IL-17, and IL-23 in psoriasis may inhibit insulin receptor signaling and glucose uptake, leading to insulin resistance and increased blood glucose levels. The duration of treatment until remission in psoriasis patients with T2DM tends to be longer than in non-diabetic patients. In patients with comorbidities, low-dose methotrexate (5–7.5 mg/week) was administered. The coexistence of GPP and T2DM may lead to more severe exacerbations, prolonged treatment duration, and reduced quality of life, posing challenges in management.

Digital images often experience noise disturbances that can reduce visual quality and interfere with the image analysis process. One common type of noise is salt and pepper noise, especially in grayscale images, which is characterized by the random appearance of black and white dots. This study applied the Deep Convolutional Autoencoder (DCAE) method with a skip connection mechanism to eliminate salt and pepper noise in grayscale images measuring 256×256 pixels. The dataset used consists of 300 pairs of clean images and noisy images that have gone through the preprocessing stage, including normalization and data augmentation. The model was trained using an Adam optimizer with a Mean Squared Error (MSE) loss function and validated through a train-test split scheme to avoid overfitting. Model performance was evaluated using Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) metrics. The test results showed that the DCAE model with skip connections was able to effectively reduce noise while maintaining the main structure of the image based on the PSNR and SSIM values obtained, and showed better performance than conventional median filters. In addition, the model was successfully implemented into a Streamlit-based application to perform the image denoising process interactively, making it easier for users to experiment and visualize results in real-time.

Company value is an important indicator that reflects company performance and investor perceptions of future business prospects and sustainability. Various strategic decisions made by management, such as capital intensity management, investment decisions, and tax aggressiveness policies, play a significant role in shaping company value. This study aims to examine and analyze the effect of capital intensity, investment decisions, and tax aggressiveness on company value through a literature review approach. The method used is a literature review by examining various relevant national and international scientific articles obtained from academic databases such as Google Scholar, Publish or Perish, and SINTA. The results of the study show that capital intensity has a positive effect on company value because it reflects long-term production capacity and operational efficiency. Investment decisions have also been proven to have a positive effect on company value because they signal management's optimism about future growth prospects. Meanwhile, tax aggressiveness can increase company value through tax savings and increased cash flow, but it has the potential to cause reputational and governance risks if done excessively. Overall, the reviewed literature shows that these three variables have an impact on company value, with the caveat that optimal and transparent management is necessary. This study is expected to serve as a reference for further research and as a consideration for company management and investors in making strategic decisions.

This study investigates the phenomenon of quasi-social movements that emerged during the August 25, 2025 protests in Indonesia. Quasi-social movements are defined as forms of public mobilization that appear to arise spontaneously, lack formal organizational structures, and are often leveraged for particular political agendas. Using a qualitative method supported by literature review, this research examines social movement theories, the functions of social media, and the socio-political dynamics that shaped the protests. The findings indicate that the protests were not purely the result of spontaneous civic engagement, but were instead driven by top-down mobilization orchestrated by elite actors through digital narratives, disinformation, and the broader influence of communication technology. This phenomenon signals a shift in the relationship between citizens and the state, including evolving patterns of political participation that do not always follow organized forms. The protests further illustrate how mass mobilization in the digital era can be generated through narrative construction and manipulation of public perception. In conclusion, quasi-social movements represent a new pattern of technology-mediated political expression, highlighting the need for more critical perspectives on modern forms of social mobilization.

This study investigates the determinants of dividend payout policy in energy sector firms listed on the Indonesia Stock Exchange during the 2020–2024 period. Dividend policy is a critical issue in emerging markets, especially in capital-intensive industries with high investment needs and earnings volatility. The research examines whether profitability and ownership structure—specifically institutional and managerial ownership—significantly influence dividend payout decisions, considering firm characteristics. The study analyzes the effect of profitability, institutional ownership, and managerial ownership on the dividend payout ratio, while controlling for firm size and leverage. A quantitative approach is used, employing pooled ordinary least squares (OLS) regression on 245 firm-year observations. Dividend payout ratio is measured as dividend per share divided by earnings per share, profitability is proxied by return on equity, and ownership variables are expressed as shareholding proportions. Descriptive analysis and classical assumption tests precede hypothesis testing. The results show that profitability positively and significantly affects dividend payout, suggesting that firms with better financial performance tend to distribute higher dividends. Firm size also positively influences dividend policy, while leverage negatively impacts it, reflecting the role of financial capacity and capital structure. However, institutional and managerial ownership do not show significant effects on dividend payout decisions. The findings indicate that dividend policy in Indonesian energy firms is primarily driven by financial performance and structural characteristics rather than ownership-based governance mechanisms. This study offers sector-specific evidence that refines agency and signaling perspectives on dividend policy in emerging markets, with practical implications for managers, investors, and regulators.

Youth radicalization is a serious issue that threatens social harmony, national stability, and the sustainability of nationhood in Indonesia. Various reports indicate that nearly one-third of terrorism perpetrators over the past decade have come from young people, signaling ideological vulnerability among the productive age group. This article aims to propose a youth deradicalization approach through contemporary Islamic thought, emphasizing rationality, openness of thinking, and humanism as its main foundations. This study employs a qualitative method with a literature review approach by analyzing the works, ideas, and thoughts of contemporary Islamic scholars and examining their relevance in the context of youth development and empowerment. The findings show that contemporary Islamic approaches that emphasize critical reasoning, dialogue, and respect for humanity are effective in shaping moderate attitudes, fostering social awareness, and strengthening an inclusive and tolerant Islamic identity. Therefore, this article recommends that religious education be integrated into curricula that emphasize the values of religious moderation, respect for diversity, and the rejection of violence. This approach is expected to help young people become more tolerant, adaptive, and resilient against the influence of extremist ideologies that are harmful to society.

This study is motivated by the increasing need for accurate modeling and classification of one-dimensional signal data in intelligent systems. The rapid development of deep learning has led to the adoption of more adaptive and complex neural network architectures capable of capturing both temporal dependencies and local patterns in sequential data. This research aims to analyze and compare the performance of several deep learning models, namely Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and a hybrid Convolutional Neural Network–GRU (CNN–GRU) model for signal data classification. The research method employs a quantitative experimental approach involving data preprocessing, windowing, model training, and performance evaluation. Model performance is evaluated using accuracy, precision, recall, and F1-score metrics. The results indicate that the hybrid CNN–GRU model outperforms the other models, particularly in capturing local features and long-term temporal dependencies within signal data. These findings suggest that the integration of convolutional layers and recurrent mechanisms enhances feature representation and learning stability. This study is expected to contribute both theoretically and practically to the development of deep learning models for signal processing and time-series-based intelligent applications.