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This study aims to analyze and compare the cabin noise levels of the Mitsubishi Destinator Ultimate variant (sunroof) and Exceed variant (non-sunroof) under various driving speed conditions. Measurements were conducted using a Sound Level Meter inside the cabin at idle, 10, 30, 50, 70, 90, and 100 km/h. Results indicate that the Ultimate (sunroof) variant consistently produces higher noise levels than the Exceed (non-sunroof) variant across all speed variations. The largest noise difference was recorded at 100 km/h at 6.50 dB, while the smallest occurred at idle at 0.05 dB. Noise levels in both variants increased significantly with increasing speed, with the sunroof variant reaching 70.00 dB and the non-sunroof variant reaching 63.50 dB at 100 km/h. This difference is attributed to gaps and seals in the sunroof panel, which allow greater noise penetration from the external environment.

This study aims to identify the management system for Taman Imbi by the Department of Public Works and Public Housing (Dinas PUPR) and to analyze its comfort level based on the perceptions of park visitors and street vendors (PKL). Taman Imbi, managed by the PUPR Service (Section for Environmental Management and Parks), is one of the main urban parks in the city center of North Jayapura District. The research uses a descriptive, qualitative approach combined with Likert-scale analysis. Primary data were obtained from questionnaires distributed to 100 park visitors and 10 business actors (PKL) operating around the park. Eight comfort indicators were assessed, based on Hakim (2003): aesthetics, cleanliness, safety, noise, circulation, aroma, physical form, and climate/natural forces. Results indicate that, based on visitor perceptions, the overall comfort level was 56.0% (fairly good), while PKL perceptions were 64.4% (good). Among all indicators, circulation scored the lowest for visitors (46.2%), particularly ease of access. Aroma/odor scored the highest for PKL (80.0%). The study recommends rehabilitating park facilities, reactivating security posts, improving pedestrian and parking access, and clarifying spatial zoning for economic and community activities in the park.

Batik motif classification has attracted growing attention in visual computing due to its role in cultural heritage preservation, textile informatics, museum documentation, and automated cataloging. Although many studies report high classification accuracy, robustness under real-world acquisition conditions remains insufficiently understood. Batik images are frequently affected by illumination variation, blur, folds, watermark overlays, wearable deformation, scale inconsistency, and background clutter, creating challenges that extend beyond conventional image-noise assumptions. Existing studies largely focus on improving classification performance, while the interactions among acquisition variability, feature representation, evaluation practice, and deployment constraints remain fragmented. This systematic literature review addresses this gap by synthesizing batik classification research through a robustness-aware perspective. Using query expansion, backward and forward citation chaining, relevance screening, and thematic coding, 116 candidate records were identified, resulting in 50 highly relevant studies for detailed analysis. The review reveals that robustness is shaped less by denoising alone than by the combined effects of acquisition conditions, representation design, evaluation realism, and deployment context. Handcrafted descriptors remain competitive for small datasets and structured motifs due to their data efficiency and interpretability, whereas deep learning models achieve the highest reported accuracy when supported by sufficient data diversity and realistic augmentation. Hybrid representations emerge as the most consistently balanced approach, combining local texture stability with higher-level abstraction across heterogeneous acquisition settings. The review further identifies recurring robustness failure patterns, including background dependency, illumination instability, motif-scale inconsistency, wearable deformation, and source-shift vulnerability. Based on these findings, a robustness-oriented research agenda is proposed, emphasizing cross-acquisition evaluation, representation-stability analysis, batik-specific robustness benchmarks, acquisition-aware augmentation, and deployable lightweight or hybrid architectures. The study contributes a domain-specific synthesis that reframes batik motif classification from an accuracy-centric task toward a robustness-aware visual recognition problem.

Gesture  recognition  technology  is  used  to  detect  movements  through  image processing,   enabling  computers  or digital devices to understand and interpret human  body  movements  as  input  or  commands.   This  technology  has  great potential  to bridge communication between the deaf community and individuals without   hearing   impairments,    enhancing  interaction  and  enriching  mutual understanding between the two.  However,  the accuracy ofgesture recognition is often  affected  by variations in the distance between hand landmarks.  Based on this problem,  this research proposes a methodfor stabilizing the measurement of distances between landmark points  in gesture recognition through a polynomial regression  approach.   Specifically,   the  distance  between  hand  landmarks  is calculated and stabilized using polynomial  regression to improve the accuracy of gesture recognition.  This method is implemented using the MediaPipeframework to detect and track hands in real-time,  and the OpenCV library to manage video. The  research  results  show  that  this  approach  can  significantly  improve  the stability  and accuracy  of gesture detection.   The developed system successfully detects gestures for  letters A  through F with a high accuracy  rate,  averaging above 98,3%.  The use ofpolynomial regression helps enhance detection accuracy by reducing noise in the landmark data.

This research critically investigates the circulation of disinformation concerning the instability of fuel prices on the digital platform X and its subsequent implications for the polarization of modern society. In an era where unverified economic news frequently dictates public reaction, fake news often acts as a potent catalyst for mass anxiety. By implementing a quantitative framework driven by lexicon-based computational sentiment analysis, this study effectively processed a dataset of 500 public opinion samples extracted via Google Colab spanning from April 2024 to April 2026. To ensure computational accuracy and eliminate textual noise, the data underwent a rigorous preprocessing phase encompassing case folding, alongside the systematic removal of URLs, account mentions, numbers, hashtags, and punctuation marks. The statistical outcomes revealed a highly disproportionate emotional landscape, overwhelmingly dominated by 451 negative reviews. In stark contrast, neutral observations and positive affirmations were nearly absent, recording only 40 and 9 instances, respectively. The data compellingly illustrates that the relentless influx of pessimistic narratives regarding economic instability directly induces financial panic, undermines rational discourse, and severely fragments cyberspace into deeply polarized factions.

Proprioceptive sensor data, including inertial measurement units (IMU), joint encoders, and torque sensors, plays a critical role in state estimation for quadruped robots operating in dynamic and unstructured environments. However, these signals are often degraded by various sources of error, such as high-frequency noise, bias, drift, and contact-induced disturbances, which directly affect estimation accuracy and stability. This study presents a systematic analysis of sensor-specific noise characteristics and evaluates the effectiveness of preprocessing methods tailored to each sensor modality. Specifically, moving average filtering is applied to encoder signals to mitigate noise amplification during differentiation, while first-order low-pass filtering is employed for IMU and torque signals to suppress high-frequency noise. Experimental results on a publicly available quadruped dataset demonstrate that encoder velocity RMSE is reduced by 12.09%, high-frequency energy decreases by 59.63%, and signal-to-noise ratio (SNR) improves by 145.6%. However, variance reductions remain limited (3.39% for IMU and 4.05% for torque), indicating the persistence of impulsive, non-Gaussian noise caused by contact events. These findings highlight that linear preprocessing methods are effective for attenuating high-frequency noise but insufficient for handling non-Gaussian disturbances. The study provides practical insights into the effectiveness and limitations of preprocessing strategies, serving as a foundation for developing more robust signal processing and state estimation frameworks in quadruped robotics.

This research aims to analyze the role and impact of PT Batujaya Bersama Sejahtera (PT BBS) on the socio-economic conditions of the community in Walandano Village, Balaesang Tanjung District. The background of this study is driven by the massive expansion of the mining industry in Central Sulawesi, which triggers a structural shift from traditional agriculture to an industrial economy. This study employs a qualitative method with data collection techniques including in-depth interviews, observation, and documentation. The findings indicate that PT BBS plays a significant role in local economic development by providing employment opportunities, increasing household income, and improving public infrastructure such as roads and jetties. However, the study also identifies social disruptions, including public protests regarding land issues and environmental concerns like dust and noise pollution. The implications of this research suggest that the company must strengthen its Corporate Social Responsibility (CSR) programs by focusing on sustainable community empowerment and more transparent communication to mitigate social risks. These findings contribute to the literature on regional economic development and social change in coastal mining areas.

The steering gear is a vital component in ship operations, functioning to control the ship’s direction accurately. The reliability of this system highly depends on the performance of the hydraulic system, where the mechanical seal plays a key role in maintaining system pressure and preventing oil leakage. This study aims to identify the main factors causing mechanical seal damage and analyze its impact on the performance efficiency of the steering gear AHTS Logindo Stamina. Qualitative data were obtained through participatory observation and in-depth interviews with the second engineer. Meanwhile, quantitative data were gathered by measuring technical parameters such as oil pressure, operating temperature, and rudder movement time, which were then compared with SOLAS standards. This damage results in oil leakage through the mechanical seal gap, abnormal noise in the hydraulic system, and a significant decrease in working pressure of -25,86%. The operational impacts include the occurrence of steering gear failure alarms, decreased hydraulic efficiency of 25.87%, and the risk of system failure that could endanger ship maneuverability. This study recommends preventive maintenance through oil quality monitoring, environmental condition control in the steering gear room, and periodic mechanical seal replacement in accordance with operational standards to maintaining optimal steering system performance.

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 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.

The hospital laundry installation is a non-medical supporting unit with a high risk of occupational health and safety (OHS) hazards due to direct exposure to chemicals, machinery, and contaminated linen. Potential hazards in this unit include physical (heat, noise), chemical (detergents and disinfectants), biological (viruses and bacteria from linen), and ergonomic (improper working posture) risks. This study is a descriptive quantitative research with a semi-quantitative approach. The aim is to identify, assess, evaluate, and control OHS risks in the laundry unit. Data were collected through observation, interviews, and document review with seven laundry staff members. Risk assessment was conducted using a risk matrix based on the AS/NZS 4360:2004 standard. The results show that several risks ranged from low to very high levels, including infection from linen, chemical exposure, noise, and injury from machinery. Risk control measures implemented include applying the hierarchy of controls, starting from elimination, substitution, engineering controls, Administratif controls, and the use of personal protective equipment (PPE). It is concluded that improvements in supervision, staff training, and facility upgrades are necessary to minimize occupational accidents in the laundry installation.

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 research is motivated by the negative impacts of production activities at a manufacturing company engaged in rubber processing, specifically at PTPN VII Unit Pematang Kiwah Natar, South Lampung. The factory's operations directly impact the environment, generating noise pollution, air pollution, unpleasant odors, and liquid waste. This situation requires the company to implement Environmental Management Accounting (EMA) to balance business sustainability with social and environmental responsibility. This is in line with Law No. 40 of 2007 concerning Limited Liability Companies and PSAK 1 of 2021. The main objective of the study was to evaluate the suitability of the implementation of environmental management accounting at PTPN VII Uni ;t Pematang Kiwah Natar, South Lampung, based on the International Guidance Document IFAC 2005 and PSAK 1 of 2021. This study used a qualitative descriptive method. Primary and secondary data were collected through interviews, observation, and documentation. The research results show that the company has implemented environmental management accounting using PSAK 1 of 2021, where the company has fulfilled the identification, presentation, measurement, recognition, and disclosure stages using the 2022 sustainability report and the 2022 financial statements of PTPN VII. Furthermore, PTPN VII Unit Pematang Kiwah Natar, South Lampung, has classified environmental costs by allocating environmental costs based on the International Guidance Document IFAC 2005 and Ikhsan (2008). Therefore, PTPN VII Unit Pematang Kiwah Natar, South Lampung, has demonstrated its commitment to environmental regulatory compliance.

Occupational Health and Safety (OHS) is an important element in creating a safe, healthy, and sustainable work environment. PT. X as a logistics and port operations company has potential occupational hazards originating from physical, chemical, and biological factors that need to be managed optimally. This study aims to evaluate the implementation of OHS at PT. X based on the results of measurements of physical, chemical, and biological factors of the work environment and their compliance with the provisions of the Minister of Manpower Regulation No. 5 of 2018. This study uses a descriptive method with an evaluative approach to work environment monitoring data in 2025 in the generator and office areas. The parameters analyzed include noise, lighting, hot work climate (ISBB), inhalable and respirable dust exposure, and microbiological air quality in the form of total bacteria and fungi. The results show that most parameters meet the specified standards, with the exception of the generator area which exceeds the noise limit and the hot work climate which exceeds the Action Level (AL). The implementation of OHS at PT. X has been running quite well, indicated by most of the work environment parameters that meet the standards. However, strengthening risk controls, particularly regarding noise and hot working conditions in operational areas, is still necessary. This evaluation is expected to serve as a basis for continuous improvement in the implementation of Occupational Health and Safety (OHS) to protect workers from potential occupational hazards and support the productivity and sustainability of company operations.

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.

This study aims to develop an Indonesian traffic sign detection system using a transfer learning approach to improve road safety and traffic efficiency. The dataset was obtained from Kaggle and consists of 2,100 images across 21 traffic sign classes. The research stages include data collection, preprocessing to reduce noise and normalize image brightness, object detection using YOLOv5, and classification based on transfer learning with ResNet, VGG-16, and MobileNet architectures. Model performance was evaluated using accuracy, precision, recall, and F1-score metrics. Experimental results indicate that the YOLOv5 model is capable of detecting traffic sign objects; however, the classification performance remains relatively low, with a mean Average Precision (mAP) value of 0.17. These findings suggest that further optimization is required in data preprocessing, dataset quality, and model parameter tuning to achieve better performance. This study demonstrates that transfer learning has significant potential for developing computer vision-based traffic sign detection systems, although further improvements are necessary to ensure robustness under real-world Indonesian traffic conditions.

Pedestrian is one of the most important public spaces for urban areas. On the border of Muara Enim city has a pedestrian that attracts attention, namely the pedestrian welcome intersection kepur. Simpang Kepur pedestrian has a border gate that is the center of attention and the first impression when entering the city of Muara Enim so that it has the opportunity as a face or symbol of the identity of the city of Muara Enim. Visually, the existence of pedestrians and gates at the Kepur intersection looks quite attractive but functionally it is not in accordance with the characteristics of pedestrian activities on the pedestrian so that research is needed to rearrange the previous design so that the function of the pedestrian becomes even better. The method used is qualitative through observation based on facts and activities in the field. Analysis based on the impression of place and activity on the pedestrian. The results obtained that there is a need to change the appearance of the color processing so that the pedestrian becomes more alive, need to keep the pedestrian so that there is no loss or damage, the need for guardrails or vegetation / view barrier plants in the area behind or beside the pedestrian, rearrangement of plants that can absorb dust and can absorb noise, and arrangement of street furniture, namely visual recommendations for a wider bus stop design, replace permanent seating, replace permanent trash.

One of the most common occupational health risk factors found in industrial areas is noise, and long-term exposure ≥85 dB(A) for more than 8 hours has the potential to cause progressive and permanent Noise-Induced Hearing Loss (NIHL). The risk of NIHL increases if workers do not use Ear Protection Equipment (APT) optimally and have certain individual factors such as age, working period, and length of exposure. This study aims to determine the relationship between the use of APT and worker factors with hearing loss due to noise in PT HMK II Palembang City in 2025. This study uses an analytical quantitative design with a cross sectional approach. A sample of 55 respondents was selected through purposive sampling technique. Data was collected using a questionnaire and analyzed by chi-square test. The results showed that there was a significant relationship between the use of APT (p=0.000) and there was no significant relationship between, the age of workers (p=0.838), working time (p=0.290), and length of work (p=0.172) and noise-induced hearing loss. The use of APT has been shown to be associated with a reduced risk of noise-induced hearing loss, although age, length of employment, and length of work did not show a significant relationship.

Big data platforms face significant challenges related to cybersecurity and privacy due to the vast volume, variety, and velocity of data they manage. Traditional static security measures often fail to address the dynamic and complex nature of big data environments. This research proposes an adaptive cybersecurity framework that integrates dynamic access control and differential privacy mechanisms to enhance both the security and privacy of big data platforms. The dynamic access control mechanism continuously adjusts access permissions in real-time based on changing risk and trust levels, ensuring that sensitive data remains secure even as user roles and data flows evolve. The differential privacy mechanism adds noise to data, preserving individual privacy while allowing for meaningful data analysis. Through simulations and case studies, the framework was evaluated in various real-world environments, including healthcare, IoT, and finance, where it demonstrated scalability, efficiency, and robust security performance. The results showed that the proposed framework significantly reduced unauthorized access attempts and maintained data privacy, while still enabling effective data analysis. Although there were some challenges regarding performance overhead, particularly in resource-constrained environments, the framework remained effective in large-scale systems. The findings highlight the importance of adaptive security practices in big data environments and suggest that future research should focus on refining dynamic security mechanisms and applying differential privacy in diverse real-world scenarios. These advancements are essential for ensuring that big data platforms can handle evolving cyber threats without compromising data utility or privacy.

Benign Paroxysmal Positional Vertigo (BPPV) is the most common cause of peripheral vertigo, characterized by brief episodes of vertigo due to otoconia displacement. Although most previous studies have focused on intrinsic factors such as age, gender, osteoporosis, and metabolic disorders, evidence regarding the role of environmental factors, particularly occupational noise exposure, is limited. Chronic noise has the potential to affect vestibular function through both sensory and vascular mechanisms. This study aims to narratively review the effect of occupational noise exposure on the risk of BPPV by integrating clinical, epidemiological, and experimental findings. The method used is a literature-based narrative review of the PubMed, Scopus, Web of Science, and Google Scholar databases without year restrictions, using the keywords "BPPV", "occupational noise exposure", "vestibular dysfunction", "VEMP", and "otoconia displacement". The search results obtained 25 relevant articles linking BPPV to otolith, hormonal, vascular, lifestyle factors, and occupational noise exposure. The results indicate that chronic noise can cause sensory damage (otoconia and vestibular hair cells), vascular disorders (hypertension, cardiovascular disorders, and inner ear microvascular circulation disorders), and exacerbate lifestyle comorbidities (sedentary lifestyle, osteoporosis, hypertension, diabetes). The discussion confirms that these multifactorial mechanisms explain the susceptibility of industrial workers to BPPV despite normal hearing function. The conclusion of this study is that workplace noise exposure has been shown to play a significant role as a risk factor for BPPV, therefore, prevention strategies, vestibular health monitoring, and healthy lifestyle interventions need to be optimized in occupational health programs.