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An important digital image processing is image segmentation, which separates objects from the background for further analysis. One segmentation technique is edge detection, which looks for boundaries between areas of different brightness. This article compares four edge detection methods: Roberts, Prewitt, Sobel, and Canny. The results show that, despite requiring more complex computations, Canny's method produces the sharpest and best connected edges; Sobel and Prewitt's method, on the other hand, is faster and simpler than Roberts' method, but is less effective in dealing with noise and often produces edges that are not connected to the plane. The choice of edge detection method depends on the application. Sobel and Prewitt are good for speed and stability, and Roberts is suitable for fast processing of images with minimal noise.

The background to this research is due to the response of the public who feel disturbed by the existence of this layer chicken farming business due to the strong smell of farm waste, the presence of large numbers of fly insects and the noise coming from the breeder's cage. The focus of this research is what the community's response is to the existence of a layer chicken farm in Tambakrejo Village, Sumbergempol District, Tulungagung Regency and what the social impact of the community is with the existence of a layer chicken farm in Tambakrejo Village, Sumbergempol District, Tulungagung Regency. The aim of this research is to find out what the community's response is to the layer chicken farming business in Tambakrejo Village, Sumbergempol District, Tulungagung Regency. To find out what social impacts are felt by the community towards the layer chicken farming business in Tambakrejo Village, Sumbergempol District, Tulungagung Regency. This research uses descriptive qualitative methods with data collection techniques carried out by observation, interviews and documentation. The research location was carried out in Tambakrejo Village with the research subjects being the people of Tambakrejo Village whose houses were close to a layer chicken farm. The results of this research show that the responses given by the community are positive and negative. The community's positive response is that the community supports the existence of this farm. The negative response given by the community was that the community objected to the existence of this livestock business by giving responses that were indifferent and less concerned. The social impact felt by the community with the existence of a layer chicken farm in Tambakrejo Village, Sumbergempol District, Tulungagung Regency, is seen from a negative aspect. The community feels disturbed by the strong smell that comes from the manure of the layer chicken farm. Apart from that, flies coming to people's homes also make people feel uncomfortable because of the large number of flies. The community also complains about the water they use every day because the water digging is close to the farm, which has an impact on the smell of the water in the digging. Viewed from a positive aspect, people can easily buy or order eggs from breeders at much cheaper prices, besides that breeder owners often distribute eggs to local communities, especially during the month of Ramadan.

Optimal health services require supporting examinations in diagnosing a disease. On lumbosacral examination, radiograph quality is often less than optimal. Digital radiography is a new system on x-ray machines that has features for better image processing, one of which is flexible noise control (FNC). This study aims to determine the effect of digital radiography processing settings on image quality on lateral position lumbosacral examination. In this study using a qualitative descriptive method with an experimental or experimental research approach. The results showed that there was an effect of digital radiography processing settings on image quality with the FNC feature. This was shown by using the Image J application, there was a change in the standard deviation value before and after processing with the FNC feature. It is known that the FNC feature suppresses the noise value in the radiograph images.

implemented and evaluated the edge detection method using the Sobel Operator, which calculates the gradient of image intensity through two convoluted kernels for horizontal (Gx) and vertical directions. (Gy). The magnitudo gradient is obtained from the combination of both such directional gradients to represent the edge force on each pixel. The main steps include image pre-processing, the application of the Sobel kernel, the calculation of magnitudo gradients, and the filtering of results to extract significant edges. The results show that the Sobel Operator is effective in highlighting intensity differences that indicate the boundary of the object, although it is sensitive to noise and less accurate for fine edges. Despite its limitations, this method is simple to implement and useful as an initial step in image processing applications such as segmentation, pattern identification, and object shape analysis.  

Construction work is one of the jobs that has various high risks for workers and people who are around it. These risks come from various sources, one of which is noise from various equipment and ongoing activities. One of the jobs that cause high noise in construction work is MEP (Mechanical Electrical Plumbing) work. Based on Permenaker Number 13 Year 2011, the Threshold Value (NAB) for noise that can be accepted by workers is 85 dBA for 8 hours a day. Based on the measurement of the construction of the flats, the MEP work in the form of Beam Separator Elevator Installation, Pipe Fabrication, Electrical Wiring Lighting Installation, as well as Welding and Grinding of Iron Pipe causes noise above the NAB. The highest noise comes from pipe fabrication in the form of iron pipe cutting activities using large grinding tools and bevel machines that produce noise of 107 dBA. From the Daily Noise Dose (DND) calculation, the noise generated from MEP work has a dangerous risk level for workers' hearing. Risk control efforts that can be done are preventive actions based on the K3 hierarchy in the form of administrative controls such as changing working hours or shifts and conducting safety talks before work is carried out. In addition, another preventive measure is the use of PPE such as ear plugs. Based on the calculation of the noise reduction level, the use of ear plugs has not reduced the noise level to reach the NAB, so it is recommended that workers in pipe fabrication work use ear muffs to reduce noise exposure.

The Lapindo Mud Disaster that occurred in Sidoarjo in 2006 has become one of the most phenomenal events in Indonesia, causing the sinking of 16 villages and drastic changes to the landscape and lives of the surrounding community. The aim of designing the Lapindo Mud Museum is as a solution to fulfill its function not only as a place to store artifacts and information, but also as a space for interaction and reflection for the community. The research method used is descriptive qualitative. Design using a metaphor approach, namely the preparation of planning and design concepts with several analyses, namely, urban analysis, site analysis, land use analysis, circulation analysis, wind direction analysis, sun direction analysis, noise analysis, several concept preparations. The research results show that the building design is used as a symbol of the Lapindo Mudflow and commemorates the victims and communities affected by the Lapindo Mudflow. With a metaphorical architectural approach, the museum is expected to overcome the boredom that often occurs during conventional museum visits and facilitate deeper interactions with visitors.

An ergonomic work environment is designed to enhance the comfort, efficiency, and well-being of employees by optimizing the interactions between individuals and their workspaces. This approach encompasses the arrangement of furniture, the selection of equipment, and the configuration of the physical workspace to prevent musculoskeletal disorders, reduce fatigue, and improve productivity. Key elements include adjustable chairs and desks, proper lighting, adequate ventilation, and the strategic placement of computer monitors and keyboards. By incorporating ergonomic principles, organizations can mitigate the risk of repetitive strain injuries and other work-related ailments, fostering a healthier and more engaged workforce. The implementation of ergonomic solutions not only contributes to physical health but also supports mental well-being by reducing stress and enhancing overall job satisfaction. As a result, businesses benefit from increased employee performance, lower absenteeism, and a more positive organizational culture.  

Digital Pathology Image Analysis (DPIA) is one of the areas where deep learning (DL) techniques offer modern, cutting-edge functionality. Convolutional Neural Network (CNN) technology outperforms the competition in classification, segmentation, and detection tasks while being just one of numerous DL techniques. Classification, segmentation, and detection methods can often be used to address DPIA concerns. Some difficulties can also be resolved using pre- and post-processing techniques. However, other CNN models have been investigated for use in addressing DPIA-related issues. Furthermore, the research seeks to explore how susceptible the model is to adversarial attacks and suggest strategies to counteract them. To predict ischemic strokes caused by blood clots, the authors of this study developed CNN with a pixel brightness transformation (PBT) technique for image enhancement and developed several approaches of image augmentation techniques to increase and provide the learning model with more diverse features. Also, adversarial training was integrated into CNN models to train the model with perturbed data in order to assess the impact of adversarial noise at different stages of training. Several metrics, including precision, F1-score, accuracy, and recall, are utilized to assess the experiments' effectiveness. The research findings indicate that employing transfer learning with a deep learning model achieved an accuracy of up to 97% using the ReLU activation function. Also, data augmentation helps improve the accuracy of the model.

This community service activity aims to increase public understanding of how to reduce the impact of noise and carbon emissions from motor vehicles. Noise and carbon emissions from motor vehicles are environmental problems that are increasing along with the growth in the number of vehicles in urban and rural areas. These impacts not only affect air quality, but also the physical and psychological health of the community. This community service activity aims to empower the community in reducing the impact of noise and carbon emissions from motor vehicles through education, training, and concrete actions based on community participation. The implementation methods include socialization of the impacts of noise and emissions, training on environmentally friendly vehicle maintenance, and campaigns promoting the use of alternative modes of transportation such as bicycles, walking, or public transportation. The results of the activity showed an increase in community knowledge and awareness of environmental issues and the formation of self-help groups that play an active role in campaigning for environmentally friendly behavior. With this program, the community is able to contribute to reducing noise and air pollution, while supporting the creation of a healthier and more sustainable environment.

In the rapidly developing digital era, social media such as Twitter has become part of everyday life and facilitates the rapid dissemination of information, including information about criminals. This research aims to analyze public sentiment towards information about criminals spread on Twitter using the Naive Bayes algorithm. This algorithm was chosen because of its simplicity and effectiveness in text classification. Data was collected through a crawling process from Twitter, followed by a preprocessing stage to remove noise. The research results show that public sentiment towards information about criminals on Twitter is divided into three categories: positive, neutral and negative. After classification, it was found that neutral sentiment increased significantly to 63.4%, while positive and negative sentiment decreased to 10.5% and 26.1%. These findings indicate that people tend to be more careful in reacting to sensitive information. This research provides important insights for related parties in managing information about criminals on social media and can be a reference for developing further policies and strategies.

This study proposes an approach for human fall classification utilizing a combination of Weighted Moving Average (WMA) and Convolutional Neural Networks (CNN) on the SisFall dataset. Falls among elderly individuals pose a significant public health concern, necessitating effective automated detection systems for timely intervention and assistance. The SisFall dataset, comprising accelerometer data collected during simulated falls and activities of daily living, serves as the basis for training and evaluating the proposed classification system. The proposed method begins by preprocessing accelerometer data using a WMA technique to enhance signal quality and reduce noise. Subsequently, the preprocessed data are fed into a CNN architecture optimized for feature extraction and fall classification. The CNN leverages its ability to automatically learn discriminative features from raw sensor data, enabling robust and accurate classification of fall and non-fall events. Experimental results demonstrate the efficacy of the proposed approach in accurately distinguishing between fall and non-fall activities, achieving high classification performance metrics such as accuracy, precision, recall, and F1-score. Comparative analysis with existing methods showcases the WMA-CNN hybrid approach's superiority in classification accuracy and robustness. Overall, the proposed methodology presents a promising framework for real-time human fall classification using sensor data, offering potential applications in wearable devices, ambient assisted living systems, and healthcare monitoring technologies to enhance safety and well-being among elderly individuals.

The researcher chose the traditional game Bubuta'an to overcome noise outside the classroom due to school renovations which resulted in students' lack of focus on learning. The traditional game Bubuta'an is also a traditional game in South Kalimantan which in Indonesian is known as the game Si Buta-Si Buta. The aim of the research is to analyze the role of traditional babuta'an games in increasing students' learning motivation. The type of research used is Classroom Action Research with a qualitative approach. The research results show that the percentage of students' classical learning motivation obtained at the first meeting was 75% with the criterion "Mostly Skilled", at the second meeting it had increased to and had increased at the second meeting to 92% with the criterion " Almost All Skilled" and at the third meeting it had increased to 96% with the criteria "Almost All are Skilled" . This was because at the first meeting several students had not achieved the assessment indicators. So these conditions are below standards set by researchers for developing students' learning motivation. To overcome these obstacles, educators make improvements motivating, providing reinforcement, paying attention and reminding students to be more interested in participating in the learning process. The conclusions obtained by traditional game Babuta'an can be used as a means of developing students' learning motivation.  

The rapid growth of data-intensive applications has posed significant challenges for classical machine learning (ML) algorithms, particularly in terms of computational efficiency and scalability. This study explores the role of quantum computing in optimizing machine learning performance through the implementation of Quantum Machine Learning (QML), specifically using the Quantum Support Vector Machine (QSVM) model. The research adopts a Design Science Research approach, involving problem identification, model development, system implementation, and performance evaluation. Both classical Support Vector Machine (SVM) and QSVM models are developed and tested using benchmark classification datasets. The results indicate that QSVM outperforms the classical SVM model across multiple evaluation metrics, including accuracy, precision, recall, and F1-score. Additionally, QSVM demonstrates improved computational efficiency by reducing training time, particularly when handling high-dimensional data. These improvements are attributed to the ability of quantum computing to utilize quantum kernel methods and map data into higher-dimensional feature spaces, enabling better pattern recognition and classification performance.  Despite these promising outcomes, the study also identifies several limitations related to current quantum hardware, such as noise, decoherence, and limited qubit availability, which may affect scalability and practical implementation. Therefore, further research is required to enhance quantum hardware reliability and develop hybrid quantum-classical models. In conclusion, quantum machine learning offers a promising solution to overcome the limitations of classical approaches, providing enhanced performance and efficiency for complex data processing tasks in future intelligent systems.

This study aims: to describe the composition and behavior of exhaust gases of motorized vehicles that can have an impact on human health. Based on the results of a theoretical study of various information that the main pollutants in motor vehicle exhaust gases are carbon monoxide (CO), hindrocarbon compounds, nitrogen oxides (NOx) and sulfur (SOx), and dust particulates including lead (PB). Chemical reactions in the atmosphere sometimes take place in a long and complex reaction chain, and produce an end product that can be more active or weaker than the original compound. Motor vehicle exhaust gases which have an impact on health are classified as follows; (1). Pollutants which mainly interfere with the respiratory tract. Included in this group are sulfur oxides, particulates, nitrogen oxides, ozone and other oxides, (2). Pollutants that cause systemic poisons, such as monoxide and lead / lead hydrocarbons, (3). Pollutants suspected of causing cancer such as hydrocarbons, (4). Conditions that interfere with comfort such as noise, street dust, etc. So the conclusion is that in anticipating the negative impacts caused by motor vehicle exhaust gases, the role of the government in setting several regulations and policies in the environmental field is needed, where every business or activity is prohibited from violating the quality standards and standard criteria of environmental damage set by government.

There are 514 urban areas in the archipelagic nation of Indonesia (Ministry of Home Affairs, 2016). There are many cities in Indonesia that are able to provide a sense of comfort and security for their residents, this of course has an impact on population growth. However, the amount of waste that can be generated and stored is limited by the carrying capacity of the urban environment. Using the idea of ​​a "green city" which consists of nine elements of road infrastructure, traffic, green open space, clean water, noise, energy, housing, clean air and buildings is one way to overcome the above problems. All these qualities need to be utilized immediately with a comprehensive systems approach and in accordance with economic, social and environmental principles in sustainable development. Blue economy approaches can be used to eliminate waste. The “blue economy” is a term that is becoming increasingly popular in today's seas and oceans. The aim of this concept is to combine sea base development opportunities with environmental management and protection. Four conceptual interpretations of the blue economy were determined through research into the dominant discourse in international economic policy documents. How the blue economy works is also examined through an “in practice” analysis of the blue economy and related actors. Next, the scope and focus of the blue economy is explored specifically on the maritime industry, which is included or excluded in various conceptualizations. This analysis reveals areas of agreement and conflict. The consensus range reflects the growing trend towards the commercialization and valuation of nature, delineating and defining maritime boundaries and enhancing the security of the world's oceans. There are several areas of conflict, particularly disputes over the legitimacy of individuals as part of the “blue economy”, highly carbon-intensive industries such as oil and gas and the growing deep sea mining industry. Oceans are becoming increasingly important in terms of potential international trade opportunities through intermediaries and buyers. the behavior of the model supporting such a relationship in the ocean is acceptable from a regular and economic point of view. The relationship between land and sea is increasing in the role and importance of the "blue economy" as the term emerges in the background. . A literature review was conducted to analyze the correct definition of the blue economy. This definition is analyzed based on the minimum requirements that are important for the blue economy. This article also tries to compile various types of activities related to marine services. This is done to determine what can be considered priority areas for blue economic growth. The blue economy concept is based on national economic development. This is comprehensively useful for achieving overall national development. Environmental accounting is stated as a process of reducing negative values ​​and creating positive values ​​in environmental accountability.

The conflict between Israel and Palestine has been a prominent topic of international discourse for several decades. This dispute spans a century, commencing in 1917 and persisting to the present day. This research delves into sentiment analysis of the Indonesian community concerning the Israel-Palestine conflict through the Twitter social media platform, with a specific focus on boycotting Israeli products. Utilizing Orange and Naive Bayes classification, the study analyzes over 300 datasets of tweets acquired through the scraping process. The objective is to comprehend the nuances, trends, and variations in sentiment among Twitter users regarding the issue of boycotting Israeli products. The results reveal that the majority of the population tends to support the boycott, with a Naive Bayes classification accuracy of 95%, Precission of 96%, Recall of 95%, and F1 Score of 95%. The data preprocessing process, encompassing transformation, tokenization, and filtering, effectively eliminates noise and prepares the data for a more in-depth sentiment analysis.

Background: Hearing loss is one of the occupational diseases. A noisy work environment as one of the impacts of the industrial sector. This is the most common cause of hearing loss. The National Institute of Health in 2012 reports that about 15% of Americans aged 20-69 years experience hearing loss in connection with work activities, because noise can cause hearing loss, the government sets standards that regulate permissible noise exposure. Hearing loss due to noise occurs slowly, in a matter of months to years. This is often not realized by the sufferer, so that when the patient starts complaining of hearing loss, it is usually already in an irreversible stage. Such conditions will affect labor productivity which will ultimately cause a decrease in the health of the workforce. In certain cases, hearing loss due to noise begins for 6 to 10 years after exposure to loud noises. PT. Riau Graindo was established on January 17, 1997 located in Desa Sidomulyo Barat Kecamatan Tampan Kota Pekanbaru, a publishing company under of Riau Pos Group (RPG), based in Pekanbaru. Research Objectives: To determine corelations of work period with the incidence of hearing loss in employees of PT. Riau Graindo Pekanbaru in 2019. Research Methods: This study was an observational analytic study with a cross sectional approach. Research Results: Majority of PT. Pekanbaru Graindo Pekanbaru that is > 5 years as many as 21 people (70%). The majority of employees of PT. Riau Graindo Pekanbaru experiencing hearing loss as many as 18 people (60%). Chi square test results obtained Pvalue = 0.001, it can be concluded that there is a relationship between work period and the incidence of hearing loss in the employees of PT. Riau Graindo Pekanbaru in 2019. Conclusion: It can be concluded that there is a relationship between working period and the incidence of hearing loss in the employees of PT. Riau Graindo Pekanbaru in 2019 with Pvalue = 0.001 smaller than α (0.05).

Background: Lung tumors are the growth of abnormal lumps in lung tissue which can be benign or malignant. One diagnostic imaging modality that is more supportive of lung disorders is by using a Computed Tomography Scan examination. Image manipulation can be done to improve the appearance of the image. One of the most common image processing techniques is called windowing. This technique allows the technology to change the contrast of the displayed image by adjusting the window width (Window Width) and window level (Window Level). Method: The type of research used in this research is quantitative research with a quasi-experimental approach, which aims to determine the effect of variations in window width and window level on image quality in CT scans of the Thorax with clinical lung tumors. Using variations of Window Width and Window Level WW 1000 and WL -500, -600, -700, WW 1250, WL -500, -600, -700 and WW 1550, WL -500, -600, -700. The author took CT scan data of the Thorax with clinical lung tumors in 4 samples. Then an assessment was carried out in the form of a check list by respondents, namely 3 radiographers and 1 radiologist. The data is then processed using SPSS. Results & Conclusions: There is an influence of WW 1000 and WL -500, -600, -700, WW 1250 and WL -500, -600, -700 and WW 1550 and WL -500, -600, -700 on image quality, namely artifacts and noise on Thorax CT Scan examination with lung tumor cases, the conclusion was obtained from the results of the Friedman test which showed a p.value value of <0.001 or smaller than 0.05 so that H0 was rejected and Ha was accepted. From the results of the mean rank Friedman test, it is known that the value of image quality is artifacts and noise in Thorax CT scans with lung tumor cases. Variations in window width 1550 and window level 500 are able to produce the best image quality with minimal artifacts and noise. Thorax CT scan examination in lung tumor cases.

Background: Kidney stones (nephrolithiasis) are solid masses formed from urine precipitated crystals. The formation of stones is caused by increased calcium, oxalate and uric acid content in the body, scans to view kidney stones (nephrolithiasis) can be performed with or without contrast. Non-contrast CT urography is the imaging modality of choice for the initial evaluation of patients with suspected urolithiasis, as it has a sensitivity of up to 98% and specificity of 96-98%. Components that affect image quality in CT scans are spatial resolution, contrast resolution, and noise. One of the important parameters in image quality is the selection of slice thickness. Spatial resolution is the ability to show objects or organs with a high level of contrast. Noise is the standard deviation percentage of the sum of pixel values obtained from the waterbath image. Methods; This type of research is quantitative research with an experimental approach to analyze the effect of slice thickness variations on image quality using variations of 2 mm, 3 mm, 4 mm, 5 mm, and 6 m. The data were collected in August 2023 with 10 samples. Data were collected in August 2023 with 10 samples. Researchers took urographic CT scan examination data with a diagnosis of nephrolithiasis. Results and Conclusion: With the results of the research conducted, it can be concluded that in the overall Friedman test results there is an effect of using slice thickness variations on image quality in urographic CT Scan examinations with nephrolithiasis diagnoses. using slice thickness variations. Based on the results of this study, the researcher can recommend on CT Scan urography with nephrolithiasis diagnosis using 4 mm slice thickness to get good and optimal image quality results.

Background: The component that can affect image quality is noise. Noise in CT Scan images comes from random variations in photon detection. Scan time is the time it takes for x-rays to exit for data collection of each slice. Pitch is one of the important component parameters in the CT Scan protocol and fundamentally affects the quality of the resulting image, as well as the time required for scanning. The use of pitch in the Radiology Installation of Ibnu Sina Islamic Hospital Pekanbaru in the head CT Scan examination is 0.55. The pitch value variations used by the author in this study are pitch 0.55, 0.75, 1, 1.25 and 1.5.Methods: This type of research is quantitative research using an experimental approach. The study was conducted to determine the effect of pitch value variation on image quality (noise) and scan time using a water phantom on head CT Scan examination protocol using pitch 0.55, 0.75, 1, 1.25 and 1.5. A sample of 10 CT Scan images was taken, and the image quality (noise) was measured by identifying a specific region in the image (Region of Interest or ROI), while the scan time value was observed from the consul monitor. Data analysis was performed using one-way ANOVA statistical test with a significance level of 5%.Results: The results show that there is a significant difference in image quality (noise) on pitch value variation, with a significance level of 0.004, and there is also a significant difference in scan time on pitch value variations with a significance level of less than 0.001. Conclusions: Based on the conclusion, pitch 1 is the value that produces optimal noise level and scan time in head CT Scan protocol using water phantom.