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This research is motivated by the decreasing production of gold mines every year, which will also affect the company's profitability. As for the methods used to measure the level of profitability of a company, one of them is by using return on assets. The level of profitability is also influenced by several other factors, including the current ratio and debt to equity ratio. The purpose of this research is to determine and analyze the influence of the Current Ratio and Debt To Equity Ratio on Return On Assets, both partially and simultaneously. This research uses descriptive quantitative methods. Researchers collected, classified and analyzed sample data using purposive sampling techniques. With 6 company samples consisting of 30 data selected and analyzed using the IBM SPSS version 27 program. The results of research and partial hypothesis testing. Current Ratio has no significant effect on Return On Assets with a value of tcount 1.228 < ttable 2.052. Debt To Equity Ratio has a significant effect on Return On Assets with a value of tcount 2.725 > ttable 2.052. The results of research and hypothesis testing with a value of Fcount 4.020 > Ftable 3.369 from the Current Ratio and Debt To Equity Ratio simultaneously have a significant effect on the Return On Assets of gold mining industry subsector companies listed on the Indonesia Stock Exchange for the 2018-2022 period.

Coal is one of the main energy sources and the largest contributor to national revenue; however, its management faces challenges related to limited availability and accuracy in reserve estimation. An essential aspect of mining management is monitoring the Run of Mine (ROM) volume, which plays a critical role in crushing, washing, and blending processes. This study aims to compare the accuracy of ROM volume measurements using Terrestrial Laser Scanner (TLS) and Unmanned Aerial Vehicle (UAV) methods in the production area of PT FAD, Berau Regency, East Kalimantan. A quantitative descriptive approach was employed, involving field data acquisition, three-dimensional modeling, and volume analysis using specialized software. The results show that ROM volume measured with TLS was 1,407.669 lcm, while UAV produced 1,387.357 lcm, with a difference of 20.312 lcm or 1.45%. This deviation is within the ASTM D6172-98 tolerance limit (<2%), indicating that both methods are valid. Although TLS offers higher accuracy, UAV is more effective and efficient in terms of measurement time, making it a reliable alternative for modern mining monitoring. This study provides practical insights for the mining industry in selecting ROM volume measurement methods that are not only accurate but also efficient in supporting sustainable operations and data-driven decision-making.

One approach the government employs to decorate public welfare, mainly among low-income families, is through social help initiatives. however, the subjectivity inside the choice process regularly ends in mistargeting all through implementation. This observe objectives to apply the ok-Nearest Neighbor (ok-NN) and Naive Bayes algorithms inside a decision support device to perceive eligible recipients based on community statistics. The ok-NN algorithm determines similarity by calculating the Euclidean distance among new and current facts, whilst the Naive Bayes set of rules utilizes a probabilistic method based at the likelihood of attribute incidence inside each elegance. Key criteria considered consist of household income, employment kind, number of dependents, housing conditions, and asset possession. Experimental consequences reveal that each algorithms are powerful in as it should be classifying eligibility for help, with k-NN barely outperforming Naive Bayes. therefore, the combination of these algorithms can support stakeholders in making extra goal and efficient selections regarding the distribution of social useful resource.

This research aims to analyze GoPay user sentiments on the X social media platform (formerly known as Twitter) using the Naive Bayes Classifier algorithm. Sentiment analysis was conducted to understand user perceptions and satisfaction levels towards GoPay digital payment services based on their shared comments and reviews. Data was collected through a tweet crawling process containing the keyword "GoPay" within a specific period. The research stages included data preprocessing (case folding, tokenizing, filtering, and stemming), sentiment labeling (positive, negative), word weighting using TF-IDF, and classification using the Naive Bayes algorithm. The results showed that from a total of 1,431 analyzed tweets, 797 data contained positive sentiments, and 643 data contained negative sentiments. With a classification accuracy rate reaching 82.94%. The most frequently positively commented factors included ease of use and offered promotions, while the main complaints were related to technical issues and customer service. This research provides insights for GoPay developers to improve services according to user feedback.  

Currently, the use of data mining technology has become essential in enhancing business management efficiency, including in the trending coffee shop industry. Data mining allows business owners to analyze sales information in depth, enabling more accurate decision-making regarding inventory management, promotions, and sales strategies. This study aims to implement the Apriori algorithm to analyze sales data at Menrabic Coffee Shop. The Apriori algorithm is used to discover association patterns or relationships between products frequently purchased together by customers, which can assist management in providing inventory that aligns with customer preferences. The research method illustrates the detailed implementation process of the Apriori algorithm, starting from sales data collection, data cleaning, programming, and analysis of the results. The implementation uses web programming languages such as HTML, CSS, MySQL, and JavaScript, while back-end logic is programmed with PHP. The results of applying this algorithm reveal the most popular sales patterns among customers, providing valuable insights for management to improve operational performance and customer satisfaction. Therefore, this study demonstrates that applying data mining with the Apriori algorithm can be an effective tool for understanding consumer behavior and supporting data-driven decision-making at Menrabic Coffee Shop. By utilizing these insights, management can optimize inventory, enhance sales strategies, and ultimately increase overall business efficiency.

This study analyzes the effectiveness of implementing predictive maintenance (PdM) on the final drive components of the Komatsu PC200-8 unit at PT. Antareja Mahada Makmur, Site PT. Multi Harapan Utama, East Kalimantan, in an effort to reduce downtime and operational losses. Before the implementation of PdM in 2022, there were 12 repair cases for the final drive with a total downtime of 772.1 hours, repair costs amounting to IDR 310.6 million, rental income loss of IDR 208.03 million, and total losses of IDR 518.63 million. In 2023, during the PdM transition phase, the number of cases decreased to 4, with a total loss of IDR 252.05 million, although downtime remained high (714.6 hours) due to the limited scope of PdM implementation on certain units and components. In 2024, with full PdM implementation, the number of repair cases decreased to 5, with total downtime of only 96 hours and losses of IDR 45.75 million. The cost of PdM implementation for the year was only IDR 21.9 million. As of July 2025, no further damage to the final drive has been recorded, demonstrating a significant improvement in equipment reliability. The reduction in total losses from 2022 to 2024 amounted to IDR 472.88 million, indicating PdM’s effectiveness in avoiding significant costs through condition monitoring methods such as oil analysis, magnetic plug rating, thermal inspection, and oil leak testing (floating seal). The findings of this study confirm that PdM is effective in reducing downtime, repair costs, and enhancing asset management in the mining sector. It also improves equipment reliability and overall operational efficiency, proving PdM to be a successful strategy in reducing losses, increasing productivity, and supporting the sustainability of company operations.

According to the circular letter from the Head of the Inspectorate General (KaIT) regarding the review of mining accident cases in September 2024 and the review of mining accidents in the third quarter of 2024, the percentage of accidents occurring in workshops reached 16.13%. Over the past five years, the Plant Department of PT Saptaindra Sejati Jobsite Sera has experienced two major incidents classified as Lost Time Injury (LTI) resulting from working with lifting equipment on undercarriage components. The purpose of this study is to identify risks, analyze risk levels, and provide recommendations for risk control in the overhaul work of the PC210-10M0 undercarriage. This research applies the HIRADC method by identifying potential hazards through calculations of likelihood and severity levels to obtain the risk level using a risk matrix. Control measures are then carried out through administrative actions such as documentation and the use of personal protective equipment (PPE). The results of this study indicate a decrease in risk levels after implementing risk controls—from extreme risk to medium risk, and from high risk to low risk. Suggestions from this study include the need to develop updated HIRADC for each section, actively conduct socialization regarding Job Safety Analysis (JSA) before work, and perform inspections as well as observations related to work behavior.

Batang Bungo River faces severe pollution from domestic waste and illegal gold mining, which has led to an increase in skin diseases and diarrhea among residents of Tanjung Gedang, exacerbated by poor physical-chemical water quality, including low pH and high levels of Total Suspended Solids (TSS) and Chemical Oxygen Demand (COD), all of which foster the growth of pathogenic microorganisme. This study aimed to identify bacteria and fungi present in Batang Bungo River water, characterizing their colony morphology and microscopic structures to understand the impact of pollution on microbial communities. The methodology involved serial dilution of water samples, followed by inoculation onto Nutrient Agar (NA) media using the pour plate technique, and incubation at 37°C for 24-48 hours. Macroscopic observations of colonies (color, shape, texture) were performed, and representative colonies were stained with crystal violet for microscopic observation at 1000x magnification to identify cellular and hyphal structures. The results indicated the presence of various microorganisms, including Gram-positive bacteria, filamentous fungi, and possibly protozoa, with colonies exhibiting characteristics such as off-white color, rough surfaces, and irregular edges. Microscopic examination after crystal violet staining revealed rod-shaped (bacilli), spherical (cocci) structures, and branched filamentous structures resembling hyphae, consistent with a mixture of bacteria and filamentous fungi. The identification of pathogens like Clostridium, Dermatophilus, and Escherichia coli in previous studies, coupled with the poor water quality, confirms significant microbiological and chemical contamination. Crystal violet proved effective as a stain for microscopic identification of microorganism structures. In conclusion, the water quality of Batang Bungo River is highly concerning and requires serious attention for monitoring and management to safeguard public health and the river ecosystem.  

This study aims to utilize data mining techniques, specifically the K-Nearest Neighbors (KNN) algorithm, to classify leaf diseases in sugarcane (Saccharum officinarum). Early and accurate detection of leaf disease types is a crucial step in prevention and control strategies, thereby reducing potential crop losses caused by pathogen attacks. Leaf diseases in sugarcane, such as leaf scald, rust, and mosaic virus, are known to affect photosynthesis, inhibit growth, and reduce the quality and quantity of sugarcane produced. The classification process in this study was carried out through image analysis of infected sugarcane leaves, where features such as color, texture, and shape were extracted using digital image processing techniques. The KNN algorithm was chosen because of its non-parametric nature, ease of implementation, and its ability to provide accurate classification results even with limited data size. The working principle of KNN is to determine the class of a new sample based on the majority class of its k nearest neighbors in the feature space, making it very suitable for the case of leaf disease image classification. In addition to building a classification model, this study also examines disease prevention strategies based on the identification results. These strategies include the use of disease-resistant sugarcane varieties, the implementation of appropriate planting patterns, land moisture management, regular plantation sanitation, and the measured and environmentally friendly use of pesticides or fungicides. Model performance evaluation was conducted using accuracy, precision, recall, and F1-score metrics to assess model effectiveness across various data scenarios. The results of this study are expected to not only contribute to the development of decision support systems for farmers and related parties but also support the application of artificial intelligence-based technology in the agricultural sector.

This study aims to explain and prove the hypothesis regarding the influence of investment decisions, financing decisions, and dividend policies on firm value in coal sub-sector companies listed on the Indonesia Stock Exchange (IDX) during the 2021–2023 period. This study used a quantitative approach with a purposive sampling method, resulting in 10 companies as research samples. Data analysis was conducted through classical assumption tests to ensure the fulfillment of regression analysis requirements, followed by hypothesis testing using multiple regression analysis. Data processing was carried out using E-Views software version 13. The results showed that partially, investment decisions have a positive and significant effect on firm value, with a probability value of 0.0000, which is smaller than the 0.05 significance level. This finding indicates that the more appropriate a company's investment decisions are, the higher the company's value is reflected in its stock performance in the capital market. Conversely, the financing decision variable does not have a significant effect on firm value, with a probability value of 0.3796, which is greater than 0.05. This indicates that the funding structure, whether derived from equity or debt, did not directly affect firm value during the study period. Similarly, the dividend policy variable did not significantly influence firm value, with a probability value of 0.7493 > 0.05. This means that the amount of dividends distributed was not a determining factor in firm value in the sample studied. However, simultaneously, all three independent variables—investment decisions, financing decisions, and dividend policy—were shown to have a significant effect on firm value, with a probability value (F-statistic) of 0.0000 < 0.05. This confirms that the combination of these three factors collectively contributes to changes in firm value in the coal sub-sector.

Electricity has emerged as an essential requirement in modern life. As demand escalates, electricity costs rise, making wastefulness a drain on financial resources. Consequently, forecasting electricity usage can enhance our management of consumption. This study presents an IoT-based monitoring and forecasting system for electricity consumption. The system comprises two NodeMCU micro-controllers, a PZEM-004T sensor for collecting real-time power data, and three relays that regulate the current flow to three distinct electrical appliances. The data gathered is transmitted to a web application utilizing the k-Nearest Neighbor (k-NN) algorithm to forecast future electricity usage based on historical patterns. We evaluated the system's performance using four weeks of electricity consumption data. The results indicated that predictions were most accurate when the user’s daily consumption pattern remained stable, achieving a Mean Absolute Error (MAE) of approximately 1 watt and a Mean Absolute Percentage Error (MAPE) ranging from 1% to 1.7%. Additionally, predictions were notably precise during the early morning hours (3:00 AM to 8:00 AM) when k=6 was employed. This study demonstrates the effectiveness of integrating IoT-based systems with machine learning for real-time energy monitoring and forecasting. Furthermore, it emphasizes the application of data mining techniques within embedded IoT environments, providing valuable insights into the implementation of lightweight machine learning for smart energy systems.

Silicotuberculosis is a complex lung disease, a combination of silicosis and tuberculosis (TB). Silicosis is a disease caused by the inhalation of silica particles, which can lead to pulmonary fibrosis, while TB is an infectious disease caused by Mycobacterium tuberculosis. Long-term exposure to silica dust can cause silicosis and also increase the risk of TB infection, especially in countries with a high TB burden. Workers exposed to silica dust in the mining, construction, and manufacturing industries are among the groups most at risk. The diagnosis of silicotuberculosis is often difficult because the clinical and radiological symptoms of the two diseases often overlap. Symptoms, such as chronic cough, shortness of breath, and chest pain, can be very similar in silicosis and TB, often delaying a correct diagnosis. The pathophysiology of silicotuberculosis involves impaired function of macrophages, immune cells that play a role in fighting infection, and a compromised immune response due to silica exposure. These disruptions facilitate the progression of TB infection, further worsening the patient's health. Primary management of silicotuberculosis includes controlling TB infection with standard anti-tuberculosis drug therapy (OTT) and preventing silica exposure. Preventing occupational exposure to silica dust is crucial to reducing the risk of developing the disease. The prognosis of the disease is greatly influenced by the severity of pulmonary fibrosis and delay in diagnosis. The more severe the fibrosis, the worse the prognosis. Therefore, preventing silica dust exposure, along with routine TB screening for high-risk workers, is crucial to reducing the incidence of silicotuberculosis. Furthermore, education about the risks of the disease is crucial to raise awareness among workers and the general public.

This study aims to analyze the correlation between learning models and student achievement at SMA Negeri 1 Kuala by applying the Apriori algorithm in data mining, using Rapid Miner software as the primary tool for analysis. The research is motivated by the shift in educational approaches from conventional teacher-centered methods toward more innovative strategies such as project-based learning and cooperative learning, which are expected to foster higher levels of student engagement and improve academic outcomes. In many schools, particularly at the secondary level, the choice of learning model, availability of facilities, and attendance rates are crucial factors that shape learning effectiveness and student performance. The data collected in this study include student grades, the types of learning models implemented, school facility conditions, and attendance rates for the 2023/2024 academic year, covering a total of 680 students. The Apriori algorithm was employed to discover hidden patterns and associations among these variables, enabling the identification of relationships between learning factors and academic achievement. By applying Rapid Miner software, the research systematically generated association rules that reflect meaningful correlations in the dataset. The results indicated that the use of the Indonesian language subject in combination with a cooperative learning model, adequate and complete school facilities, and good student attendance was strongly associated with the attainment of an A grade. This finding was supported by a support level of 53.33% and a confidence level of 100%, suggesting a robust and reliable relationship between these factors. The implementation of data mining techniques through Rapid Miner not only allowed for efficient data processing but also provided practical recommendations for educators and school administrators in designing effective instructional strategies.

This research aims to develop a recommendation system that can help retail business owners design more effective, data-driven promotional strategies. This system utilizes data mining techniques and the Apriori algorithm to extract association rules from consumer transaction data, thereby identifying more specific and accurate consumer purchasing patterns. Based on these patterns, the system can provide relevant promotional recommendations, such as product bundling, buy-one-get-one offers, or special discounts, which can attract consumer interest and increase sales. The system's implementation process is presented in the form of an interactive dashboard, which allows business owners to upload their transaction data, adjust analysis parameters, and visualize the promotional recommendation results in a way that is easier to understand and can be directly applied to their marketing strategies. This system not only provides well-structured promotional recommendations but also enables retail business owners to make more informed and efficient decisions in determining the type of promotion to implement, based on insights gained from analyzing their own transaction data. By utilizing this system, business owners can optimize their promotional strategies more efficiently and effectively, because they can quickly identify promotions that best suit consumer purchasing patterns. This can increase impulse sales, as relevant promotions will encourage consumers to purchase more products. Furthermore, this system shows great potential in increasing consumer engagement, as the promotions provided are more personalized and tailored to each consumer's preferences. Therefore, the implementation of this recommendation system has the potential to drive significant sales growth and help retail business owners achieve greater profits, as well as accelerate their business decision-making process. This system, ultimately, not only benefits business owners but also enhances the consumer shopping experience with promotions that are more tailored to their needs and preferences.

The exploitation of natural resources through mining projects in Indonesia often has an impact on the lives of residents, both socially, economically, and environmentally. Although the government has established various regulations to protect the rights of affected communities, the implementation of this legal protection still faces various challenges. In this context, this research aims to analyze legal protection for residents in mining projects in Indonesia and examine the effectiveness of regulations that have been implemented. This research uses a normative juridical method using both a statutory and a conceptual approach. The former involves examining multiple legal provisions that govern mining and community protection, including Law Number 4 of 2009 concerning Mineral and Coal Mining, Law Number 32 of 2009 concerning Environmental Protection and Management, and various derivative regulations. The latter involves investigating legal theories that are pertinent to the defense of residents' rights, including the notion of sustainable development, the right to a healthy environment, and the rights of indigenous peoples to land and natural resources. This research does not involve case studies or interviews, but focuses on a normative study of the applicable legal system. Through an analysis of national and international legal instruments, this research is expected to provide academic contributions in identifying weaknesses in existing regulations and providing recommendations for policy makers in improving legal protection for residents affected by mining projects. Thus, this research can be a basis for strengthening more effective legal protection in maintaining a balance between the exploitation of natural resources and the rights of local communities.

The issue of determining the number of students' graduation times is one of the important indicators in transmitting the quality and effectiveness of the higher education process in universities. The rate of on-time graduation not only impacts accredited institutions, but also becomes a concern for campus management in designing learning strategies and academic guidance. This study aims to apply and compare two classification algorithms in data mining, namely C4.5 and K-Nearest Neighbor KNN, in predicting the accuracy of students' graduation times. Predictions are made based on academic attributes such as Grade Point Average GPA, number of credits that have been achieved, and Semester Grade Point Average IPS as input variables. The method used in this study is Knowledge Discovery in Database KDD which includes data selection, preprocessing, transformation, data mining, and evaluation of results. The study was conducted using the RapidMiner tool, with a dataset of 279 Informatics Study Program students from the 2015 to 2019 intake. The data was classified into two categories: "graduated on time" and "not graduated on time". The test results showed that the KNN algorithm provided better performance compared to C4.5. KNN produced an accuracy of 76.08%, with a precision of 73.11% and a recall of 41.92%. Meanwhile, the C4.5 algorithm produced an accuracy of 73.49%, with a precision of 64.62% and a recall of 41.89%. This difference in accuracy indicates that KNN is more effective in capturing patterns in the data and providing more accurate predictions in this context. Thus, the KNN algorithm can be considered a more optimal method to assist universities in predicting potential student admissions in a timely manner, thus enabling early intervention for students at risk of late graduation. This research also contributes to the development of data mining-based academic decision support systems in higher education.

Class C sand mining activities in Nagari Aia Dingin, Lembah Gumanti District, Solok Regency have been ongoing since 1999 and continue to expand to this day. This mining provides economic contributions to the local community, but on the other hand, it also has a significant impact on the environment, particularly on land cover changes. This study aims to analyze changes in open land caused by sand mining activities, using remote sensing technology as a monitoring tool. The approach used is descriptive quantitative, through the interpretation of Google Earth satellite imagery in 2015 and 2018 and Sentinel-2 imagery in 2024. Spatial analysis was conducted with the help of ArcGIS software to obtain a visual and numerical picture of land cover changes. The results of the study indicate a significant increase in the area of sand mining from 2015 to 2024. In 2015, the mining area was recorded at 8.72 hectares, and increased to 22.14 hectares in 2024. This indicates an increase in mining land area of 13.42 hectares over a nine-year period. Land use conversion has occurred on a massive scale, from dryland forest, scrubland, and dryland areas to open-pit mining areas. This land cover change has the potential to cause environmental degradation such as erosion, reduced biodiversity, and disruption to regional water systems. These findings underscore the importance of stricter monitoring and sustainable spatial planning in natural resource management. The use of remote sensing technology has proven effective in monitoring the dynamics of land use change and can serve as a basis for formulating environmental policies that are more responsive to the impacts of mining activities.

Diabetes Mellitus merupakan salah satu penyakit kronis yang jumlah penderitanya terus bertambah setiap tahunnya, termasuk di wilayah Puskesmas Brebes. Banyaknya pasien dengan kondisi klinis yang beragam mendorong perlunya suatu metode untuk mengelompokkan pasien berdasarkan tingkat keparahannya. Penelitian ini bertujuan untuk menerapkan algoritma K-Means dalam proses pengelompokan pasien Diabetes Mellitus dengan menggunakan beberapa parameter klinis, yaitu Gula Darah Puasa (GDP), kadar HbA1c, Kolesterol Total (CHOL), serta tekanan darah sistolik dan diastolik. Pendekatan yang digunakan dalam penelitian ini adalah deskriptif kuantitatif dengan metode data mining berbasis algoritma K-Means. Data yang digunakan diperoleh dari rekam medis Puskesmas Brebes. Proses klasterisasi menghasilkan tiga kelompok, yaitu kategori risiko rendah, sedang, dan tinggi. Hasil penelitian menunjukkan bahwa algoritma K-Means mampu melakukan pengelompokan data pasien secara akurat sesuai tingkat keparahan. Hasil tersebut kemudian divisualisasikan melalui sistem berbasis web yang bertujuan untuk mempermudah pihak puskesmas dalam menganalisis kondisi pasien serta mendukung pengambilan keputusan medis yang lebih efektif.

This study was conducted with the aim of evaluating the extent to which sand quality influences consumer purchasing decisions in the sand mining area located in Kalicilik Hamlet, Candirejo Village, Blitar Regency. The background of this research is based on the fact that in the construction and development sector, the quality of building materials is a very crucial factor. Sand, as one of the main components in construction, plays a vital role in determining the final outcome of a construction project. Therefore, the quality of the sand used is a primary consideration for consumers in choosing a location or sand seller. This study applies a quantitative approach using a survey method as a data collection technique. Respondents in this study consisted of 74 consumers who actively make purchases at the sand mining location. Data were obtained through the distribution of questionnaires containing questions related to consumer perceptions of sand quality and their decisions in making purchases. The collected data were then analyzed using simple linear regression analysis techniques run with the help of SPSS software. The results of the analysis indicate a highly significant relationship between sand quality and consumer purchasing decisions. This is demonstrated by the coefficient of determination (R²) of 0.540, meaning that 54% of the variation in consumer purchasing decisions can be explained by sand quality. Furthermore, the significance value of 0.000 strengthens this finding, indicating that the relationship is not a coincidence. Therefore, this study concludes that the better the quality of sand offered by the mining company, the higher the likelihood of consumers deciding to purchase. The implication of these results is that businesses in the sand mining sector need to pay special attention to the quality of the products they offer.

Kecelakaan lalu lintas di Kabupaten Brebes merupakan masalah kritis karena tingginya frekuensi insiden yang terjadi di wilayah tersebut. Penelitian ini bertujuan untuk menentukan area yang rentan terhadap kecelakaan dengan menggunakan algoritma K-Means Clustering , yang mendukung proses pengambilan keputusan berbasis data. Isu utama yang dieksplorasi dalam penelitian ini adalah bagaimana algoritma K-Means dapat diimplementasikan untuk mengelompokkan zona rawan kecelakaan dan meningkatkan kesadaran masyarakat terhadap keselamatan jalan. Metodologi yang digunakan meliputi pengumpulan data melalui tinjauan pustaka, observasi langsung, dan wawancara, yang dilanjutkan dengan penggunaan algoritma K-Means untuk mengklasifikasikan data kecelakaan berdasarkan jumlah kejadian, korban jiwa, dan cedera. Temuan menunjukkan bahwa algoritma K-Means secara efektif mengelompokkan lokasi rawan kecelakaan ke dalam tiga tingkat risiko yang berbeda: tinggi, sedang, dan rendah. Dengan demikian, informasi yang terklasifikasi ini dapat membantu otoritas terkait dalam meningkatkan langkah-langkah keselamatan lalu lintas dan mengedukasi masyarakat tentang area berisiko tinggi. Hasil penelitian ini diharapkan dapat berkontribusi pada pengembangan kebijakan keselamatan lalu lintas yang lebih terinformasi dan strategis di Kabupaten Brebes.