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Traffic violations are one of the serious problems frequently occurring in various regions, including Binjai City. Various types of violations, such as disobeying road signs and markings, incomplete vehicle documents, and violations that threaten the safety of drivers and other road users, continue to increase despite preventive and repressive efforts carried out by the authorities. This condition indicates that handling traffic violations cannot rely solely on field enforcement but also requires the support of technology capable of analyzing data more comprehensively. This study aims to predict the level of traffic violations by applying the Naïve Bayes method through data mining techniques. The dataset used consists of traffic violation records in 2023 from the Binjai City Police Department, with the main variables including violations of traffic signs and markings, document completeness, and safety-related violations. The Naïve Bayes method was selected because of its ability to perform classification with good accuracy, simplicity, and efficiency in processing large amounts of data. The implementation of this research is realized by developing a web-based application using Visual Studio Code as the development environment and MySQL as the database system. The results of this study are expected to provide structured information regarding traffic violation patterns, support authorities in making more effective decisions, and serve as an alternative solution in the prevention and handling of traffic violations in Binjai City.

Mining activities in Mandailing Natal Regency, particularly in Kota Nopan, Huta Bargot, and Batang Natal Districts, play a significant role in the local economy while simultaneously generating complex legal, environmental, and social problems. Despite the existence of comprehensive national regulations such as the Mineral and Coal Mining Law and regional bylaws, enforcement remains weak, as indicated by the persistence of illegal mining (PETI), environmental degradation, and conflicts of interest among various stakeholders. This study aims to analyze the dynamics of mining law enforcement by employing a juridical-empirical approach that combines the review of statutory regulations, secondary data, and field observations in three sample districts. The findings reveal that law enforcement is far from optimal due to structural constraints, including limited institutional capacity and inconsistent implementation, as well as cultural and economic factors, such as the community’s dependence on mining as a primary livelihood. Moreover, political and economic interests often weaken oversight and create selective enforcement, further widening the gap between regulation and practice. The study emphasizes that law enforcement in the mining sector should not rely solely on repressive measures but must be integrated with community empowerment, sustainable livelihood alternatives, and the strengthening of institutional governance. Therefore, reforming regional regulations, improving inter-agency coordination, enhancing law enforcement capacity, and ensuring active community participation are crucial steps to build legal, fair, and sustainable mining governance in Mandailing Natal.

Sleep is a fundamental human need that plays a crucial role in maintaining both physical and mental health. Poor sleep quality can trigger a variety of health problems, ranging from decreased concentration to an increased risk of chronic diseases. The complexity of factors influencing sleep quality—such as stress levels, heart rate, blood pressure, physical activity, and lifestyle—makes its assessment difficult through direct observation alone. Therefore, data mining approaches are increasingly utilized to identify relevant patterns in sleep-related data. This study aims to compare the performance of the C4.5 (Decision Tree) algorithm and the Naïve Bayes algorithm in predicting sleep quality using the Sleep Health and Lifestyle dataset, which contains information from 374 respondents. The research method applied is a quantitative comparative approach employing classification techniques with 10-fold cross-validation to ensure robust evaluation. Model performance is assessed using accuracy, precision, and recall metrics to provide a comprehensive understanding of the effectiveness of each algorithm. The findings indicate that the C4.5 algorithm achieves an accuracy of 96.26% and offers advantages in terms of interpretability through its decision tree visualization, enabling easier understanding of variable relationships. In contrast, the Naïve Bayes algorithm demonstrates superior predictive performance, achieving an accuracy of 98.66% along with consistently high precision and recall across nearly all classes. These results suggest that Naïve Bayes is more effective for predictive tasks involving sleep quality, while C4.5 remains highly valuable when the goal is to interpret variable interactions and decision rules. Overall, this research highlights the potential of data mining techniques in health informatics, particularly in improving the understanding and prediction of sleep quality, which in turn can contribute to better prevention and management of sleep-related health issues.

Shadow puppet craft training is a strategic intervention in preserving cultural heritage and strengthening the creative economy sector in Indonesia. To ensure the effectiveness and efficiency of training, a planning approach is needed that is not only conventional, but also based on quantitative analysis and intelligent systems. This community service proposes a training planning strategy using an interdisciplinary approach involving Operation Research, Design of Experiment (DoE), Simulation, Metaheuristic Algorithms, and Data Mining. This study begins with the identification of key training variables, such as duration, number of participants, initial competency level, teaching materials, and instructor resources. Through the DoE approach, various combinations of variables are systematically tested to identify the optimal training design. Next, Simulation is used to model the dynamics of training implementation and evaluate implementation scenarios. To predict training needs and participant behavior, Data Mining techniques are applied to historical data of arts community training. In the final stage, Metaheuristic algorithms such as Genetic Algorithm and Simulated Annealing are used to solve complex and large-scale scheduling and resource allocation problems. The results of the integration of these approaches show an increase in training efficiency of up to 27% as well as increased participant satisfaction and the quality of work results. This activity demonstrates that applying a quantitative, data-driven approach to traditional crafts training planning can provide significant added value. This model can be replicated in other training programs based on local wisdom and other creative industry sectors.

Stock returns represent a crucial parameter that serves as a reference for investors in evaluating company performance. A decline in returns has occurred in several mining companies listed on the IDX, despite the sector’s vital role in the national economy. This study aims to examine the effect of Corporate Social Responsibility (CSR), Return on Assets (ROA), Return on Equity (ROE), Debt to Equity Ratio (DER), and Firm Size on the stock returns of mining companies listed on the IDX during the 2022–2024 period. The sample was determined using purposive sampling, resulting in 56 observational data after outliers were removed. To meet the assumptions of classical tests, several variables were transformed using natural logarithms, and data were analyzed using multiple linear regression. The results indicate that CSR, ROE, and Firm Size have no significant effect on stock returns, whereas ROA and DER show a significant positive effect. These findings suggest that investors tend to emphasize financial fundamentals, particularly profitability and capital structure, rather than non-financial aspects such as CSR activities. The implication for companies is the need to enhance operational efficiency and optimize financial structures to attract investors and improve returns. Future researchers are encouraged to incorporate external variables such as global commodity prices, market risk, and macroeconomic indicators, as well as expand the observation period and apply more diverse methodological approaches to provide a more comprehensive understanding of stock return dynamics in the mining sector.

Pengelolaan persediaan pada bisnis penjualan suku cadang motor sering menghadapi tantangan ketidakseimbangan stok akibat kurangnya pemahaman terhadap pola pembelian konsumen. CV FOLC MORA, sebagai salah satu pengecer sparepart di Palembang, mengalami ketidakefisienan dalam pengendalian stok yang dapat mengakibatkan biaya penyimpanan berlebih atau hilangnya peluang penjualan ketika barang populer tidak tersedia. Kebaruan dari penelitian ini terletak pada konteks penerapan algoritma Apriori yang difokuskan pada skala usaha mikro, kecil, dan menengah (UMKM) penjualan suku cadang motor, yang relatif jarang dieksplorasi pada penelitian terdahulu yang umumnya menitikberatkan pada e-commerce berskala besar. Selain itu, penelitian ini tidak hanya menghasilkan analisis pola pembelian, tetapi juga mengintegrasikan hasil tersebut ke dalam dashboard interaktif berbasis Streamlit yang memudahkan pelaku UMKM dalam melakukan pemantauan stok dan perencanaan pembelian. Kombinasi konteks UMKM dan pemanfaatan dashboard analitik inilah yang menjadi nilai kebaruan sekaligus kontribusi praktis penelitian ini. Penelitian ini bertujuan untuk menganalisis pola pembelian menggunakan algoritma Apriori guna mendukung pengambilan keputusan dalam optimalisasi stok dan peningkatan penjualan. Metode yang digunakan adalah CRISP-DM (Cross-Industry Standard Process for Data Mining) yang terdiri dari enam tahapan: pemahaman bisnis, pemahaman data, persiapan data, pemodelan, evaluasi, dan implementasi. Data transaksi penjualan yang dianalisis berasal dari periode Januari hingga April 2025, dengan total 1.550 transaksi dan 6.478 data item. Pengolahan data dilakukan menggunakan Python dengan bantuan pustaka Pandas dan MLxtend, sedangkan hasil analisis disajikan melalui dashboard interaktif berbasis Streamlit. Algoritma Apriori menghasilkan keterkaitan kuat antar produk, seperti “Ban Luar Swallow 80/90-17” dan “Velg TK Excel Rim” dengan nilai support 1%, confidence 76%, dan lift 2,15. Temuan ini menjadi dasar penyusunan strategi bundling produk dan keputusan restok barang. Dashboard yang dikembangkan memudahkan pemilik usaha untuk mengunggah data transaksi, mengatur parameter analisis, serta melihat frequent itemset dan rekomendasi kombinasi produk. Pendekatan ini meningkatkan efisiensi pengelolaan stok, mendukung strategi pemasaran berbasis data, serta berkontribusi pada peningkatan kepuasan pelanggan dan kinerja penjualan.

The Computer-Based National Assessment (ANBK) is an essential instrument designed to comprehensively measure student competence, including literacy, numeracy, and character aspects. However, in practice, many students still face various challenges during preparation, such as cognitive limitations, psychological readiness, and technical barriers, which affect their overall readiness to participate in ANBK. This study aims to analyze the readiness level of students at SMP Negeri 2 Kuala by employing the Rough Set method. The variables examined include digital literacy, subject matter understanding, psychological readiness, and school facility support. Data were collected from 250 ninth-grade students through structured questionnaires and subsequently processed using the Rosetta software to perform attribute reduction and generate decision rules. The findings indicate that digital literacy, subject matter understanding, and psychological readiness are the most influential variables in determining student readiness, while facility support serves only as a complementary factor. The extraction process generated seven decision rules with an accuracy level of 100%, which effectively classified students into three readiness categories: highly ready, ready, and less ready. These results confirm that the Rough Set method is highly effective for identifying dominant factors and producing decision rules that can guide schools in developing targeted strategies to enhance student readiness for ANBK.

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.

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.

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.

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.

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.  

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.

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.

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.

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 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.

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.