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Transaction logs in online retail provide opportunities for data-driven customer segmentation. This study segments customers at two scopes global (all countries) and United Kingdom (UK) using Recency, Frequency, and Monetary (RFM) features derived from the Online Retail transaction dataset. After cleaning cancellations and invalid records, RFM variables are computed per customer and normalized. K-Means clustering is applied separately for global and UK data, while the number of clusters is selected via the elbow criterion and validated using internal indices. The best configuration for both scopes yields five clusters, with moderate separation quality based on the silhouette score. Cluster profiling indicates distinct groups ranging from low-frequency low-spending customers to highly frequent high-spending customers. The comparison between global and UK segmentation shows similar structural patterns, yet different proportions across segments, supporting targeted retention and value-driven marketing actions.

The increasing use of Learning Management Systems (LMS) in higher education generates large amounts of student activity data that have the potential to provide deeper insights into learning processes. However, in practice, these data are still rarely analyzed systematically to understand variations in students’ learning activity patterns, limiting their practical use in supporting teaching and learning. This study aims to explore students’ learning activity patterns in an LMS using a clustering approach based on activity data.This research utilizes the publicly available Open University Learning Analytics Dataset (OULAD), focusing on a single course and a single academic term. LMS activity data were processed through data cleaning and feature extraction, followed by student clustering using the K-Means algorithm. The quality of the clustering results was evaluated using the Silhouette Score, and visual analysis was applied to support the interpretation of the results.The results indicate that students’ learning activities can be grouped into two main patterns, namely a group of students with high learning activity and a group with lower or moderate activity levels. These findings highlight the existence of heterogeneous learning behaviors among students, even within the same learning context.The identified learning activity patterns provide an initial foundation for utilizing LMS data to monitor student engagement and to support the development of more responsive, data-driven learning approaches in higher education.

The disparity in the quality of rehabilitation services across regional work units presents a significant challenge to effective public management. This study aims to bridge the gap between problem diagnosis and policy prediction by proposing a hybrid, data-driven approach. We integrate K-Means Clustering to map the current state of service quality and Stochastic Simulation to predict the impact of strategic interventions. Using the 2024 Public Satisfaction Index (IKM) dataset from the National Narcotics Agency (BNN), the K-Means algorithm initially identified 26 work units (15.7%) in the "Red Zone" (critical performance), highlighting urgent areas for improvement. Next, a stochastic simulation modeling a "Directed Priority Intervention" scenario was run. The results predicted a significant structural shift in the distribution of service quality, characterized by an 80.8% decrease in critical units (down to 5 units) and a 71.8% increase in excellent performing units (up to 67 units). These findings validate that the integration of clustering and simulation provides a comprehensive framework for evidence-based decision-making, enabling policymakers to optimize resource allocation and efficiently accelerate national service standardization.

Elementary education is the fundamental stage in shaping students’ character, attitudes, and learning motivation. Learning interest plays a vital role in determining students’ success in understanding and mastering the lessons. However, differences in background, abilities, and learning styles often cause significant variations in students’ interest. Therefore, it is necessary to apply an analytical method that can group students based on their level of learning interest so that teachers can provide appropriate learning strategies. This study aims to implement the K-Means Clustering algorithm to identify the learning interest of students at Sekolah Dasar Negeri Puu Naga. The research method used is a quantitative approach with data collected through questionnaires consisting of several indicators of learning interest, such as perseverance in completing assignments, enthusiasm during lessons, attention to teacher explanations, and participation in class activities. The collected data were then analyzed using the K-Means algorithm to form several clusters of learning interest. The data processing stages included determining the number of clusters, selecting the initial centroid, calculating the distance of data to the centroid, grouping data, and iterating until a stable clustering result was achieved. The results of the study show that the K-Means algorithm successfully grouped students into three main categories, namely high, medium, and low learning interest. Students in the high-interest group consistently demonstrated active learning behavior and strong intrinsic motivation, while those in the medium group showed fluctuating interest influenced by external factors such as the learning environment and teaching methods. Meanwhile, students in the low-interest group displayed a lack of attention and motivation, thus requiring special interventions. These findings provide valuable insights for the school, especially teachers, in designing adaptive and personalized teaching strategies. In conclusion, the application of the K-Means algorithm is proven effective as an analytical tool to identify students’ learning interest.

This study discusses the application of the K-Means Clustering algorithm in the car rental ordering system. The objective is to help group booking data based on certain patterns such as car type, booking frequency, and rental duration. The clustering results are expected to improve service efficiency and help companies better understand customer preferences. The research was conducted using historical car rental booking data from a rental company. The results show that the K-Means method can successfully cluster booking data into several useful clusters for business decision-making. This extended paper also explores theoretical concepts of clustering, related studies, limitations of the method, and potential future enhancements such as integrating predictive analytics. It highlights the importance of transforming large volumes of raw booking data into actionable business intelligence to support marketing strategies, fleet management, and customer segmentation.

Discipline is a crucial aspect of education that plays a significant role in shaping students’ character and sense of responsibility. However, the manual discipline assessment process at SDIP Baitul Maal presents several issues, such as inaccurate data, limited analysis capabilities, and difficulty for teachers and parents to monitor students effectively. With the advancement of information technology, digital systems offer a potential solution to improve the efficiency and objectivity of the evaluation process. This study aims to design and develop a web-based application for assessing student discipline by implementing the K-Means Clustering method optimized with the Elbow method. The system is designed to cluster students based on numerical data such as attendance, tardiness, neatness, and rule violations, allowing for more accurate classification of discipline levels. The results show that the system successfully groups students into clusters automatically and provides informative visualizations of the outcomes. Additionally, the system facilitates real-time monitoring and evaluation by school staff and parents through a user-friendly interface. Therefore, the application of the K-Means Elbow method proves effective in supporting decision-making within the educational environment. This research is expected to contribute to the digital transformation of school management and enhance the quality of student character development.

Pulmonary tuberculosis remains a pressing public health problem, particularly in the work area of the Duduk Health Center (UPT Puskesmas). Effective management of this disease requires a thorough understanding of the characteristics of the causes of pulmonary TB in patients. This study aims to classify pulmonary TB cases based on the main causes such as diabetes mellitus, irritant factors, pleural effusion, and family environmental conditions. The research method used is a clustering technique with the K-Means algorithm. The data used are data on pulmonary TB patients in 2020–2025 with variables of age, gender, and causative factors collected from medical records. The analysis process was carried out using MATLAB R2014b software. The clustering model was carried out in 3, 4, and 5 clusters to compare the level of segmentation efficiency. Based on the calculation results, the model with 5 clusters showed the lowest cluster variance value of 0.4889 compared to the 3-cluster model (0.7333) and 4-cluster models (0.6151), which indicates that the division into 5 clusters produces the most compact and representative data group. Each cluster shows a different combination of characteristics of pulmonary TB patients, for example: (1) elderly male patients with comorbid diabetes; (2) adolescent females with the negative influence of environmental factors; (3) adult males exposed to irritants; (4) patients with pleural effusion; and (5) groups with multiple factors. The results of this study can provide strategic input for the Finished Community Health Center UPT in formulating more targeted and targeted intervention policies in order to prevent, control, and handle pulmonary tuberculosis cases in a sustainable and effective manner.

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.

Terrestrial digital television transmitter stations are the main facilities in the transmission of digital television broadcasts to the public. The quality of the transmitted signal is expected to reach the Central Java-1 service area well so as to provide optimal and reliable quality of digital television broadcast performance according to the needs of the community, but currently, complaints about signal problems such as service coverage and reception quality still occur a lot, coverage and signal quality received by community-owned television transmitters cannot be separated from the influence of the quality performance of digital television transmission stations. The purpose of this research is to analyse the coverage performance of digital television services of digital television transmitter stations using the K-Means Clustering Method, identify areas with the best signal coverage and group areas based on the level of signal performance. The data used includes field strength parameters collected through field measurements at 25 service area location points, topography factors and transmitter distance were found to be the main causes of signal quality differences. Data analysis shows that the K-Means Clustering method effectively clusters areas with signal reception quality categories of very good cluster 3 areas, good cluster 8 areas, fair cluster 5 areas and poor cluster 9 areas. The results of this study can be recommended in the evaluation and optimisation of tele-transmitting station networks.

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.

The study presents a comprehensive framework for optimizing customer retention budget by integrating clustering, classification, and mathematical optimization techniques. The study begins with the IBM Telco dataset, which is prepared through data cleansing, encoding, and scaling.  In the preliminary phase, customer segmentation is performed using K-Means clustering, with k = 3 and k = 4 identified as optimal based on the elbow method and Silhouette score. The configurations produced three (Premium, Standard, Low) and four (Premium, Standard Plus, Standard, Low) customer segments based on purchase preferences, which served as input features for churn prediction. In the second phase, the dataset was divided into training and test sets in an 80:20 ratio, followed by data balancing using the Synthetic Minority Over-sampling Technique (SMOTE) and Edited Nearest Neighbors (ENN). Multiple classification algorithms were evaluated, including Naive Bayes (NB), Random Forest (RF), Categorical Boosting (CatBoost), Light Gradient Boosting Machine (LightGBM), Extreme Gradient Boosting (XGBoost), Gradient Boosting (GB), Support Vector Machine (SVM), Logistic Regression (LR), K-Nearest Neighbors (KNN), and Multi-Layer Perceptron (MLP) using F1-score as the performance metric. CatBoost and LightGBM, with k values of 3 and 4, respectively, were the highest-performing classification models, with only minimal differences in performance.    Ultimately, customer segmentation established customer prioritization, whereas churn prediction assessed customer churn likelihood. Four distinct configurations were assessed utilizing mixed-integer linear programming (MILP) to optimise retention budget allocation within uniform budget constraints, discount amounts, and churn thresholds. In both the k=3 and k=4 scenarios, CatBoost surpassed LightGBM, with CatBoost at K=3 effectively discounting 66% of at-risk consumers across all three segments, hence improving the intervention's efficacy and budget allocation, making it the ideal choice for maximizing customer retention. The results demonstrate the importance of segmentation in enhancing retention budgeting and budget optimization, particularly concerning parameter sensitivity.

Crime is a social issue that continues to evolve alongside increasing community activity and regional development. This study aims to Cluster crime data in Binjai City based on the location of incidents using the K-Means algorithm and the Cross Industry Standard Process for Data Mining (CRISP-DM) approach. The data were obtained from the Binjai Police Department, with attributes including the type of crime, time of occurrence, and location, categorized by district. A comprehensive data preprocessing stage was carried out, involving the extraction of information from raw data, normalization of crime type labels, and conversion of categorical data into numerical form using label encoding. The optimal number of Clusters was determined using the Silhouette score method, which yielded the best result at K = 10. The Clustering results were further evaluated using the Davies-Bouldin Index (DBI) to ensure Cluster quality. The analysis revealed that Binjai Utara District has the highest number of crimes, particularly aggravated theft (curat), which frequently occurs from early morning to late morning. This Clustering is expected to provide valuable insights for authorities in formulating more targeted and data-driven regional security strategies.

This study aims to conduct customer segmentation and develop a classification model to predict the clusters of new customers at Monex Toys Abadi Bekasi, a micro, small, and medium enterprise (MSME). Segmentation was performed using the K-Means Clustering algorithm, incorporating parameters such as Recency, Frequency, Monetary (RFM), purchased products, payment methods, shipping cost discounts, and the total number of products purchased by customers. The segmentation results revealed two clusters: (1) Discount Hunters and (2) Loyal Customers. Subsequently, a classification process was conducted to predict customer clusters using the K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) algorithms. Evaluation results indicated that all models achieved high accuracy exceeding 98%. The best-performing model was obtained with SVM using a 70:30 data split, achieving an accuracy of 98.81%. This classification model was then implemented into a Streamlit-based cluster prediction application, enabling users to identify customer segments in real-time. The findings of this research are expected to assist MSMEs in understanding customer behavior, enhancing service quality, and supporting more effective marketing strategies.

Public transportation, with Transjakarta as its main pillar, requires a deep understanding of customer behavior to improve service quality and maintain loyalty. This study aims to segment Transjakarta customers using data mining techniques, specifically the K-Means Clustering algorithm, based on the RFM (Recency, Frequency, Monetary/Value) behavioral model. 37,900 rows of raw transaction data were processed into a clean database, resulting in 1,917 unique customers for analysis. The RFM metrics were then normalized using Min-Max Scaler. The optimal number of clusters was evaluated using the Elbow Curve and Silhouette Score Methods, which led to the determination of k = 4 clusters. The segmentation results identified four customer groups requiring specific strategies: Cluster 3 (Champions) with high R, F, and V (requiring rewards and retention); Cluster 0 (Active, Low Value) with high R and F but low V (requiring upsells and cross-sells); Cluster 1 (Potential/At-Risk); and Cluster 2 (Dormant/Lost). Preliminary analysis (EDA) showed that nearly half of customers (49.3%) used Bank DKI cards, dominated by the productive age group (25–45 years old), with the Rusun Kapuk Muara–Penjaringan route being the busiest. The main managerial recommendation is to strengthen the partnership with Bank DKI and optimize services in this busy corridor.

Acute Respiratory Infection (ARI) is an infectious disease that often affects the upper and lower respiratory tract. This disease is one of the main causes of death in children under five, especially in areas with less favourable environmental conditions. This study aims to map the distribution of ARI in Central Java Province using the K-Means clustering method. Through data analysis that includes inputting, transforming, processing, and visualisation, this study successfully identified three clusters of areas with different levels of ARI distribution. Cluster 0 indicates areas with low risk, such as Demak and Semarang Regency, Cluster 1 indicates areas with medium risk, such as Klaten, Magelang Regency, Pati, while cluster 2 indicates areas with high risk, including Semarang City and Surakarta City. The results of this analysis are presented as a map using QGIS to spatially visualise the distribution of ARI across Central Java. Thus, local governments can design more effective and targeted ARI prevention and control strategies.

The research was intended to cluster the production areas of Indonesia's fishery products especially Skipjack Tuna, Tuna, Mackarel Tuna, and shrimp using data science techniques. The algorithm used was K-means Clustering. The data used was annual production data for each province for the last 3 years (2019 – 2021). Determination of the number of clusters using the Elbow Method. For each commodity, three clusters were obtained, namely clusters with low production, medium production, and high production. For Skipjack Tuna, there were 19 provinces belonging to the low cluster, 13 provinces being medium, and 2 provinces being high. For Tuna, there were 22 provinces in the low cluster, 9 provinces in the middle, and 3 provinces in the high cluster. For Mackarel Tuna, low was 19 provinces, medium was 12 provinces, and high was 3 provinces. For shrimp, 23 provinces were low, 7 provinces were medium, and 4 provinces were high. High production clusters for Skipjack Tuna were North Sulawesi and North Maluku Provinces, Tuna were North Sulawesi, North Maluku and Maluku Provinces, for Mackarel Tuna were Aceh, East Java and Maluku Provinces, and for shrimp were North Sumatra, West Kalimantan, South Kalimantan and East Kalimantan Provinces.

The importance of data mining in Indonesia is increasing along with the growth of big data in various strategic sectors. Data mining plays an important role in transforming complex data into useful information to support data-driven decision making, which is urgently needed in the face of competitive challenges and operational complexity. This research aims to examine the development of data mining techniques and applications in Indonesia over the last decade (2015-2024). Through a systematic literature review approach, data was collected from academic publications in SCOPUS indexed databases. From the initial 95 papers found, a further selection was made based on accessibility, title, and abstract until 64 papers were included in the article review. The results show that techniques such as K-Means, Naive Bayes, and Decision Tree are most commonly used. In the business sector, clustering through K-Means is widely applied for market segmentation and consumer pattern analysis. The healthcare sector mainly utilizes classification techniques, such as Naive Bayes and Decision Tree, for disease risk prediction and early diagnosis. Meanwhile, the education sector uses data mining to assess student performance and predict potential dropouts, assisting institutions in optimizing learning strategies.

Based on the 2016-2020 Central Java Disaster Risk Assessment, floods and landslides are the most frequent disasters, with 818 flood cases accounting for 31.33% of the total disasters and landslides accounting for 29.57%. This study aims to cluster disaster-prone areas in Central Java using the K-Means algorithm and the GeoPandas library. Data on disaster events for the period 2019-2023 was obtained from the National Disaster Management Agency, while administrative map data of Central Java was downloaded from the Geoportal of Central Java Province. The research stages include data collection, data cleaning, standardization using the Standard Scaler method, application of the K-Means algorithm for regional clustering, and visualization of results using GeoPandas. The results showed that Central Java was divided into four clusters, namely: cluster 0 (disaster-prone areas) includes 3 regions, cluster 1 (non-disaster-prone areas) has 22 regions, cluster 2 (flood-prone areas) consists of 7 regions, and cluster 3 (landslide-prone areas) has 3 regions. The results of this research provide spatial data-based information that can be used as a basis in decision-making for disaster mitigation in Central Java.

The rapid development of information and communication technology has driven the need for information services that are more relevant and adaptive to user behaviour. This research aims to integrate data analytics in the study of user behaviour to support the development of effective information services. The dataset used is Kaggle's Online Retail Dataset, which includes sales transaction data of online retail companies in the UK from December 2010 to December 2011. The analysis was conducted through customer segmentation using K-Means Clustering algorithm and predictive analysis with Association Rule Mining. The segmentation results successfully grouped customers into four main clusters, namely loyal customers, potential customers, passive customers, and low-spending customers. Model evaluation showed optimal performance with an accuracy rate of 85%, precision of 82%, recall of 78%, and F1-Score of 80%, and Silhouette Score of 0.62, indicating effective customer segmentation. The findings prove that the application of data analytics can provide deep insights into customer behaviour and support the development of more personalised and adaptive information services. This research is expected to be a reference in designing data-driven information service development strategies in various sectors.

This study aims to measure the level of participant satisfaction with learning services at LKP Multi Talenta Komputer Purwokerto. Participant satisfaction is an important indicator in assessing the effectiveness of the learning curriculum implemented. Factors such as facilities, services, and obstacles in learning become the main benchmark. Data from 54 respondents were processed using K-Means Clustering algorithm to identify the most superior and weak factors. The results of this study provide recommendations for future service improvements, in order to increase participant loyalty and learning effectiveness.