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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.

PT. PLN (Persero) UP3 Binjai faces challenges in handling electricity usage violations that increase every year. Lack of utilization of data violations that can be utilized to produce useful information in supporting strategic decision making by PLN, especially in the implementation of Electricity Usage Control (P2TL) activities. This study aims to identify customer violation patterns based on rayon, power, and type of violation with data mining methods using the K-Means Clustering algorithm. The results of the study show that the 3rd cluster represents the most violation-prone area, namely in the West Binjai Rayon, with a power of 450 VA and the most frequent type of P2 violation. The results of the study show that the K-Means algorithm with the Elbow method is able to systematically group data violations based on certain characteristics. The results of this study can provide recommendations to PLN UP3 Binjai to improve the effectiveness of monitoring and enforcement strategies through a more targeted approach.  

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.

This research aims to analyze the best-selling products at Toko Hartati using the K-Means Clustering method. K-Means Clustering is an unsupervised learning algorithm that is effective in grouping data based on certain similar characteristics. In this context, the data used includes the number of sales, product prices, and product categories. Through this analysis, it is hoped that insight can be gained regarding products that have the best sales performance, as well as sales patterns that can be used as a reference in stock management and marketing strategies. The data used in this research includes sales transactions during a certain period, with the aim of identifying product clusters based on sales patterns. The analysis results show the existence of two main product groups, where the first cluster contains products with high sales numbers, which can be classified as best-selling products, while the second cluster includes products with lower sales. These findings provide valuable information for the management of Toko Hartati in determining more targeted marketing strategies and more efficient stock management. This research suggests using the K-Means Clustering method in data-based decision making to improve sales performance in retail stores.

This research aims to cluster disease data based on patient age using the K-Means method at RSUD Dr. RM. Djoelham. In this case study, the clustering method with the K-Means algorithm is used to group patients based on patient age, address and type of disease. With this method, information can be obtained regarding patient grouping patterns based on age at Dr. RM. Djoelham, who helps identify the closest relationships between patient groups and provides insight into the distribution of disease across age groups, regions and types of disease suffered.This research was conducted at RSUD Dr. RM. Djoelham by loading data from patients treated at the hospital. The data used is 1,100 patient data from 2022-2024 which has been recorded by the hospital. This patient data will be analyzed using 3 variables in the research, namely Patient Age (C1), Address (C2), and Type of Disease (C3). With the results, cluster 1 contains 320 data, cluster 2 contains 326 data, and cluster 3 contains 454 data.

This research aims to apply the K-Means Clustering method in grouping consumer interests regarding the use of services at the Binjai Post Office. The Post Office is part of a state-owned enterprise in North Sumatra Province with the main task of providing postal and logistics services. Postal services remain one of the most important means of communication, especially for sending packages, letters, and documents. However, with various services and diverse consumer needs, post offices can provide more effective and relevant services. The K-Means Clustering method is a classification technique based on machine learning algorithms used to identify patterns present in consumer interest data. The data used in this research includes various related variables, namely the type of delivery, total cost, and delivery time. The results of the clustering process conducted using 3 clusters indicate that there is a grouping of consumer data based on preferences for using delivery services. In group 1, there are (21 data points) with a centroid at coordinates (C1) 2; 4.3810; 3.5238. In group 2, there are (124 data points) with a centroid at coordinates (C2) 3; 2.0565; 3.1452. In group 3, there are (387 data points) with a centroid at coordinates (C3) 3.6925; 1.1370; 1.7209. This research shows that the application of K-Means Clustering can enhance the understanding of consumer interests and assist in the development of more targeted strategies to optimally meet needs.

Fire is a disaster that cannot be predicted when it will occur and where it will occur, it's just that densely populated areas are areas that are vulnerable to the danger of fire. Fire disaster in Samarinda City. Data obtained from the Samarinda City Fire and Rescue Service are fire incidents from 2021 to 2023. In 2021 there were 230 fire incidents, in 2022 there were 209 fires, in 2023 there were 99 fires. so this city is one of the cities that experiences the most fires on the island of Kalimantan. Several supporting facilities and infrastructure owned by the Samarinda City Fire and Rescue Department, such as hydrants and fire extinguishing posts, have been increased in number. This research functions to group fire data per year using the k-means clustering algorithm.

Akomodasi merupakan orientasi sosial yang bermakna keutuhan sosial demi menjauhi serta mendamaikan kegentingan, pertikaian yang saling berkaitan. Seumpama bentuk kegiatan akomodasi yang bermanfaat mempersiapkan perlengkapan guna melengkapi keperluan. provinsi jawa barat terdapat tempat wisata yang ramai dikunjungi oleh wisatawan nusantara dan mancanegara oleh sebab itu pengunjung menggunakan fasilitas akomodasi yang tersedia di setiap daerah, wisatawan nusantara ramai menginap di villa sedangkan wisatawan mancanegara lebih memilih hotel berbintang. Penelitian ini memfokuskan bagaimana pengelompokan wisatawan daerah wisata akomodasi dan jenis wisatawan terbanyak yang ramai berkunjung menggunakan algoritma k-means, analisis ini memakai metode Knowledge Discovery in Database (KDD). Teknik pengumpulan data atau pengumpulan data bersumber dari Dinas Pariwisata dan Kebudayaan Provinsi Jawa Barat. Dengan tujuan kiranya menjadi pengetahuan untuk wisatawan serta mampu mempublikasikan wisata di media sosial dan diharapkan akan lebih meningkat jumlah wisatawan yang berkunjung dan meningkatnya pembangunan sarana dan prasarana di setiap daerah akomodas.Hasil dari penelitian ini bahwa daerah akomodasi yang ramai dikunjungi adalah Kota Bandung dengan jenis wisatawan yang datang yaitu pengunjung  dalam negeri (Nusantara).

Indonesia merupakan negara dengan beberapa kota di setiap provinsinya, namun tentunya setiap kota mempunyai tingkat inflasi yang berbeda setiap tahunnya. Dengan banyaknya data inflasi di Indonesia, sulit bagi pemerintah untuk mengkategorikan angka inflasi. Penulis berinisiatif melakukan penelitian tentang mengelompokkan nilai inflasi pengeluaran di jawa barat. Tujuan membuat laporan ini merupakan untuk memperoleh informasi dengan kualitas terbaik dari data yang diproses. Proses ini di harapkan dapat membantu pemerintah dalam mengetahui nilai inflasi berdasarkan kelompok pengeluaran tertinggi dan terendah di Jawa Barat. Metode yang digunakan adalah metode k-means clustering. Penelitian ini juga di dukung menggunakan salah satu perangkat lunak atau tools untuk mengolah data mining yaitu RapidMiner. Didapat hasil cluster terbaik yaitu 5 cluster berdasarkan nilai DBi 0,063. Dimana yang termasuk ke dalam cluster tersebut adalah cluster 0, cluster 2, dan cluster 4 dengan tingkat inflasi rendah berjumlah 5 Kabupaten/Kota. Dan yang termasuk ke dalam tingkat inflasi tinggi adalah cluster 1 dan cluster 3 berjumlah 2 Kabupaten/Kota. Hasil dari data ini juga menunjukkan nilai akurasi sebesar 100%, sehingga bisa disimpulkan jika algoritma k-means mempunyai nilai akurasi tinggi.

Science and technology will facilitate human work. However, on the other hand it will increase competition. In facing intense competition, it is necessary to have competent human resources. Students are expected to be academically prepared, in the form of knowledge and skill readiness to face increasingly fierce competition. One way to see student competence is to look at learning outcomes that can be represented by the exam scores taken. The midterm exam (UTS) is a form of exam which is an assessment component. By knowing the UTS scores, the lecturer knows the distribution of students in terms of academic competence. For this reason, it is necessary to group (clustering) using the k-means algorithm as a consideration for lecturers in forming student study groups based on UTS value clusters.