Search Results - "k-means"

Basic human needs include a house that serves as a place to live and a shelter from everything. In Indonesia, owning a house is still a challenging aspect due to its high price. Information on house prices is needed for prospective buyers or consumers, so that buyers can adjust their needs and finan...

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The K-means clustering algorithms (Random, K-means++, Canopy, and Farthest First) are unsupervised machine learning techniques designed to group data points based on their similarities. The study examined the effects of clustering algorithms and distance metrics on climate data analysis from meteoro...

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This article deals with, results which are shown using K-Means as a tool for diagnosis of Aircraft engine condition.

Kacang kedelai merupakan tanaman pangan yang dapat diolah dalam berbagai olahan, seperti tempe & tahu. Indonesia mempunyai banyak varietas kedelai, varietas lokal atau impor. Meningkatnya konsumsi kedelai tersebut sangat dipengaruhi oleh pemilihan varietas dari kedelai tersebut. Tetapi hanya beb...

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Assessment of the ability of IT employees is very necessary to determine the ability of employees to work so that it can be a reference and evaluation for the future. Therefore, we need a technique that can group the employee's ability to determine the employee's ability using the K-Means...

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Background: Tuberculosis (TBC) is a major health problem in Indonesia, especially in Gorontalo, with high spread due to poor ventilation, overcrowding, and unhealthy lifestyles. Purpose: To analyze the characteristics of TB patients in one of Gorontalo City's hospitals using K-Means Clustering....

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<i>Background and Objectives:</i> Traditionally, we evaluate the size of a wound by using Opsite Flexigrid transparent film dressing, placing it over the wound, tracing the edges of the wound, and then calculating the area. However, this method is both time-consuming and subjective, ofte...

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Penelitian ini bertujuan menghasilkan nilai Customer Lifetime Value (CLV) pada setiap segmen pelanggan dengan menggunakan algoritma K-means dalam melakukan klusterisasi pelanggan. Pembentukan kluster menggunakan metode validasi Dunn Index dan Silhoutte Coefficient dengan nilai 0.84 dan 0.54. Kluster...

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Two unsupervised classifiers for optimum multithreshold are presented; fast Otsu and k-means. The unparametric methods produce an efficient procedure to separate the regions (classes) by select optimum levels, either on the gray levels of image histogram (as Otsu classifier), or on the gray levels o...

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Technological developments have transformed traditional buying and selling practices into online transactions. Amazon, as one of the largest e-commerce platforms, continues to innovate to maintain its competitive advantage, offering a variety of products and services. This research uses the K-Means...

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In this article, we analyse the skills and competencies of young Polish adults in the context of a rapidly changing labour market. The research focuses on understanding how young adults, faced with technological progress, globalisation, and demographic changes, can adapt their skills to meet future...

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Jakarta, as a metropolitan city in Indonesia, often experiences flooding caused by high rainfall, poor drainage systems, and rapid urbanization. This research aims to classify flood-prone areas in Jakarta using a combination of K-Means Clustering and Naïve Bayes Classifier algorithms. The research p...

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Indonesaia tepatnya Pulau Jawa menjadi sentra kegiatan ekonomi, pendidikan, dan teknologi. Seiring berkembangnya teknologi informasi dan komunikasi (TIK), kebutuhan akan keterampilan digital semakin meningkat. Permasalahan penelitian masih ada perbedaan signifikan dalam tingkat penguasaan keterampil...

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Television remains one of the most influential mass media platforms for disseminating information and entertainment to the public, with one notable example being the I’Murojaah program aired by Muslim TV on MNC Channels. The objectives of this study are to identify the level of viewer satisfaction...

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Clustering high-dimensional data remains challenging because traditional k-means is sensitive to noise, outliers, and high dimensionality, often leading to unstable performance. The research presents a robust clustering system which combines the Fully Corrective Frank-Wolfe (FCFW) algorithm with k-...

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Crime is a phenomenon that significantly impacts society, necessitating mapping efforts that can be utilized for further analysis. Clustering, as a data analysis technique, groups objects based on similarities or differences in their characteristics. This approach enhances the understanding of data...

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Under the background of "double carbon", the installed capacity of wind power grows year by year, characterized by intermittency and volatility, bringing challenges to the reliable operation of the power system. This study proposes an optimal capacity configuration method for supercapacito...

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In order to address the issues of arbitrary center selection and unreasonable privacy budget allocation leading to poor clustering performance in differential privacy K-means clustering algorithm, a new center selection scheme is designed based on two principles for initial center selection. By calc...

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This study aims to apply the K-Means Clustering method using employee attendance data. The background of this research problem is to improve the understanding and management of employee attendance by identifying similar attendance patterns in different groups. Employee attendance impacts their moral...

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Bu çalışma, Türkiye’nin Doğu Karadeniz Bölgesi’ndeki yedi ilde (Artvin, Bayburt, Giresun, Gümüşhane, Ordu, Rize, Trabzon) 2009-2021 yılları arasındaki iklim değişikliği ve çevresel göstergelerin güvenlik perspektifinden değerlendirilmesini amaçlamaktadır. Disiplinler arası bir yaklaşımla, K-means (K...

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