Analisis Clustering Trafik Jaringan Menggunakan Metode K-Means
نویسندگان
چکیده
Penelitian ini dilakukan untuk membuat sebuah model clustering trafik internet dengan menggunakan algoritma K-means. Penggunaan di wilayah kampus banyak digunakan oleh mahasiswa dan staf pegawai keperluan proses belajar mengajar, ataupun membantu bekerja. internet, pada jam-jam sibuk perkuliahan yang aktif kecepatan menjadi lambat. Hal dipengaruhi banyaknya pengiriman paket header flow/arus lalu lintas koneksi semakin berat/lambat. Dalam mengatasi masalah tersebut, maka diperlukan metode clustring penggunaan k-means dapat mengetahui jenis atau berdasarkan fitur arus/flow pengembangan data mining. Data dalam penelitian yaitu diambil melalui hasil capture wifi selama 3 hari sub bagian Pusat Komputer STMIK WiCiDa wireshark bettercap akan melakukan serangan arp spoof, dimana penulis diposisikan sebagai penengah menangkap dari semua perangkat jaringan sama. Tools dijalankan kali linux. sudah dicapturing difilter kemudian diexport bentuk .pcap. Hasil berupa Clustring meng-clusterkan arus tiga cluster Web, Video VoIP, Network. Pada saat pengujian menghasilkan nilai akurasi baik Mendapatkan : Cluster 0 = 302638 data, 1 331982 451426 data.
منابع مشابه
Persistent K-Means: Stable Data Clustering Algorithm Based on K-Means Algorithm
Identifying clusters or clustering is an important aspect of data analysis. It is the task of grouping a set of objects in such a way those objects in the same group/cluster are more similar in some sense or another. It is a main task of exploratory data mining, and a common technique for statistical data analysis This paper proposed an improved version of K-Means algorithm, namely Persistent K...
متن کاملAdaptive K-Means Clustering
Clustering is used to organize data for efficient retrieval. One of the problems in clustering is the identification of clusters in given data. A popular technique for clustering is based on K-means such that the data is partitioned into K clusters. In this method, the number of clusters is predefined and the technique is highly dependent on the initial identification of elements that represent...
متن کاملConstrained K-Means Clustering
We consider practical methods for adding constraints to the K-Means clustering algorithm in order to avoid local solutions with empty clusters or clusters having very few points. We often observe this phenomena when applying K-Means to datasets where the number of dimensions is n 10 and the number of desired clusters is k 20. We propose explicitly adding k constraints to the underlying clusteri...
متن کاملSubspace K-means clustering.
To achieve an insightful clustering of multivariate data, we propose subspace K-means. Its central idea is to model the centroids and cluster residuals in reduced spaces, which allows for dealing with a wide range of cluster types and yields rich interpretations of the clusters. We review the existing related clustering methods, including deterministic, stochastic, and unsupervised learning app...
متن کاملk-means clustering
The sets Sj are the sets of points to which μj is the closest center. In each step of the algorithm the potential function is reduced. Let’s examine that. First, if the set of centers μj are fixed, the best assignment is clearly the one which assigns each data point to its closest center. Also, assume that μ is the center of a set of points S. Then, if we move μ to 1 |S| ∑ i∈S xi then we only r...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Buletin Poltanesa
سال: 2022
ISSN: ['1412-0097', '2614-8374']
DOI: https://doi.org/10.51967/tanesa.v23i2.1736