Increasing numbers of interconnected networks to the internet have led to an increase in cyber attacks which necessitates the need for an effective intrusion detection system. In this paper, two machine learning techniques: Rough Set (LEM2 Algorithm) and k-Nearest Neighbour (kNN) are used for intrusion detection. Rough set is a classic mathematical tool for feature extraction in a dataset which...

In this paper, methods for constructing two dimensional nearest neighbour balanced (NNB) designs are considered. The methods given by Afsarinejad and Seeger (1988) are extended to give a new family of nearest neighbour balanced designs. Both nearest neighbour balanced designs with and without borders are constructed. A method of construction of a class of partial nearest neighbour balanced (PNN...

Pada tahun 2020, Kementerian Pendidikan dan Kebudayaan meluncurkan program Merdeka Belajar yang membantu para mahasiswa mahasiswi untuk menghadapi lingkungan kerja setelah mereka lulus. Akan tetapi, ini memunculkan polemik. Penelitian mencoba melakukan klasifikasi sentimen pada kasus Magang menggunakan algoritma KNN. KNN dipilih dikarenakan lebih handal dalam menangani data noisy, namun meningk...

The Current wireless technology is used to find out where the user in room. Utilization of WiFi strength signal from Access Point (AP) can provide information on position a Alternative determination user's room using Receive Signal Strength (RSS). This research was conducted by comparing distance between users 2 or more APs euclidean technique. Euclidean technique as calculator there are two po...

Probabilistic k-nearest neighbour (PKNN) classification has been introduced to improve the performance of original k-nearest neighbour (KNN) classification algorithm by explicitly modelling uncertainty in the classification of each feature vector. However, an issue common to both KNN and PKNN is to select the optimal number of neighbours, k. The contribution of this paper is to incorporate the ...

This new alternate approach to data processing for analyses that traditionally employed grid-based counting methods is necessary because it removes a user-imposed coordinate system that not only limits an analysis but also may introduce errors. We have modified the widely used "binomial" analysis for APT data by replacing grid-based counting with coordinate-independent nearest neighbour identif...

In this paper we propose a generalisation of the k-nearest neighbour (k-NN) retrieval method based on an error function using distance metrics in the solution and problem space. It is an interpolate method which is proposed to be effective for sparse case bases. The method applies equally to nominal, continuous and mixed domains, and does not depend upon an embedding n-dimensional space. In con...

Nearest neighbour (NN) searches and k nearest neighbour (k-NN) searches are widely used in pattern recognition and image retrieval. An NN (k-NN) search finds the closest object (closest k objects) to a query object. Although the definition of the distance between objects depends on applications, its computation is generally complicated and time-consuming. It is therefore important to reduce the...