K-Value Effect for Detecting Stairs Descent using Combination GLCM and KNN

This study aims to analyze the k-value on K nearest neighbor classification. k-value is the distance used to find the closest data to label the class from the testing data. Each k-value can produce a different class label against the same testing data. The variants of k-value that we use are k=3, k=5 and k=7 to find the best k-value. There are 2 classes that are used in this research. Both classes are stairs descent and floor classes. The gray level co-occurrence matrix method is used to extract features. The data we use comes from videos obtained from the camera on the smart wheelchair taken by the frame. Refer to the results of our tests, the best k-value is obtained when using k=7 and angle 0° with accuracy is 92.5%. The stairs descent detection system will be implemented in a smart wheelchair

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Mangrove Forest Classification in Drone Images Using HSV Color Moment and Haralick Features Extraction with K-Nearest Neighbor

Signal and Image Processing Letters ◽

10.31763/simple.v1i3.6 ◽

2019 ◽

Vol 1 (3) ◽

pp. 1-12

Author(s):

Agus Wahyu Widodo ◽

Deo Hernando ◽

Wayan Firdaus Mahmudy

Keyword(s):

Mangrove Forest ◽

Nearest Neighbor ◽

Classification Method ◽

Mangrove Forests ◽

K Nearest Neighbor ◽

Distance Method ◽

K Value ◽

Nearest Neighbor Classification ◽

Haralick Features ◽

Neighbor Classification

Due to the problems with uncontrolled changes in mangrove forests, a forest function management and supervision is required. The form of mangrove forest management carried out in this study is to measure the area of mangrove forests by observing the forests using drones or crewless aircraft. Drones are used to take photos because they can capture vast mangrove forests with high resolution. The drone was flown over above the mangrove forest and took several photos. The method used in this study is extracting color features using mean values, standard deviations, and skewness in the HSV color space and texture feature extraction with Haralick features. The classification method used is the k-nearest neighbor method. This study conducted three tests, namely testing the accuracy of the system, testing the distance method used in the k-nearest neighbor classification method, and testing the k value. Based on the results of the three tests above, three conclusions obtained. The first conclusion is that the classification system produces an accuracy of 84%. The second conclusion is that the distance method used in the k-nearest neighbor classification method influences the accuracy of the system. The distance method that produces the highest accuracy is the Euclidean distance method with an accuracy of 84%. The third conclusion is that the k value used in the k-nearest neighbor classification method influences the accuracy of the system. The k-value that produces the highest accuracy is k = 3, with an accuracy of 84%.

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A proposed method for handling an imbalance data in classification of blood type based on Myers-Briggs type indicator

Jurnal Teknologi dan Sistem Komputer ◽

10.14710/jtsiskom.2020.13625 ◽

2020 ◽

Vol 8 (4) ◽

pp. 276-283

Author(s):

Ahmad Taufiq Akbar ◽

Rochmat Husaini ◽

Bagus Muhammad Akbar ◽

Shoffan Saifullah

Keyword(s):

Nearest Neighbor ◽

Data Use ◽

Blood Type ◽

K Nearest Neighbor ◽

Myers Briggs Type Indicator ◽

Imbalance Data ◽

Type Indicator ◽

Testing Data ◽

Neighbor Classification

Blood type still leads to an assumption about its relation to some personality aspects. This study observes preprocessing methods for improving the classification accuracy of MBTI data to determine blood type. The training and testing data use 250 data from the MBTI questionnaire answers given by 250 respondents. The classification uses the k-Nearest Neighbor (k-NN) algorithm. Without preprocessing, k-NN results in about 32 % accuracy, so it needs some preprocessing to handle data imbalance before the classification. The proposed preprocessing consists of two-stage, the first stage is the unsupervised resample, and the second is the supervised resample. For the validation, it uses ten cross-validations. The result of k-Nearest Neighbor classification after using these proposed preprocessing stages has finally increased the accuracy, F-score, and recall significantly.

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Gini Index With Local Mean Based For Determining K Value In K-Nearest Neighbor Classification

Journal of Physics Conference Series ◽

10.1088/1742-6596/1235/1/012006 ◽

2019 ◽

Vol 1235 ◽

pp. 012006

Author(s):

M E Saputra ◽

H Mawengkang ◽

E B Nababan

Keyword(s):

Gini Index ◽

Nearest Neighbor ◽

K Nearest Neighbor ◽

K Value ◽

Nearest Neighbor Classification ◽

Local Mean ◽

Neighbor Classification

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Classification of Bird Based on Face Types Using Gray Level Co-Occurrence Matrix (GLCM) Feature Extraction Based on the k-Nearest Neighbor (K-NN) Algorithm

Journal of Applied Intelligent System ◽

10.33633/jais.v6i2.4627 ◽

2021 ◽

Vol 6 (2) ◽

pp. 111-119

Author(s):

Daurat Sinaga ◽

Feri Agustina ◽

Noor Ageng Setiyanto ◽

Suprayogi Suprayogi ◽

Cahaya Jatmoko

Keyword(s):

Feature Extraction ◽

Nearest Neighbor ◽

Correlation Energy ◽

Training Data ◽

Gray Level ◽

K Nearest Neighbor ◽

Testing Data ◽

Occurrence Matrix

Indonesia is one of the countries with a large number of fauna wealth. Various types of fauna that exist are scattered throughout Indonesia. One type of fauna that is owned is a type of bird animal. Birds are often bred as pets because of their characteristic facial voice and body features. In this study, using the Gray Level Co-Occurrence Matrix (GLCM) based on the k-Nearest Neighbor (K-NN) algorithm. The data used in this study were 66 images which were divided into two, namely 55 training data and 11 testing data. The calculation of the feature value used in this study is based on the value of the GLCM feature extraction such as: contrast, correlation, energy, homogeneity and entropy which will later be calculated using the k-Nearest Neighbor (K-NN) algorithm and Eucliden Distance. From the results of the classification process using k-Nearest Neighbor (K-NN), it is found that the highest accuracy results lie at the value of K = 1 and at an degree of 0 ° of 54.54%.

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Algorithm Configuration K-Nearest To Clarification Medicine Tree Based On Extraction, Variation Of Color, Texture And Shape Of Leaf

Ilomata International Journal of Social Science ◽

10.52728/ijss.v2i1.187 ◽

2021 ◽

Vol 2 (1) ◽

pp. 81-91

Author(s):

Ardhi Dinullah Baihaqie ◽

Rayung Wulan

Keyword(s):

Medicinal Plants ◽

Nearest Neighbor ◽

Leaf Shape ◽

Image Data ◽

K Nearest Neighbor ◽

K Value ◽

Average Value ◽

Algorithm Configuration ◽

Occurrence Matrix ◽

Color Texture

At this time to overcome difficulties in identifying medicinal plants that have an impact on the frequent errors in the use of medicinal plants. The formulation of the problem to be discussed in this study is how to identify medicinal plants based on feature extraction of color, texture, and leaf shape. Steps to resolve this problem by collecting image data of medicinal plants, then the image data extracted leaf color features using Red Green Blue (RGB) and Hue Saturation Value (HSV), based on leaf texture using the Gray Level Co-occurrence Matrix (GLCM), based on the shape leaves use eccentricity and metrics. and then classified by the K-Nearest Neighbor (KNN) method. The results in this study the accuracy of Chinese Petai leaves is superior to other types of leaves, which is 98%, which occurs at each K value. Other types of leaves have various values. Saga leaves range between 94% - 97%, Green Betel leaves between 92.8% - 97%, and Red Betel leaves between 91.7% - 95%, Optimal K values indicated by K = 3 have an average accuracy rate of 96.7% also have sensitivity value of 93.3%. The addition of K = 5, K = 7, K = 9, and K = 11 tends to decrease the average value of accuracy and sensitivity.

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PREDIKSI KELANCARAN PEMBAYARAN CICILAN CALON DEBITUR DENGAN METODE K-NEAREST NEIGHBOR

JURTEKSI ◽

10.33330/jurteksi.v7i2.1078 ◽

2021 ◽

Vol 7 (2) ◽

pp. 195-202

Author(s):

Sri Ayu Rizky ◽

Rolly Yesputra ◽

Santoso Santoso

Keyword(s):

Data Mining ◽

Nearest Neighbor ◽

Training Data ◽

Mining Method ◽

K Nearest Neighbor ◽

Application System ◽

K Value ◽

Testing Data ◽

Calculation Process ◽

K Nearest Neighbor Algorithm

Abstract: In this research, a prediction system has been successfully developed to predict whether or not a prospective money borrower will run smoothly. Prospective borrowers who will borrow, some of the data that meet the criteria will be inputted by the office clerk into a prediction application system interface to be processed using the Data Mining method, namely the K-Nearest Neighbor Algorithm with the Codeigniter programming language 3. The results of the Euclidean calculation process are based on predetermined criteria Between training data (training) to testing data (test) will be displayed with a table that has been sorted from smallest to largest containing 9 closest neighbors according to the K value that has been determined, namely 9. The nine neighbors will be taken the dominant category. This dominant category can be used as a guideline that makes it easier for the leader to make a decision on the next borrower. Keywords: Data Mining; Euclidean; K-Nearest Neighbor; Prospective Borrowers; Abstrak: Dalam penelitian ini telah berhasil dibuat sebuah sistem prediksi untuk memprediksi lancar atau tidak lancarnya seorang calon peminjam uang. Calon peminjam uang yang akan meminjam, sebagian datanya yang memenuhi kriteria akan diinputkan petugas kantor ke dalam sebuah interface sistem aplikasi prediksi untuk diolah menggunakan metode Data Mining yaitu Algoritma K-Nearest Neighbor dengan bahasa pemrograman Codeigniter 3. Hasil proses perhitungan Euclidean berdasarkan kriteria yang sudah ditentukan antara data training (latih) ke data testing (uji) tersebut akan ditampilkan dengan sebuah tabel yang sudah diurutkan dari yang terkecil ke terbesar berisi 9 tetangga terdekat sesuai dengan nilai K yang sudah ditentukan yaitu 9. Sembilan tetangga tersebut akan diambil kategori yang dominan. Kategori yang dominan tersebut bisa dijadikan suatu pedoman yang memudahkan pimpinan dalam mengambil sebuah keputusan kepada calon peminjam selanjutnya. Kata kunci: Debitur; Data Mining; Euclidean; K-Nearest Neighbor

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Optimizing Error Rate in Intrusion Detection System Using Artificial Neural Network Algorithm

International Journal of Emerging Research in Management and Technology ◽

10.23956/ijermt.v6i9.102 ◽

2018 ◽

Vol 6 (9) ◽

pp. 152

Author(s):

S. Vijaya Rani ◽

G. N. K. Suresh Babu

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Intrusion Detection ◽

Error Rate ◽

Learning Process ◽

Nearest Neighbor ◽

Detection System ◽

Support Vector ◽

K Nearest Neighbor ◽

Artificial Neural

The illegal hackers penetrate the servers and networks of corporate and financial institutions to gain money and extract vital information. The hacking varies from one computing system to many system. They gain access by sending malicious packets in the network through virus, worms, Trojan horses etc. The hackers scan a network through various tools and collect information of network and host. Hence it is very much essential to detect the attacks as they enter into a network. The methods available for intrusion detection are Naive Bayes, Decision tree, Support Vector Machine, K-Nearest Neighbor, Artificial Neural Networks. A neural network consists of processing units in complex manner and able to store information and make it functional for use. It acts like human brain and takes knowledge from the environment through training and learning process. Many algorithms are available for learning process This work carry out research on analysis of malicious packets and predicting the error rate in detection of injured packets through artificial neural network algorithms.

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Supervised Classifier Approach for Intrusion Detection on KDD with Optimal MapReduce Framework Model in Cloud Computing

Recent Patents on Computer Science ◽

10.2174/1573401315666190619113510 ◽

2019 ◽

Vol 12 ◽

Author(s):

M. Ilayaraja ◽

S. Hemalatha ◽

P. Manickam ◽

K. Sathesh Kumar ◽

K. Shankar

Keyword(s):

Machine Learning ◽

Cloud Computing ◽

Intrusion Detection ◽

Decision Tree ◽

Learning Strategies ◽

Nearest Neighbor ◽

Detection System ◽

K Nearest Neighbor ◽

Mapreduce Model ◽

The Web

Cloud computing is characterized as the arrangement of assets or administrations accessible through the web to the clients on their request by cloud providers. It communicates everything as administrations over the web in view of the client request, for example operating system, organize equipment, storage, assets, and software. Nowadays, Intrusion Detection System (IDS) plays a powerful system, which deals with the influence of experts to get actions when the system is hacked under some intrusions. Most intrusion detection frameworks are created in light of machine learning strategies. Since the datasets, this utilized as a part of intrusion detection is Knowledge Discovery in Database (KDD). In this paper detect or classify the intruded data utilizing Machine Learning (ML) with the MapReduce model. The primary face considers Hadoop MapReduce model to reduce the extent of database ideal weight decided for reducer model and second stage utilizing Decision Tree (DT) classifier to detect the data. This DT classifier comprises utilizing an appropriate classifier to decide the class labels for the non-homogeneous leaf nodes. The decision tree fragment gives a coarse section profile while the leaf level classifier can give data about the qualities that influence the label inside a portion. From the proposed result accuracy for detection is 96.21% contrasted with existing classifiers, for example, Neural Network (NN), Naive Bayes (NB) and K Nearest Neighbor (KNN).

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