Metode Pencocokan Bunyi Ketuk Buah dengan Kadar Kemanisan Menggunakan k-Nearest Neighbour

2017 ◽  
Vol 8 (2) ◽  
Author(s):  
Ranny Ranny ◽  
Yustinus Eko Soelistio ◽  
Ni Made Satvika
Keyword(s):  
Fourier Transform ◽  
Fast Fourier Transform ◽  
Nearest Neighbor ◽  
Training Data ◽  
Nearest Neighbour ◽  
K Nearest Neighbor ◽  
Feature Extraction Method ◽  
Feature Based ◽  
Index Terms ◽  
Very High

The development of fruit local industry is very high, but it less competitive than the imported fruit product. The kind of Indonesian fruit is very variative, but the support technology in this industry is stiil not implemented. This problem make the local fruit industry cannot compete with imported fruit. The purpose of the research is to develop a technology that can increase the using of technology on fruit industry. This research focus is fruit sweetness measeurment technology. This research the fruit tapping sound. Fast Fourier Transform is used as sound feature extraction method to get the feature. Based on the feature the fruit sweetness level can be predicted using the k Nearest Neighbour (kNN). The experiment on this research is divided into two parts. is using the training data to predict the sweetness levelof the fruits. The result of the research shows that the correlation between tapping sound and sweetness level can be used to predict the sweetness level of the fruit. Index Terms—Sweetness Degree, Brix, k Nearest Neighbor, and Fast Fourier Transform.

scholarly journals Human Emotion Recognition Based on EEG Signal Using Fast Fourier Transform and K-Nearest Neighbor

2020 ◽  
Vol 5 (6) ◽  
pp. 1082-1088
Author(s):  
Anton Yudhana ◽  
Akbar Muslim ◽  
Dewi Eko Wati ◽  
Intan Puspitasari ◽  
Ahmad Azhari ◽  
...  
Keyword(s):  
Fourier Transform ◽  
Fast Fourier Transform ◽  
Emotion Recognition ◽  
Nearest Neighbor ◽  
Eeg Signal ◽  
K Nearest Neighbor ◽  
Human Emotion

Klasifikasi Daun Dengan Perbaikan Fitur Citra Menggunakan Metode K-Nearest Neighbor

2016 ◽  
Vol 7 (2) ◽  
pp. 98-104 ◽  
Author(s):  
Febri Liantoni
Keyword(s):  
Nearest Neighbor ◽  
Data Extraction ◽  
Median Filter ◽  
Training Data ◽  
K Nearest Neighbor ◽  
Invariant Moment ◽  
Processing Level ◽  
Index Terms ◽  
Well Data ◽  
Life On Earth

Plants are the most important part in life on earth as oxygen supplier to breathe, groceries, fuel, medicine and more. Plants can be classified based on its leaves shape. Classification process is required well data extraction feature, so it needs fixing feature process at pre-processing level. Combining median filter and image erosion is used for fixing feature process. Whereas for feature extraction is used invariant moment method. In this research, it is used leaves classification based on leaves edge shape. K-Nearest Neighbor Method (KNN) is used for leaves classification process. KNN method is chosen because this method is known rapid in training data, effective for large training data, simple and easy to learn. Testing the result of leaves classification from image which is on dataset has been built to get accuracy value about 86,67%. Index Terms—Classification, Median Filter, Invariant Moment, K-Nearest Neighbor.

scholarly journals IMPLEMENTASI METODE K-NEAREST NEIGHBOUR (K_NN) UNTUK MENDUGA SALINITAS AIR LAUT

2020 ◽  
Vol 1 (1) ◽  
pp. 27-32
Author(s):  
Yuri Efenie ◽  
Miftahul Walid
Keyword(s):  
Measurement Method ◽  
Additional Data ◽  
Nearest Neighbor ◽  
Sea Water ◽  
Training Data ◽  
Nearest Neighbour ◽  
K Nearest Neighbor ◽  
Long Time ◽  
Input Variables

In this research, trying to predict the salinity of sea water using the K-Nearest Neighbor method, this method serves to clarify the input data using the distance measurement method with training data, the variable used in this study is the value of the location of coordinates (latitude and longitude) and the output is in the form of salinity, the case study in this study is the southern waters of Sumenep, the system has been able to make an estimate but with an error rate of 1.00 so that there is a need for re-analysis because the data used is only small, the need for additional data so that the results will be more optimal, it is also necessary to experiment with changing methods or simplifying rules or by adding input variables in the system that have been created so that it produces better accuracy values, because the existing system still requires a long time in estimating.

scholarly journals k-Nearest Neighbor Learning with Graph Neural Networks

Mathematics ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 830
Author(s):  
Seokho Kang
Keyword(s):  
Neural Network ◽  
Nearest Neighbor ◽  
Learning Algorithm ◽  
Weighting Function ◽  
High Sensitivity ◽  
Training Data ◽  
K Nearest Neighbor ◽  
Main Challenge ◽  
Benchmark Datasets ◽  
Graph Neural Networks

k-nearest neighbor (kNN) is a widely used learning algorithm for supervised learning tasks. In practice, the main challenge when using kNN is its high sensitivity to its hyperparameter setting, including the number of nearest neighbors k, the distance function, and the weighting function. To improve the robustness to hyperparameters, this study presents a novel kNN learning method based on a graph neural network, named kNNGNN. Given training data, the method learns a task-specific kNN rule in an end-to-end fashion by means of a graph neural network that takes the kNN graph of an instance to predict the label of the instance. The distance and weighting functions are implicitly embedded within the graph neural network. For a query instance, the prediction is obtained by performing a kNN search from the training data to create a kNN graph and passing it through the graph neural network. The effectiveness of the proposed method is demonstrated using various benchmark datasets for classification and regression tasks.

scholarly journals Android Malware Detection using Machine Learning

2020 ◽  
Vol 8 (2S12) ◽  
pp. 65-70
Keyword(s):  
Machine Learning ◽  
Nearest Neighbor ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
User Interest ◽  
Android Malware ◽  
Android Malware Detection

Machine Learning is empowering many aspects of day-to-day lives from filtering the content on social networks to suggestions of products that we may be looking for. This technology focuses on taking objects as image input to find new observations or show items based on user interest. The major discussion here is the Machine Learning techniques where we use supervised learning where the computer learns by the input data/training data and predict result based on experience. We also discuss the machine learning algorithms: Naïve Bayes Classifier, K-Nearest Neighbor, Random Forest, Decision Tress, Boosted Trees, Support Vector Machine, and use these classifiers on a dataset Malgenome and Drebin which are the Android Malware Dataset. Android is an operating system that is gaining popularity these days and with a rise in demand of these devices the rise in Android Malware. The traditional techniques methods which were used to detect malware was unable to detect unknown applications. We have run this dataset on different machine learning classifiers and have recorded the results. The experiment result provides a comparative analysis that is based on performance, accuracy, and cost.

An Incremental Isomap Method for Hyperspectral Dimensionality Reduction and Classification

2021 ◽  
Vol 87 (6) ◽  
pp. 445-455
Author(s):  
Yi Ma ◽  
Zezhong Zheng ◽  
Yutang Ma ◽  
Mingcang Zhu ◽  
Ran Huang ◽  
...  
Keyword(s):  
Manifold Learning ◽  
Nearest Neighbor ◽  
Hyperspectral Image ◽  
Hyperspectral Data ◽  
Training Data ◽  
Support Vector ◽  
Data Sets ◽  
K Nearest Neighbor ◽  
Data Set ◽  
Data Points

Many manifold learning algorithms conduct an eigen vector analysis on a data-similarity matrix with a size of N×N, where N is the number of data points. Thus, the memory complexity of the analysis is no less than O(N2). We pres- ent in this article an incremental manifold learning approach to handle large hyperspectral data sets for land use identification. In our method, the number of dimensions for the high-dimensional hyperspectral-image data set is obtained with the training data set. A local curvature varia- tion algorithm is utilized to sample a subset of data points as landmarks. Then a manifold skeleton is identified based on the landmarks. Our method is validated on three AVIRIS hyperspectral data sets, outperforming the comparison algorithms with a k–nearest-neighbor classifier and achieving the second best performance with support vector machine.

scholarly journals Business Intelligence using the K-Nearest Neighbor Algorithm to Analyze Customer Behavior in Online Crowdfunding Systems

2020 ◽  
Vol 202 ◽  
pp. 16005
Author(s):  
Chashif Syadzali ◽  
Suryono Suryono ◽  
Jatmiko Endro Suseno
Keyword(s):  
Business Intelligence ◽  
Nearest Neighbor ◽  
Customer Behavior ◽  
Training Data ◽  
Business Strategies ◽  
Intelligence Analysis ◽  
K Nearest Neighbor ◽  
Nearest Neighbor Algorithm ◽  
K Nearest Neighbor Algorithm

Customer behavior classification can be useful to assist companies in conducting business intelligence analysis. Data mining techniques can classify customer behavior using the K-Nearest Neighbor algorithm based on the customer's life cycle consisting of prospect, responder, active and former. Data used to classify include age, gender, number of donations, donation retention and number of user visits. The calculation results from 2,114 data in the classification of each customer’s category are namely active by 1.18%, prospect by 8.99%, responder by 4.26% and former by 85.57%. System accuracy using a range of K from K = 1 to K = 20 produces that the highest accuracy is 94.3731% at a value of K = 4. The results of the training data that produce a classification of user behavior can be used as a Business Intelligence analysis that is useful for companies in determining business strategies by knowing the target of optimal market.

scholarly journals Automatic Petrol and Diesel Engine Sound Identification Based on Machine Learning Approaches

2019 ◽  
Vol 130 ◽  
pp. 01011
Author(s):  
Halim Frederick ◽  
Astuti Winda ◽  
Mahmud Iwan Solihin
Keyword(s):  
Machine Learning ◽  
Fourier Transform ◽  
Diesel Engine ◽  
Fast Fourier Transform ◽  
Intelligent System ◽  
Mechanical Energy ◽  
Petrol Engine ◽  
Support Vector ◽  
Learning Approaches ◽  
Feature Extraction Method

Petrol and diesel engine have a significantly different way to convert chemical energy into mechanical energy. In this work, the intelligent system approach is used to automatically identify the type of engine based on the sound of the engine. The combination of signal processing and machine learning technique for automatic petrol and diesel engine sound identification is presented in this work. After a signal preprocessing step of the engine sound, a Fast Fourier Transform (FFT)-based frequency characteristic modelling technique is applied as the feature extraction method. The resulting features extracted from the sound signal, in the form of frequency in the FFT matrix, are used as the inputs for the machine learning, the Support Vector Machine (SVM), step of the proposed approach. The experiment of FFT with SVM-based diesel and petrol engine sound identification has been carried out. The results show that the proposed approach produces a good accuracy in the relatively short training time. Experimental results show the training and testing accuracy of 100 % and 100 % respectively. They confirm the effectiveness of the proposed intelligent automatic diesel and petrol engine sound identification based on Fast Fourier Transform (FFT) and Support Vector Machines (SVMs).

scholarly journals Identification of Leukemia Subtypes from Microscopic Images Using Convolutional Neural Network

Diagnostics ◽  
2019 ◽  
Vol 9 (3) ◽  
pp. 104 ◽  
Author(s):  
Ahmed ◽  
Yigit ◽  
Isik ◽  
Alpkocak
Keyword(s):  
Machine Learning ◽  
Data Augmentation ◽  
Nearest Neighbor ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Data Set ◽  
Leukemia Data

Leukemia is a fatal cancer and has two main types: Acute and chronic. Each type has two more subtypes: Lymphoid and myeloid. Hence, in total, there are four subtypes of leukemia. This study proposes a new approach for diagnosis of all subtypes of leukemia from microscopic blood cell images using convolutional neural networks (CNN), which requires a large training data set. Therefore, we also investigated the effects of data augmentation for an increasing number of training samples synthetically. We used two publicly available leukemia data sources: ALL-IDB and ASH Image Bank. Next, we applied seven different image transformation techniques as data augmentation. We designed a CNN architecture capable of recognizing all subtypes of leukemia. Besides, we also explored other well-known machine learning algorithms such as naive Bayes, support vector machine, k-nearest neighbor, and decision tree. To evaluate our approach, we set up a set of experiments and used 5-fold cross-validation. The results we obtained from experiments showed that our CNN model performance has 88.25% and 81.74% accuracy, in leukemia versus healthy and multiclass classification of all subtypes, respectively. Finally, we also showed that the CNN model has a better performance than other wellknown machine learning algorithms.

Sign in / Sign up

Export Citation Format

Share Document