scholarly journals ANALISIS SUARA PERNAPASAN PARU-PARU ASMA DENGAN TIDAK ASMA MENGGUNAKAN METODE K NEAREST NEIGHBORS

DIELEKTRIKA ◽  
2021 ◽  
Vol 8 (1) ◽  
pp. 1
Author(s):  
Ari Satriadi
Keyword(s):  
Cross Validation ◽  
Nearest Neighbors ◽  
K Nearest Neighbors ◽  
Fold Cross Validation

Asma adalah penyakit pada saluran napas yang menyebabkan peningkatan hiperesponsif jalan napas dan menimbulkan gejala mengi/wheeze (napas berbunyi ngik-ngik). Bunyi napas wheeze merupakan salah satu ciri yang menandakan seseorang menderita asma. Penelitian ini dilakukan untuk membuat serta menguji suatu sistem yang dapat mengidentifikasi perbedaan ciri suara pernapasan wheeze pada pasien asma dan pernapasan lainnya dengan metode k-Nearest Neighbors (k-NN). Ciri suara yang digunakan yaitu rata-rata sinyal dan standar deviasi sinyal dalam domain waktu, rata-rata spektrum, standar deviasi spektrum, magnitude tertinggi saat frekuensi 0Hz, frekuensi dengan magnitude tertinggi pertama, kedua, dan ketiga.  K-NN adalah sebuah metode untuk melakukan klasifikasi terhadap objek berdasarkan data pembelajaran yang jaraknya paling dekat dengan objek tersebut. Didapatkan data suara pernapasan wheeze dan non wheeze melalui perekaman langsung kepada subjek penderita asma dan tidak asma. Dari seluruh data suara yang didapatkan kemudian dilakukan segmentasi data untuk mengambil event pernapasasn yang dibutuhkan kemudian dilakukan ekstraksi ciri untuk mendapatkan ciri matematis dari suara tersebut. 80% dari total keseluruhan data dilakukan pelatihan menggunakan metode 10 fold cross validation dan diapatkan hasil pelatihan dengan kemampuan klasifikasi maksimum pada k=3 dan k=5 dengan validitas yang sama 97,2%. Untuk pengujian kinerja k-NN pada tahap akhir diperoleh kemampuan maksimum pengklasifikasian untuk k=3 adalah 86,6% dan k=5 adalah 86,6%.

scholarly journals Impression Classification of Endek (Balinese Fabric) Image Using K-Nearest Neighbors Method

2018 ◽  
pp. 213-220 ◽  
Author(s):  
Gede Aditra Pradnyana ◽  
I Komang Agus Suryantara ◽  
I Gede Mahendra Darmawiguna
Keyword(s):  
Cross Validation ◽  
Nearest Neighbors ◽  
K Nearest Neighbors ◽  
K Value ◽  
Training Samples ◽  
And Training ◽  
Validation Testing ◽  
Fold Cross Validation ◽  
Learning Data

An impression can be interpreted as a psychological feeling toward a product and it plays an important role in decision making. Therefore, the understanding of the data in the domain of impressions will be very useful. This research had the objective of knowing the performance of K-Nearest Neighbors method to classify endek image impression using K-Fold Cross Validation method. The images were taken from 3 locations, namely CV. Artha Dharma, Agung Bali Collection, and Pengrajin Sri Rejeki. To get the image impression was done by consulting with an endek expert named Dr. D.A Tirta Ray, M.Si. The process of data mining was done by using K-Nearest Neighbors Method which was a classification method to a set of data based on learning data that had been classified previously and to classify new objects based on attributes and training samples. K-Fold Cross Validation testing obtained accuracy of 91% with K value in K-Nearest Neighbors of 3, 4, 7, 8.

scholarly journals Analisis Perbandingan Algoritma SVM, KNN, dan CNN untuk Klasifikasi Citra Cuaca

2021 ◽  
Vol 8 (2) ◽  
pp. 311
Author(s):  
Mohammad Farid Naufal
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Computer Vision ◽  
Support Vector Machine ◽  
Convolutional Neural Network ◽  
Cross Validation ◽  
Nearest Neighbors ◽  
Support Vector ◽  
Classification Algorithms ◽  
K Nearest Neighbors

<p class="Abstrak">Cuaca merupakan faktor penting yang dipertimbangkan untuk berbagai pengambilan keputusan. Klasifikasi cuaca manual oleh manusia membutuhkan waktu yang lama dan inkonsistensi. <em>Computer vision</em> adalah cabang ilmu yang digunakan komputer untuk mengenali atau melakukan klasifikasi citra. Hal ini dapat membantu pengembangan <em>self autonomous machine</em> agar tidak bergantung pada koneksi internet dan dapat melakukan kalkulasi sendiri secara <em>real time</em>. Terdapat beberapa algoritma klasifikasi citra populer yaitu K-Nearest Neighbors (KNN), Support Vector Machine (SVM), dan Convolutional Neural Network (CNN). KNN dan SVM merupakan algoritma klasifikasi dari <em>Machine Learning</em> sedangkan CNN merupakan algoritma klasifikasi dari Deep Neural Network. Penelitian ini bertujuan untuk membandingkan performa dari tiga algoritma tersebut sehingga diketahui berapa gap performa diantara ketiganya. Arsitektur uji coba yang dilakukan adalah menggunakan 5 cross validation. Beberapa parameter digunakan untuk mengkonfigurasikan algoritma KNN, SVM, dan CNN. Dari hasil uji coba yang dilakukan CNN memiliki performa terbaik dengan akurasi 0.942, precision 0.943, recall 0.942, dan F1 Score 0.942.</p><p class="Abstrak"> </p><p class="Abstrak"><em><strong>Abstract</strong></em></p><p class="Abstract"><em>Weather is an important factor that is considered for various decision making. Manual weather classification by humans is time consuming and inconsistent. Computer vision is a branch of science that computers use to recognize or classify images. This can help develop self-autonomous machines so that they are not dependent on an internet connection and can perform their own calculations in real time. There are several popular image classification algorithms, namely K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Convolutional Neural Network (CNN). KNN and SVM are Machine Learning classification algorithms, while CNN is a Deep Neural Networks classification algorithm. This study aims to compare the performance of that three algorithms so that the performance gap between the three is known. The test architecture is using 5 cross validation. Several parameters are used to configure the KNN, SVM, and CNN algorithms. From the test results conducted by CNN, it has the best performance with 0.942 accuracy, 0.943 precision, 0.942 recall, and F1 Score 0.942.</em></p><p class="Abstrak"><em><strong><br /></strong></em></p>

scholarly journals CGENet: A Deep Graph Model for COVID-19 Detection Based on Chest CT

Biology ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 33
Author(s):  
Si-Yuan Lu ◽  
Zheng Zhang ◽  
Yu-Dong Zhang ◽  
Shui-Hua Wang
Keyword(s):  
Extreme Learning Machine ◽  
Cross Validation ◽  
Graph Model ◽  
Chest Ct ◽  
Selection Algorithm ◽  
Efficient Tool ◽  
K Nearest Neighbors ◽  
Average Accuracy ◽  
Learning Machine ◽  
Fold Cross Validation

Accurate and timely diagnosis of COVID-19 is indispensable to control its spread. This study proposes a novel explainable COVID-19 diagnosis system called CGENet based on graph embedding and an extreme learning machine for chest CT images. We put forward an optimal backbone selection algorithm to select the best backbone for the CGENet based on transfer learning. Then, we introduced graph theory into the ResNet-18 based on the k-nearest neighbors. Finally, an extreme learning machine was trained as the classifier of the CGENet. The proposed CGENet was evaluated on a large publicly-available COVID-19 dataset and produced an average accuracy of 97.78% based on 5-fold cross-validation. In addition, we utilized the Grad-CAM maps to present a visual explanation of the CGENet based on COVID-19 samples. In all, the proposed CGENet can be an effective and efficient tool to assist COVID-19 diagnosis.

scholarly journals Analisis Perbandingan Algoritma Klasifikasi Citra Chest X-ray Untuk Deteksi Covid-19

Teknika ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 96-103
Author(s):  
Mohammad Farid Naufal ◽  
Selvia Ferdiana Kusuma ◽  
Kevin Christian Tanus ◽  
Raynaldy Valentino Sukiwun ◽  
Joseph Kristiano ◽  
...  
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Cross Validation ◽  
Nearest Neighbor ◽  
Nearest Neighbors ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
K Nearest Neighbors ◽  
X Ray ◽  
Chest X Ray

Kondisi pandemi global Covid-19 yang muncul diakhir tahun 2019 telah menjadi permasalahan utama seluruh negara di dunia. Covid-19 merupakan virus yang menyerang organ paru-paru dan dapat mengakibatkan kematian. Pasien Covid-19 banyak yang telah dirawat di rumah sakit sehingga terdapat data citra chest X-ray paru-paru pasien yang terjangkit Covid-19. Saat ini sudah banyak peneltian yang melakukan klasifikasi citra chest X-ray menggunakan Convolutional Neural Network (CNN) untuk membedakan paru-paru sehat, terinfeksi covid-19, dan penyakit paru-paru lainnya, namun belum ada penelitian yang mencoba membandingkan performa algoritma CNN dan machine learning klasik seperti Support Vector Machine (SVM), dan K-Nearest Neighbor (KNN) untuk mengetahui gap performa dan waktu eksekusi yang dibutuhkan. Penelitian ini bertujuan untuk membandingkan performa dan waktu eksekusi algoritma klasifikasi K-Nearest Neighbors (KNN), Support Vector Machine (SVM), dan CNN  untuk mendeteksi Covid-19 berdasarkan citra chest X-Ray. Berdasarkan hasil pengujian menggunakan 5 Cross Validation, CNN merupakan algoritma yang memiliki rata-rata performa terbaik yaitu akurasi 0,9591, precision 0,9592, recall 0,9591, dan F1 Score 0,959 dengan waktu eksekusi rata-rata sebesar 3102,562 detik.

scholarly journals Collaborative Classification Approach for Airline Tweets Using Sentiment Analysis

2021 ◽  
Vol 12 (3) ◽  
pp. 3597-3603
Author(s):  
M.Veera Kumari Et.al
Keyword(s):  
Sentiment Analysis ◽  
Cross Validation ◽  
Majority Voting ◽  
Quality Of Services ◽  
Classification Methods ◽  
K Nearest Neighbors ◽  
Classification Techniques ◽  
Improve Accuracy ◽  
Fold Cross Validation

In the world there are so many airline services which facilitate different airline facilities for their customers. Those airline services may satisfy or may not satisfy their customers. Customers cannot express their comments immediately, so airline services provide the twitter blog to give the feedback on their services. Twitter has been increased to develop the quality of services[4]. This paper develop the different classification techniques to improve accuracy for sentiment analysis. The tweets of services are classified into three polarities such as positive, negative and neutral. Classification methods are Random forest(RF), Logistic Regression(LR), K-Nearest Neighbors(KNN), Naïve Baye’s(NB), Decision Tree(DTC), Extreme Gradient Boost(XGB), merging of (two, three and four) classification techniques with majority Voting Classifier, AdaBoost measuring the accuracy achieved by the function using 20-fold and 30-fold cross validation was compassed in the validation phase. In this paper proposes a new ensemble Bagging approach for different classifiers[10]. The metrics of sentiment analysis precision, recall, f1-score, micro average, macro average and accuracy are discovered for all above mentioned classification techniques. In addition average predictions of classifiers and also accuracy of average predictions of classifiers was calculated for getting good quality of services. The result describes that bagging classifiers achieve better accuracy than non-bagging classifiers.

scholarly journals Implementasi Web Service Dan Analisis Kinerja Algoritma Klasifikasi Data Mining Untuk Memprediksi Diabetes Mellitus

2017 ◽  
Vol 8 (2) ◽  
pp. 701
Author(s):  
Doni Setyawan ◽  
Agustinus Suradi
Keyword(s):  
Diabetes Mellitus ◽  
Data Mining ◽  
Random Forest ◽  
Web Service ◽  
Cross Validation ◽  
World Health ◽  
K Nearest Neighbors ◽  
Naive Bayesian ◽  
Naïve Bayesian ◽  
Fold Cross Validation

Salah satu penyakit yang ditimbulkan akibat kesalahan pola gaya hidup adalah Diabetes Mellitus (DM). Gejala penyakit diabetes sering dilalaikan oleh kebanyakan orang, sehingga mereka cenderung untuk mengabaikannya dan tidak mau melakukan medical check up. Di Indonesia jumlah penderita DM terus mengalami peningkatan dari tahun ke tahun. World Health Organization (WHO) memperkirakan jumlah penderita DM tipe 2 di Indonesia akan mengalami peningkatan secara signifikan hingga 21,3 juta jiwa pada tahun 2030 mendatang. Ternyata dengan bantuan ilmu data mining, data pasien diabetes dapat digunakan untuk memprediksi apakah sesorang positif diabetes atau tidak. Tahapan awal dilakukan preprocessing data untuk menangani missing dan non numeric values. Kemudian traning dan testing menggunakan k-fold cross validation dengan algoritma K-Nearest Neighbors (KNN), random forest dan naive bayesian. Pengujian dilakukan dengan menghitung accuracy, sensitivity dan specificity. Dari hasil uji 10-fold cross validation diperoleh rata-rata akurasi tertinggi ketika menggunakan naive bayesian yaitu 75,65%, sedangkan KNN 75,53% dan random forest 73,69%. Perhitungan sensitivity dan specificity dengan membagi 786 data menjadi 594 data training dan 192 data testing. Untuk KNN diperoleh sensitivity 56,72% dan specificity 78,68%, random forest diperoleh sensitivity 53,73% dan specificity 86,4%, sedangkan naive bayesian diperoleh sensitivity 62,69% dan specificity 84%. Implementasi restful web service diterapkan pada model dengan akurasi tertinggi yaitu naive bayesian dengan format json sebagai return value.

scholarly journals PERBANDINGAN KINERJA MUTUAL K-NEAREST NEIGHBOR (MKNN) DAN K-NEAREST NEIGHBOR (KNN) DALAM ANALISIS KLASIFIKASI KELAYAKAN KREDIT

2019 ◽  
Vol 8 (3) ◽  
pp. 366-376
Author(s):  
Annisa Sugesti ◽  
Moch. Abdul Mukid ◽  
Tarno Tarno
Keyword(s):  
Cross Validation ◽  
Nearest Neighbor ◽  
Nearest Neighbors ◽  
Classification Performance ◽  
K Nearest Neighbor ◽  
Classification Technique ◽  
Optimal Value ◽  
Fold Cross Validation

Credit feasibility analysis is important for lenders to avoid the risk among the increasement of credit applications. This analysis can be carried out by the classification technique. Classification technique used in this research is instance-based classification. These techniques tend to be simple, but are very dependent on the determination of  K values. K is number of nearest neighbor considered for class classification of new data. A small value of K is very sensitive to outliers. This weakness can be overcome using an algorithm that is able to handle outliers, one of them is Mutual K-Nearest Neighbor (MKNN). MKNN removes outliers first, then predicts new observation classes based on the majority class of their mutual nearest neighbors. The algorithm will be compared with KNN without outliers. The model is evaluated by 10-fold cross validation and the classification performance is measured by Gemoetric-Mean of sensitivity and specificity. Based on the analysis the optimal value of K is 9 for MKNN and 3 for KNN, with the highest G-Mean produced by KNN is equal to 0.718, meanwhile G-Mean produced by MKNN is 0.702. The best alternative to classifying credit feasibility in this study is K-Nearest Neighbor (KNN) algorithm with K=3.Keywords: Classification, Credit, MKNN, KNN, G-Mean.

Combination of Support Vector Machine and K-Fold cross-validation for prediction of long-term degradation of the compressive strength of marine concrete

2018 ◽  
Vol 1 (1) ◽  
pp. 120-130 ◽  
Author(s):  
Chunxiang Qian ◽  
Wence Kang ◽  
Hao Ling ◽  
Hua Dong ◽  
Chengyao Liang ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Environmental Factors ◽  
Cross Validation ◽  
Concrete Strength ◽  
Simulation Method ◽  
Support Vector ◽  
Svm Model ◽  
Artificial Neural Network Ann ◽  
Influence Degree ◽  
Fold Cross Validation

Support Vector Machine (SVM) model optimized by K-Fold cross-validation was built to predict and evaluate the degradation of concrete strength in a complicated marine environment. Meanwhile, several mathematical models, such as Artificial Neural Network (ANN) and Decision Tree (DT), were also built and compared with SVM to determine which one could make the most accurate predictions. The material factors and environmental factors that influence the results were considered. The materials factors mainly involved the original concrete strength, the amount of cement replaced by fly ash and slag. The environmental factors consisted of the concentration of Mg2+, SO42-, Cl-, temperature and exposing time. It was concluded from the prediction results that the optimized SVM model appeared to perform better than other models in predicting the concrete strength. Based on SVM model, a simulation method of variables limitation was used to determine the sensitivity of various factors and the influence degree of these factors on the degradation of concrete strength.

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