Application of support vector machine for prediction and classification

Kramar, Vadim; Alchakov, Vasiliy; Dushko, Veronika; Kramár, Tomáš

doi:10.1088/1742-6596/1015/3/032070

Cited by 7 publications

(2 citation statements)

References 5 publications

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“…Cara untuk menemukan hyperplane adalah dengan mencari titik yang maksimum dan menghitung margin hyperplane. Margin merupakan jarak antara hyperplane dengan data yang paling dekat dari setiap kelas, sedangkan data yang terdekat dengan hyperplane disebut support vector (Kramar et al, 2018).…”

Section: Support Vector Machineunclassified

Komparasi Metode Support Vector Machine (Svm), K-Nearest Neighbors (Knn), Dan Random Forest (Rf) Untuk Prediksi Penyakit Gagal Jantung

Adi

Wintarti

2022

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Jantung adalah organ yang mempunyai peranan penting dalam kelangsungan hidup manusia karena fungsinya untuk mendistribusikan darah dari paru-paru ke seluruh bagian tubuh, yang dimana darah tersebut mengandung banyak sekali oksigen sehingga dapat membantu proses metabolisme di dalam tubuh manusia. Maka dari itu, organ jantung perlu dilindungi, dirawat, dan dijaga kondisinya untuk mencegah kerusakan pada jantung yang mengakibatkan penyakit gagal jantung. Begitu banyak orang yang meninggal karena penyakit gagal jantung, sehingga harus ada penelitian yang terbarukan untuk memprediksi penyakit gagal jantung tersebut. Peranan Artificial Intelligence (AI) dalam mendeteksi gagal jantung pada penelitian ini bertujuan untuk membandingkan prediksi akurasi dari tiga metode yang dipakai oleh peneliti, yaitu metode Support Vector Machine (SVM), K-Nearest Neighbors (KNN), dan Random Forest (RF). Dataset diambil dari laman UCI Machine Learning dengan judul Heart Failure Clinical Records Dataset. Akurasi paling tinggi yang dapat dihasilkan adalah menggunakan metode SVM dan RF dimana menghasilkan akurasi yang bernilai sama, yaitu 97% dengan rincian metode SVM menggunakan parameter C = 1, gamma = 0.01, kernel = linear dan dalam total waktu running program selama 2.82 detik sedangkan metode RF menggunakan parameter n_estimators = 30, random_state = 0, dan dalam total waktu running program selama 7.29 detik. Lalu untuk metode KNN menghasilkan akurasi yang bernilai 93%, dengan menggunakan parameter n_neighbors = 20 dan dalam total waktu running program selama 0.60 detik. Pada penelitian ini, peneliti juga membuat program Prediksi Gagal Jantung dengan Graphical User Interface (GUI) menggunakan ketiga metode yang telah diuji akurasinya.

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Section: Support Vector Machineunclassified

Komparasi Metode Support Vector Machine (Svm), K-Nearest Neighbors (Knn), Dan Random Forest (Rf) Untuk Prediksi Penyakit Gagal Jantung

Adi

Wintarti

2022

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“…SVM, which originated from the new statistical theory [31][32][33] in the 1990s, is a commonly used model in machine learning. It can not only be used for the classification of nonlinear samples but also be applied to the field of Mathematical Problems in Engineering regression prediction.…”

Section: Stone Cultural Relics Crack Predictionmentioning

confidence: 99%

Crack Prediction Based on Wavelet Correlation Analysis Least Squares Support Vector Machine for Stone Cultural Relics

Liu

et al. 2021

Mathematical Problems in Engineering

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Preventive protection of cultural relics is to make use of all the science and technology beneficial to the research and protection of archaeological heritage to predict the disease of cultural relics. The existing preventive cultural relics protection system has made some achievements in environmental monitoring, but the analysis and utilization of large data of cultural relics are still insufficient. In this paper, under the idea of multisource information fusion, a least squares support vector machine regression method based on multivariate time series wavelet correlation analysis is proposed to achieve accurate crack prediction of stone cultural relics. Firstly, the correlation of multivariate time series of stone cultural relics are quantitatively analyzed and the validity of characteristic variables of the crack is discriminated by wavelet correlation analysis; then, a least squares support vector machine prediction model is constructed based on the correlation obtained from the analysis; finally, the good performance of the method is verified by using the environmental monitoring data of the rock mass fracture in the North Qianfo Cliff of Dafo Temple in Binzhou City of Shaanxi Province. The experimental results show that the proposed method is more effective than the traditional backpropagation neural network, support vector machine, and relevance vector machine regression methods. This method is universal and easy to implement for multisource data prediction of nonmovable cultural relics diseases. It provides a scientific theoretical reference for the preventive protection of cultural relics.

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The Computer Vision System for a Crewless Boat

Kramar,

Alchakov

2024

2024 International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM)

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Application of support vector machine for prediction and classification

Cited by 7 publications

References 5 publications

Komparasi Metode Support Vector Machine (Svm), K-Nearest Neighbors (Knn), Dan Random Forest (Rf) Untuk Prediksi Penyakit Gagal Jantung

Komparasi Metode Support Vector Machine (Svm), K-Nearest Neighbors (Knn), Dan Random Forest (Rf) Untuk Prediksi Penyakit Gagal Jantung

Crack Prediction Based on Wavelet Correlation Analysis Least Squares Support Vector Machine for Stone Cultural Relics

The Computer Vision System for a Crewless Boat

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