scholarly journals Peramalan Harga Saham Menggunakan Metode Support Vector Machine (SVM)

2021 ◽  
Vol 3 (1) ◽  
pp. 61-74
Author(s):  
Dony Abdul Chalid ◽  
Vincentius Ryan Cokrodiharjo
Keyword(s):  
Support Vector Machine ◽  
Support Vector ◽  
Model Support

Penelitian ini bertujuan untuk menganalisis ketepatan model Support Verctor Machine (SVM) dalam memprediksi harga saham. Kombinasi dari 28 periode perkiraan dan 30 periode input indikator teknis digunakan. Data transaksi saham dari 31 perusahaan yang terdaftar di Bursa Efek Indonesia dan aktif diperdagangkan antara Maret 2006 dan Februari 2018 digunakan. Hasil menunjukkan bahwa kinerja sistem tertinggi tidak terjadi ketika indikator teknis periode input kira-kira sama dengan periode perkiraan. Meskipun demikian, hasil kinerja saham perusahaan berbeda-beda. Namun model prediksi dengan SVM memberikan keuntungan yang lebih besar dibandingkan dengan strategi buy and hold.

scholarly journals Indonesian Stock Prediction using Support Vector Machine (SVM)

2018 ◽  
Vol 164 ◽  
pp. 01031 ◽  
Author(s):  
Murtiyanto Santoso ◽  
Raymond Sutjiadi ◽  
Resmana Lim
Keyword(s):  
Support Vector Machine ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Prediction Method ◽  
Gaussian Mixture ◽  
Machine Intelligence ◽  
Support Vector ◽  
Predicted Model ◽  
Python Programming ◽  
Model Support

This project is part of developing software to provide predictive information technology-based services artificial intelligence (Machine Intelligence) or Machine Learning that will be utilized in the money market community. The prediction method used in this early stages uses the combination of Gaussian Mixture Model and Support Vector Machine with Python programming. The system predicts the price of Astra International (stock code: ASII.JK) stock data. The data used was taken during 17 yr period of January 2000 until September 2017. Some data was used for training/modeling (80 % of data) and the remainder (20 %) was used for testing. An integrated model comprising Gaussian Mixture Model and Support Vector Machine system has been tested to predict stock market of ASII.JK for l d in advance. This model has been compared with the Market Cummulative Return. From the results, it is depicts that the Gaussian Mixture Model-Support Vector Machine based stock predicted model, offers significant improvement over the compared models resulting sharpe ratio of 3.22.

scholarly journals Classification of Spam Text using SVM

2021 ◽  
Vol 23 (08) ◽  
pp. 616-624
Author(s):  
Gaddam Akhil Reddy ◽  
◽  
Dr. B. Indira Reddy ◽  
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Text Messages ◽  
Training Data ◽  
Support Vector ◽  
Spam Detection ◽  
Advantages And Disadvantages ◽  
Machine Learning Model ◽  
Model Support

The necessity for spam detection is particularly pertinent nowadays, as there is no quality control over social media, and users have the ability to distribute unverified material, therefore facilitating fraud and deceit. Spam detection can aid in the prevention of such fraud. This scenario has developed mostly as a result of the distribution of disparate, unconfirmed information via shopping websites, emails, and text messages (SMS). There are several ways of categorising and identifying spam. Each of them has certain advantages and disadvantages. The machine learning model “Support Vector Machine” is employed to detect spam in this case. SVM is a basic concept: the method proposes a line or hyperplane to classify the data. The model can categorise any type of text into a given category after being fed a set of labelled training data for each category.

scholarly journals A Novel Support Vector Machine with Globality-Locality Preserving

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Cheng-Long Ma ◽  
Yu-Bo Yuan
Keyword(s):  
Support Vector Machine ◽  
Recognition Rate ◽  
Support Vector ◽  
Data Sets ◽  
Data Space ◽  
Locality Preserving ◽  
Proposed Model ◽  
Improved Model ◽  
Model Support ◽  
Learning Data

Support vector machine (SVM) is regarded as a powerful method for pattern classification. However, the solution of the primal optimal model of SVM is susceptible for class distribution and may result in a nonrobust solution. In order to overcome this shortcoming, an improved model, support vector machine with globality-locality preserving (GLPSVM), is proposed. It introduces globality-locality preserving into the standard SVM, which can preserve the manifold structure of the data space. We complete rich experiments on the UCI machine learning data sets. The results validate the effectiveness of the proposed model, especially on the Wine and Iris databases; the recognition rate is above 97% and outperforms all the algorithms that were developed from SVM.

Support vector machine? – not only for MRI-images

2020 ◽  
Author(s):  
V Vasilevska ◽  
K Schlaaf ◽  
H Dobrowolny ◽  
G Meyer-Lotz ◽  
HG Bernstein ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Support Vector

KLASIFIKASI SMS SPAM MENGGUNAKAN SUPPORT VECTOR MACHINE

2019 ◽  
Vol 15 (2) ◽  
pp. 275-280
Author(s):  
Agus Setiyono ◽  
Hilman F Pardede
Keyword(s):  
Data Mining ◽  
Support Vector Machine ◽  
Decision Tree ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
Spam Detection ◽  
Support Vector Machine Algorithm ◽  
Data Mining Techniques ◽  
To Receive

It is now common for a cellphone to receive spam messages. Great number of received messages making it difficult for human to classify those messages to Spam or no Spam.  One way to overcome this problem is to use Data Mining for automatic classifications. In this paper, we investigate various data mining techniques, named Support Vector Machine, Multinomial Naïve Bayes and Decision Tree for automatic spam detection. Our experimental results show that Support Vector Machine algorithm is the best algorithm over three evaluated algorithms. Support Vector Machine achieves 98.33%, while Multinomial Naïve Bayes achieves 98.13% and Decision Tree is at 97.10 % accuracy.

Classification of Fatigued Bills Based on a Support Vector Machine by Using the Amplitude Difference Features of Acoustic Signals

2011 ◽  
Vol 131 (8) ◽  
pp. 1495-1501
Author(s):  
Dongshik Kang ◽  
Masaki Higa ◽  
Hayao Miyagi ◽  
Ikugo Mitsui ◽  
Masanobu Fujita ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Acoustic Signals ◽  
Support Vector ◽  
Amplitude Difference

An Accurate Packer Identification Method Using Support Vector Machine

2014 ◽  
Vol E97.A (1) ◽  
pp. 253-263 ◽  
Author(s):  
Ryoichi ISAWA ◽  
Tao BAN ◽  
Shanqing GUO ◽  
Daisuke INOUE ◽  
Koji NAKAO
Keyword(s):  
Support Vector Machine ◽  
Support Vector ◽  
Identification Method
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