Selection of Specialization Class Using Support Vector Machine (SVM) Method in Sekolah Menengah Atas Negeri 1 Ambon

The curriculum is a plan to form the abilities and character of children based on a standard. One of its form is the division of specialization classes at the high school level. The 2013 curriculum emphasizes that all students in Indonesia can practice their abilities based on their interests and talents, so students no longer choose majors but choose abilities (interests) in them specialize. This research uses the Support Vector Machine (SVM) method in specialization Decision Making System (DMS) at SMA Negeri 1 Ambon. By using the motivating acceptance factors and student selection as input data, this SVM method that processed with MATLAB Software produces a Classification of Interest Class with an accuracy rate more than 95%.

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Intelligent Bearing Diagnostics Using Wavelet Support Vector Machine

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.493.337 ◽

2014 ◽

Vol 493 ◽

pp. 337-342 ◽

Cited By ~ 1

Author(s):

Achmad Widodo ◽

I. Haryanto ◽

T. Prahasto

Keyword(s):

Support Vector Machine ◽

Input Data ◽

Intelligent System ◽

Feature Space ◽

Rolling Element Bearing ◽

Support Vector ◽

Fault Diagnostics ◽

Standard Data ◽

Rolling Element

This paper deals with implementation of intelligent system for fault diagnostics of rolling element bearing. In this work, the proposed intelligent system was basically created using support vector machine (SVM) due to its excellent performance in classification task. Moreover, SVM was modified by introducing wavelet function as kernel for mapping input data into feature space. Input data were vibration signals acquired from bearings through standard data acquisition process. Statistical features were then calculated from bearing signals, and extraction of salient features was conducted using component analysis. Results of fault diagnostics are shown by observing classification of bearing conditions which gives plausible accuracy in testing of the proposed system.

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Research of Diver Sonar Image Recognition Based on Support Vector Machine

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.785-786.1437 ◽

2013 ◽

Vol 785-786 ◽

pp. 1437-1440 ◽

Cited By ~ 2

Author(s):

Ke Li ◽

Chong Lun Li ◽

Wei Zhang

Keyword(s):

Support Vector Machine ◽

Support Vector ◽

Small Object ◽

Accuracy Rate ◽

Machine Method ◽

Support Vector Machine Method ◽

Feature Parameters ◽

Sonar Images ◽

The Right ◽

Selection Of

To recognize small diver target from the dim special diver sonar images accurately, the Support Vector Machine method is used as classifier. According to the main characteristics of diver, five feature parameters, including Average-scale, Velocity, Shape, Direction, Included angle, are chosen as the input of characteristics vectors to train the net. And then the testing images are classified and identified. The experimental results show that accuracy rate of recognition reaches 94.5% for as many as 200 testing images. The experiment indicates that small object recognition from complex sonar images based on the right selection of feature parameters is of good performance by using the SVM method as well as good engineering foreground.

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Rough Support Vector Machine for Classification with Interval and Incomplete Data

Journal of Artificial Intelligence and Soft Computing Research ◽

10.2478/jaiscr-2020-0004 ◽

2020 ◽

Vol 10 (1) ◽

pp. 47-56 ◽

Cited By ~ 2

Author(s):

Robert K. Nowicki ◽

Konrad Grzanek ◽

Yoichi Hayashi

Keyword(s):

Support Vector Machine ◽

Rough Sets ◽

Incomplete Data ◽

Input Data ◽

Missing Values ◽

Classification Scheme ◽

Equivalence Classes ◽

Support Vector ◽

Positive Boundary

AbstractThe paper presents the idea of connecting the concepts of the Vapnik’s support vector machine with Pawlak’s rough sets in one classification scheme. The hybrid system will be applied to classifying data in the form of intervals and with missing values [1]. Both situations will be treated as a cause of dividing input space into equivalence classes. Then, the SVM procedure will lead to a classification of input data into rough sets of the desired classes, i.e. to their positive, boundary or negative regions. Such a form of answer is also called a three–way decision. The proposed solution will be tested using several popular benchmarks.

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OPTIMALISASI SUPPORT VEKTOR MACHINE (SVM) UNTUK KLASIFIKASI TEMA TUGAS AKHIR BERBASIS K-MEANS

Telematika ◽

10.31315/telematika.v13i2.1722 ◽

2017 ◽

Vol 13 (2) ◽

pp. 59

Author(s):

Oman Somantri ◽

Slamet Wiyono ◽

Dairoh Dairoh

Keyword(s):

Support Vector Machine ◽

Decision Support ◽

Text Mining ◽

Support Vector ◽

Accuracy Rate ◽

Study Program ◽

Policy Makers ◽

Final Project ◽

Mining Algorithms

The difficulty in determining the classification of students final project theme often experienced by each college. The purpose of this study is to provide a decision support for policy makers in the study program so that each student can be achieved in accordance with their own competence. From the research that has been done text mining algorithms using Support Vector Machine ( SVM ) and K -Means as the technology used was produced a better accuracy rate with an accuracy rate of 86.21 % when compared to the SVM without K -Means is 85 , 38 %

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Microlandform classification method for grid DEMs based on support vector machine

Arabian Journal of Geosciences ◽

10.1007/s12517-021-07596-0 ◽

2021 ◽

Vol 14 (13) ◽

Author(s):

Fangbin Zhou ◽

Lianhua Zou ◽

Xuejun Liu ◽

Yunfei Zhang ◽

Fanyi Meng ◽

...

Keyword(s):

Support Vector Machine ◽

Classification Method ◽

Support Vector ◽

Svm Classifier ◽

Regular Grid ◽

Classification Problems ◽

Accuracy Rate ◽

Digital Elevation ◽

Grid Based

AbstractMicrolandform classification of grid digital elevation models (DEMs) is the foundation of digital landform refinement applications. To solve the shortcomings of the traditional regular grid DEM microlandform classification method, including low automation and incomplete classification results, a support vector machine (SVM) classifier was designed for grid DEM microlandform classification, and an automatic grid-based DEM microlandform classification method based on the SVM method was created. The experiment applies the SVM-based grid DEM microlandform classification method to identify different hill positions, namely, the summit, shoulder, back-slope, foot-slope, toe-slope, and alluvium. The results show that this method is most efficient in identifying the toe-slope, with an accuracy rate of 99.60%, and least efficient in identifying the foot-slope, with an accuracy rate of 98.18%. The kappa coefficient and model evaluation index F1-score verify that the method and model are reliable when applied to grid DEM microlandform classification problems.

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