IMPLEMENTASI METODE SUPPORT VECTOR MACHINE UNTUK INDENTIFIKASI PENYAKIT DAUN TANAMAN KUBIS

Tanaman kubis merupakan salah satu sayuran yang banyak dikonsumsi masyarakat, dalam produksi bibit tanaman kubis sering mengalami hambatan karena serangan hama. Salah satu komponen dalam keberhasilan produksi kubis adalah masa perkembangan bibit, yang dikhawatirkan banyak mendapat serangan hama. Dalam penelitian ini pengolahan citra digital digunakan untuk mengidentifikasi hama/penyakit terhadap bibit tanaman kubis. Penelitian ini dimulai dengan pengumpulan citra daun tanaman kubis. Tahapan selanjutnya adalah pre-processing citra dengan menghilangkan background dari citra masukan kemudian dilakukan proses grayscale untuk mendapatkan nilai yang akan digunakan untuk proses selanjutnya. Hasil tersebut kemudian akan dihitung dengan menggunakan metode Support Vector Machine (SVM). Proses training dilakukan dengan Sequential Training yang kemudian dilakukan proses testing. Hasil dari klasifikasi dipengaruhi oleh proses segmentasi yang dilakukan serta input parameter yang digunakan saat proses training. Dari hasil pengujian menunjukkan rata-rata akurasi hasil klasifikasi mencapai 80.55%.

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Sequential training of Support Vector Machine

2008 9th Symposium on Neural Network Applications in Electrical Engineering ◽

10.1109/neurel.2008.4685556 ◽

2008 ◽

Author(s):

Ivica M. Markovic ◽

Branimir T. Todorovic

Keyword(s):

Support Vector Machine ◽

Support Vector ◽

Sequential Training

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Optimising the modelling of eutrophication for Bohai Bay based on the cellular automata – support vector machine method

Journal of Hydroinformatics ◽

10.2166/hydro.2014.098 ◽

2014 ◽

Vol 16 (5) ◽

pp. 1125-1141

Author(s):

Zheng Dongsheng ◽

Xiang Xianquan ◽

Tao Jianhua

Keyword(s):

Support Vector Machine ◽

Cellular Automata ◽

Marine Environment ◽

Input Parameter ◽

Coupled Model ◽

Remote Sensing Data ◽

Support Vector ◽

Bohai Bay ◽

Machine Method ◽

Basic Model

With the development of marine economy, eutrophication has become one of the key issues in the marine environment. In this paper, a eutrophication model for Bohai Bay based on the cellular automata-support vector machine (CA-SVM) has been established by applying the soft computing approach with a large quantity of remote sensing data to the marine environment. In order to optimise the coupled model further, two main tasks have been done in this study. First, to choose reasonable influence factors as the input parameters of the model, nine series of training and simulation exercises were conducted based on nine different types of input parameter combinations. A reasonable input parameter combination was selected, and the eutrophication model (the basic model) was established by the comparative analysis of the simulation results. Second, according to Shelford's Law of Tolerance, an optimised model was developed. It is combined of nine special models and each model corresponds to a stage of sea surface temperature and the chlorophyll-a concentration, respectively. The comparison between the optimised model and the basic model indicated that prediction accuracy was improved by the optimised model. By this study, it can be observed this model could provide a scientific basis for the prediction and management of the aquatic environment of Bohai Bay.

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Application of Support Vector Machine and Gene Expression Programming on Tropospheric ozone Prognosticating for Tehran Metropolitan

Civil Engineering Journal ◽

10.28991/cej-030984 ◽

2017 ◽

Vol 3 (8) ◽

pp. 557 ◽

Cited By ~ 14

Author(s):

Vahid Mehdipour ◽

Mahsa Memarianfard

Keyword(s):

Gene Expression ◽

Support Vector Machine ◽

Tropospheric Ozone ◽

Meteorological Parameters ◽

Gene Expression Programming ◽

Input Parameter ◽

Developed Countries ◽

Support Vector ◽

Effective Parameters ◽

Measurement Tools

Air pollution became fatal issue for humanity and all environment and developed countries unanimously allocated vast investments on monitoring and researches about air pollutants. Soft computing as a novel way for pollutants prediction can be used for measurement tools calibration which can coincidently decrease the expenditures and enhance their ability to adapt quickly. In this paper support vector machine (SVM) and gene expression programming (GEP) as two powerful approaches with reliable results in previous studies, used to predict tropospheric ozone in Tehran metropolitan by using the photochemical precursors and meteorological parameters as predictors. In a comparison between the two approaches, the best model of SVM gave superior results as it depicted the RMSE= 0.0774 and R= 0.8459 while these results of gene expression programming, respectively, are 0.0883 and 0.7938. Sensitivity of O3 against photochemical precursors and meteorological parameters and also for every input parameter, has been analysed discreetly and the gained results imply that PM2.5, PM10, temperature, CO and NO2 are the most effective parameters for O3 values tolerances. For SVM, several kernel tricks used and the best appropriate kernel selected due to its result. Nonetheless, gamma and sin2 values varied for every kernel and in the last radial basis function kernel opted as the best trick in this study. Finally, the best model of both applications revealed, and the resulted models evaluated as reliable and acceptable.

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The Research on the Method of Feature Selection in Support Vector Machine Based Entropy

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.354-355.1192 ◽

2011 ◽

Vol 354-355 ◽

pp. 1192-1196

Author(s):

Xiao Yan Zhu ◽

Xi Tian ◽

Xiao Xun Zhu

Keyword(s):

Support Vector Machine ◽

Feature Selection ◽

Fault Diagnosis ◽

Input Parameter ◽

Rotating Machinery ◽

Support Vector ◽

Intelligent Fault Diagnosis ◽

State Recognition ◽

Fault State

The large rotating machinery functioning of the rotor is one of the most important issues. It has great significance to identify the fault early and implement intelligent fault diagnosis. However there is a big nonlinear about large rotating machinery and has less fault samples. This led great difficulties for feature selection and state recognition. Based on Entropy in feature selection, we extract each intrinsic mode’s function energy as eigenvector and make them for input parameter of the support vector machine (SVM) to fault diagnosis. The experiment shows that this method can classify the fault state, and completed intelligent fault diagnosis.

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Support vector machine? – not only for MRI-images

10.1055/s-0039-3403050 ◽

2020 ◽

Author(s):

V Vasilevska ◽

K Schlaaf ◽

H Dobrowolny ◽

G Meyer-Lotz ◽

HG Bernstein ◽

...

Keyword(s):

Support Vector Machine ◽

Support Vector

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KLASIFIKASI SMS SPAM MENGGUNAKAN SUPPORT VECTOR MACHINE

Jurnal Pilar Nusa Mandiri ◽

10.33480/pilar.v15i2.693 ◽

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.

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