BibTex Citation Data :
@article{JSINBIS33732, author = {Mhd Furqan and Rakhmat Kurniawan and Kiki HP}, title = {Evaluasi Performa Support Vector Machine Classifier Terhadap Penyakit Mental}, journal = {JSINBIS (Jurnal Sistem Informasi Bisnis)}, volume = {10}, number = {2}, year = {2020}, keywords = {Autism; Bipolar; Classification; Schizophrenia; Support Vector Machine}, abstract = { Expression of genes found in the brains of autism, bipolar, and schizophrenia patients identified as overlapping. The overlap is a state in which the values of genes are similar. This paper aims to determine the best performance of support vector machines algorithm in classifying autism, bipolar, and schizophrenia based on the expression of genes using genome-wide association studies data. Using three support vector machine kernels, this study evaluates the performance of gaussian, laplacian, and sigmoid for genome-wide association studies datasets. The datasets were obtained from Psychiatric Genomics Consortium publications, where 660 data were taken with each disorder consisting of 220 data. This study proposes an optimal kernel for one-against-one and one-against-all multiclass support vector machine, and the performance is evaluated using accuracy. The study results show that the Gaussian kernel has the best accuracy performance compared to other support vector machines kernels in classifying genome-wide association studies data of autism, bipolar, and schizophrenia as early diagnosis. }, issn = {2502-2377}, pages = {203--210} doi = {10.21456/vol10iss2pp203-210}, url = {https://ejournal.undip.ac.id/index.php/jsinbis/article/view/33732} }
Refworks Citation Data :
Expression of genes found in the brains of autism, bipolar, and schizophrenia patients identified as overlapping. The overlap is a state in which the values of genes are similar. This paper aims to determine the best performance of support vector machines algorithm in classifying autism, bipolar, and schizophrenia based on the expression of genes using genome-wide association studies data. Using three support vector machine kernels, this study evaluates the performance of gaussian, laplacian, and sigmoid for genome-wide association studies datasets. The datasets were obtained from Psychiatric Genomics Consortium publications, where 660 data were taken with each disorder consisting of 220 data. This study proposes an optimal kernel for one-against-one and one-against-all multiclass support vector machine, and the performance is evaluated using accuracy. The study results show that the Gaussian kernel has the best accuracy performance compared to other support vector machines kernels in classifying genome-wide association studies data of autism, bipolar, and schizophrenia as early diagnosis.
Note: This article has supplementary file(s).
Article Metrics:
Last update:
Penulis yang mengirimkan naskah harus memahami dan menyetujui bahwa jika diterima untuk dipublikasikan, hak cipta dari artikel adalah milik JSINBIS dan Universitas Diponegoro sebagai penerbit jurnal.Hak cipta (copyright) meliputi hak eksklusif untuk mereproduksi dan memberikan artikel dalam semua bentuk dan media, termasuk cetak ulang, foto, mikrofilm dan setiap reproduksi lain yang sejenis, serta terjemahan. Penulis mempunyai hak untuk hal-hal berikut:
JSINBIS dan Universitas Diponegoro serta Editor melakukan segala upaya untuk memastikan bahwa tidak ada data, pendapat atau pernyataan yang salah atau menyesatkan yang dipublikasikan di jurnal ini. Isi artikel yang diterbitkan di JSINBIS adalah tanggung jawab tunggal dan eksklusif dari masing-masing penulis.
View My Stats This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.