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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Sains dan Matematika
Eko Tjahjono
Departemen Matematika, Fakultas Sains Dan Teknologi, Universitas Airlangga
Astronomy Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Mathematics Physics

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Improving of classification accuracy of cyst and tumor using local polynomial estimator Nur Chamidah; Kinanti Hanugera Gusti; Eko Tjahjono; Budi Lestari
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 17, No 3: June 2019
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v17i3.12240

Abstract

Cyst and tumor in oral cavity are seriously noticed by health experts along with increasing death cases of oral cancer in developing country. Early detection of cyst and tumor using dental panoramic image is needed since its initial growth does not cause any complaints. Image processing is done by mean for distinguishing the classification of cyst and tumor. The results in previous studies about classification of cyst and tumor were done by using a mathematical computation approach namely supports vector machine method that have still not satisfied and have not been validated. Therefore, in this study we propose a method, i.e., nonparametric regression model based on local polynomial estimator that can be improve the classification accuracy of cyst and tumor on human dental panoramic image. By using the proposed method, we get the classification accuracy of cyst and tumor, i.e., 90.91% which is greater than those by using the support vector machine method, i.e., 76.67%. Also, in validation process we obtain that the nonparametric regression model approach gives a significant Press’s Q statistical testing value. So, we conclude that the nonparametric regression model approach improves the classification accuracy and gives better outcome to classify cyst and tumor using dental panoramic image than the support vector machine method.
Estimasi Model Regresi Panel Komponen Error Satu Arah dengan Metode Generalized Least Square Mahfudhotin Mahfudhotin; Suliyanto Suliyanto; Eko Tjahjono
Sains dan Matematika Vol. 6 No. 1 (2017): Oktober, Sains & Matematika
Publisher : Universitas Negeri Surabaya

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Abstract

Model regresi ­panel komponen error satu arah merupakan model regresi gabungan antara data cross-section dan data time series yang memiliki spesifikasi yang tepat untuk menggambarkan  individu secara random dari populasi yang besar. Tujuan dari penulisan tugas akhir ini adalah untuk mendapatkan estimasi model regresi panel komponen error satu arah menggunakan metode Generalized Least Square dan untuk menguji kesesuaian model menggunakan uji Hausman dan uji Multiple Lagrange. Hasil estimasi parameter regresi masih bergantung pada komponen delta_g^2  dan delta_a^2  sehingga untuk mengestimasinya dilakukan proses iterasi sampai diperoleh vektor parameter yang konvergen. Model regresi ­panel komponen error satu arah dapat dituliskan dalam bentuk persamaan. Penerapan model ini dilakukan pada data Produk Domestik Regional Bruto (PDRB) perkapita pada provinsi di Indonesia periode 2007 sampai 2010 sebagai variabel dependen , sedangkan variabel prediktornya meliputi : Tingkat Pengangguran Terbuka, Investasi Penanaman Modal Asing, Investasi Penanaman Modal Dalam Negeri, Jumlah Angkatan Kerja, dan Pengeluaran Konsumsi Rumah Tangga. Model ini mempunyai nilai  R^2 = 0.9991 dan MSE = 2.7518. The one-way error panel component regression model is a combined regression model between cross-section data and time series data that has the right specifications to describe N random individuals from large populations. The purpose of writing this final project is to obtain a one-way error component panel regression model estimation using the Generalized Least Square method and to test the suitability of the model using the Hausman test and the Multiple Lagrange test. The results of the estimation of the regression parameters still depend on the components  delta_g^2  and delta_a^2so to estimate them the iteration process is performed until a converging parameter vector is obtained. The one-way error panel regression model can be written in the equation. The application of this model is carried out on the per capita Gross Regional Domestic Product (GRDP) data in provinces in Indonesia from 2007 to 2010 as the dependent variable , while the predictor variables include: Open Unemployment Rate , Foreign Investment Investment , Investment Domestic Investment , Total Labor Force , and Household Consumption Expenditures . This model has a value of  R^2 = 0.9991 and MSE = 2.7518
Co-Authors Budi Lestari Kinanti Hanugera Gusti Mahfudhotin Mahfudhotin Nur Chamidah Suliyanto