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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Pancaran Pendidikan
Budi Lestari
University of Jember
Humanities Chemistry Computer Science & IT Education Languange, Linguistic, Communication & Media 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.
Modeling Student Mathematics Achievement in Senior High School Based on Selection Results Using Gee 2 Method with Natural Spline Erfan Syahuri; I Made Tirta; Budi Lestari; Dian Anggraeni
Pancaran Pendidikan Vol 6, No 3 (2017)
Publisher : The Faculty of Teacher Training and Education The University of Jember Jember, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (854.303 KB) | DOI: 10.25037/pancaran.v6i3.54

Abstract

Every school has a vision and mission to become the superior institution so that it can compete and gain trust from the public. To achieve that, one of the efforts of the school is doing the selection of new students at the beginning of each academic year. In Lumajang region, admission of new students (PPDB) are selected using several components, such as national test scores (NUN) and Mapping/Placement test (MP). This research explores the best model of the relationship between selection components (and other conditions of students at the time of selection) and academic achievement during high school (in the form semester mathematics grade) starting from semester 1 till 5 at 3 schools in Lumajang regions. We apply Generalized Estimating Equation order 2 (GEE2) with Natural Spline. The results show that (i) the three schools, have different model and PGRI has the highest mean, followed by SMA1and SMA3, as shown by significant negative estimates of the coefficients. (i) Altough it is relatively small, distance from school has negatif contribution to the mathematics grade as shown by negatif (but significant) coefficient; (ii) The Junior High School NUN has nonlinear (and nonparametric) contribution as shown by the graphical representation and coefficient of natural spline. (iii) Score of Placement Test contribute positively and significantly to the the smester mathematics grade.
Co-Authors Dian Anggraeni Eko Tjahjono Erfan Syahuri I Made Tirta Kinanti Hanugera Gusti Nur Chamidah