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All Journal Jurnal Matematika Tensor: Pure and Applied Mathematics Journal
A. Z. Wattimena
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Computer Science & IT Mathematics

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Analisis Regresi Cox Proportional Hazard Untuk Menentukan Faktor-Faktor yang Memengaruhi Lama Studi Mahasiswa Inti Arpen; Yopi Andry Lesnu; A. Z. Wattimena; M. Yahya Matdoan
Jurnal Matematika Vol 11 No 1 (2021)
Publisher : Mathematics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JMAT.2021.v11.i01.p133

Abstract

Higher education has an important role for students, so that students can pursue their studies and can complete their studies on time. Length of study is the time it takes a student to complete his studies. For undergraduate level (S1) is 4 years and no later than 7 years. There are several factors that influence the length of study of students, namely internal factors and student external factors. The survival analysis method is a statistical method that studies the duration of an event or event that occurs. The Cox regression model is a well-known model in survival analysis for explaining the relationship between individual failure at a time and explanatory variables in the presence of censorship. The results of this study indicate that the factors that influence the length of study of students are the factor of GPA> 3, factors majoring in mathematics, majoring in biology and factors majoring in physics.
Klasifikasi Citra Tekstur Daging Sapi, Kambing, dan Babi Menggunakan Ekstraksi Fitur Wavelet Haar dan Symlet Berbasis Support Vector Machine Green Kenny Sarimanella; Francis Yunito Rumlawang; Harmanus Batkunde; Meilin Imelda Tilukay; A. Z. Wattimena
Tensor: Pure and Applied Mathematics Journal Vol 7 No 1 (2026): Tensor: Pure and Applied Mathematics Journal
Publisher : Department of Mathematics, Faculty of Mathematics and Natural Sciences, Pattimura University, Ambon, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/tensorvol7iss1pp59-66

Abstract

Meat is one of the animal protein sources widely consumed by the public; however, distinguishing different types of meat visually is often difficult because they have very similar textures. This study applies the Support Vector Machine (SVM) method with feature extraction based on Haar Wavelet and Symlet Wavelet (Sym4) to classify texture images of beef, goat meat, and pork. The dataset consisted of 1200 digital images processed through resizing, grayscale conversion, and normalization stages. Feature extraction was performed using the Discrete Wavelet Transform (DWT) to obtain statistical texture features. The classification process employed the Radial Basis Function (RBF) kernel with a multiclass classification approach. The results showed that the Haar Wavelet achieved an accuracy of 96.67%, while the Symlet Wavelet (Sym4) achieved 94.17%. These findings indicate that the combination of wavelet methods and SVM is effective for automatic and objective meat type identification
Co-Authors Batkunde, Harmanus Green Kenny Sarimanella Inti Arpen M. Yahya Matdoan Meilin Imelda Tilukay RUMLAWANG, FRANCIS YUNITO Yopi Andry Lesnu