Claim Missing Document
Check
Articles

Found 22 Documents
Search

Effect Size Digital Mathematics Textbook in Blended Learning Assisted by Schoology Improves Mathematical Problem Solving Ability for Polytechnic Students I Ketut Darma; I Gede Made Karma; I Made Anom Santiana; Ni Wayan Sadiyani
SOSHUM : Jurnal Sosial dan Humaniora Vol. 13 No. 1 (2023): March 2023
Publisher : Unit Publikasi Ilmiah, P3M, Politeknik Negeri Bali

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31940/soshum.v13i1.101-108

Abstract

This study aims to determine the effect size of digital mathematics textbooks in blended learning assisted by Schoology to improve mathematical problem-solving abilities in polytechnic students. The research was conducted at the Bali State Polytechnic (PNB), using the Research & Development method with a 4-D model (define, design, develop, and disseminate). Currently, the dissemination stage is carried out through an effectiveness test using a quasi-experiment approach, with a one-group pretest-posttest design. The subjects are PNB engineering students in 2020/2021, and 10 classes are taken by purposive sampling. Data were collected using a mathematical problem-solving ability test and analyzed using a t-test, N-Gain scores, and effect size. The results of the analysis show that the effect size of digital mathematics textbooks to improve mathematical problem-solving skills in Schoology-assisted blended learning is very large. The implication is that increasing the ability to solve mathematical problems through blended learning assisted by Schoology for polytechnic students will be quite effective if it is facilitated by digital mathematics textbooks.
Application of feature-based image matching method as an object recognition method Karma, I Gede Made; Darma, I Ketut
Bulletin of Electrical Engineering and Informatics Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i2.8803

Abstract

In everyday life, objects are recognized based on the suitability of their characteristics to familiar objects. A feature matching process occurs when recognizing objects. This concept is what we want to apply and test in this research. Because various factors can influence the level of accuracy and success of an image matching method, the first step taken is to improve the accuracy level of the image matching method used. There are three feature-based image matching methods, which are implemented as object recognition methods. These three methods are the result of modifications of the image matching function method, normalized 2D cross correlation method and point feature matching which were later named PICMatch, NCMatch and FBMatch. As image matching methods, these three modified methods show performance with a success rate above 95%. However, when applied as an object recognition method, both individually and combined, the three methods only have a maximum accuracy of 7%. These results are obtained by matching the samples using one of the methods with the best match rate, in the order of application of the PICMatch, NCMatch, and FBMatch methods.