cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
-
Contact Email
-
Phone
-
Journal Mail Official
-
Editorial Address
-
Location
Kab. sleman,
Daerah istimewa yogyakarta
INDONESIA
Pythagoras: Jurnal Matematika dan Pendidikan Matematika
Published by Universitas Negeri Yogyakarta
ISSN : 19784538     EISSN : 2527421X     DOI : 10.21831
Core Subject : Education,
Education Mathematics
Arjuna Subject : -
Articles 4 Documents
 1 
Search results for , issue "Vol. 20 No. 2 (2025)" : 4 Documents clear
Digital Project Development Study: How Does Scratch Accommodate Students' Numeracy and Logical Thinking Skills? Ummah, Siti Khoiruli; Effendi, Moh Mahfud; Rosyadi, Alfiani Athma Putri
PYTHAGORAS Jurnal Matematika dan Pendidikan Matematika Vol. 20 No. 2 (2025)
Publisher : Department of Mathematics Education, Faculty of Mathematics and Natural Sciences, UNY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/pythagoras.v20i2.82415

Abstract

Numeracy and logical thinking skills need to be accommodated in lectures, especially logic and algorithm lectures. The purpose of this study is to develop a digital project-based lecture design assisted by the Scratch Application to accommodate students' numeracy and logical thinking skills. The research method uses the ADDIE (Analysis, Design, Develop, Implementation, Evaluation) development model with 20 students as trial subjects. The research instruments used were observations of the implementation of the lecture model and the provision of tests so that the data analysis technique used was using inferential statistical tests to see the effectiveness of the lecture model compared to the lecture model using the discussion method. The results of the study showed a digital project in the form of making simple animations in the form of games by applying mathematical logic rules when compiling scripts. The use of branching and looping algorithms is able to accommodate students' abilities in logical thinking so that there is a systematic thinking pattern. In conclusion, the digital project-based lecture design assisted by Scratch has significant differences with the lecture model using the discussion method based on students' numeracy and logical thinking skills.
Kerangka SPIKR untuk Mengajarkan Keterampilan Kolaborasi Antardisiplin Ilmu bagi Statistisi dan Data Saintis Indonesia Mualifah, Laily Nissa Atul; Vance, Eric Alan
PYTHAGORAS Jurnal Matematika dan Pendidikan Matematika Vol. 20 No. 2 (2025)
Publisher : Department of Mathematics Education, Faculty of Mathematics and Natural Sciences, UNY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/pythagoras.v20i2.82940

Abstract

Di abad ke-21, dengan pesatnya peningkatan volume data dan kompleksitas masalah, statistisi dan data saintis tidak lagi mampu menyelesaikan permasalahan hanya berdasarkan bidang keilmuan mereka saja. Mereka kini dituntut untuk berkolaborasi dengan profesional dari berbagai disiplin ilmu guna mendorong inovasi dan kreativitas dalam pemecahan masalah. Keterampilan kolaborasi ini bukanlah keterampilan yang spesifik pada disiplin ilmu tertentu, melainkan keterampilan umum yang dapat diajarkan dan dipelajari oleh berbagai pihak dari semua bidang ilmu. Penelitian ini memperkenalkan kerangka SPIKR, yaitu suatu kerangka yang dirancang untuk mengajarkan keterampilan kolaborasi antardisiplin ilmu kepada statistisi dan data saintis Indonesia. Kerangka SPIKR terdiri dari lima komponen utama: Sikap, Pola Pertemuan, Isi Proyek, Komunikasi, dan Relasi. Hasil penelitian kami menunjukkan bahwa setiap komponen dalam SPIKR memiliki peran yang sangat penting dalam meningkatkan keterampilan kolaborasi antardisiplin ilmu di kalangan statistisi dan data saintis Indonesia. Untuk mengajarkan SPIKR dengan efisien, kami menemukan bahwa metode pembelajaran berbasis kelompok dan memfasilitasi mahasiswa untuk melakukan kolaborasi nyata dengan mitra kolaborasi dari disiplin ilmu yang berbeda terbukti menjadi pendekatan yang sangat efektif dalam meningkatkan keterampilan non-teknis kolaborasi.
Comparative Study of Lightweight Deep Learning Architectures for Potato Plant Disease Detection Faisol, Ahmad; Rudhistiar, Deddy; Puspasari, Betty Dewi
PYTHAGORAS Jurnal Matematika dan Pendidikan Matematika Vol. 20 No. 2 (2025)
Publisher : Department of Mathematics Education, Faculty of Mathematics and Natural Sciences, UNY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/pythagoras.v20i2.90854

Abstract

Potato leaf diseases pose a significant threat to crop productivity and global food security, necessitating accurate and reliable diagnostic systems for early detection. Although deep learning based image classification has shown promising results in plant disease recognition, many existing studies rely on simple train test splits, insufficient handling of class imbalance, and limited statistical analysis. This study presents a comprehensive evaluation of multiple pretrained convolutional neural network architectures for multi class potato leaf condition classification, including disease categories and a healthy class. DenseNet121, EfficientNetV2 S, InceptionV3, MobileNetV3 Small, ResNet50, and Xception were evaluated using a stratified K fold cross validation framework. Class imbalance was addressed through class weighted loss functions, and model performance was assessed using accuracy, macro averaged F1 score, and weighted F1 score reported as mean values with 95% confidence intervals. The experimental results indicate that ResNet50 achieved the best overall performance with a mean accuracy of 99.07% ± 0.38% and a macro F1 score of 98.24% ± 0.80%, demonstrating strong and consistent classification across all classes. Lightweight architectures such as MobileNetV3 Small also delivered competitive results with an accuracy of 97.77% ± 0.59%, highlighting their suitability for deployment in resource constrained agricultural environments. These findings emphasize the importance of statistically robust evaluation and imbalance aware training strategies for developing reliable deep learning based systems in precision agriculture.
Exploring Spatial Nonstationarity in the Number of Motor Vehicles in East Java Using Robust Geographically Weighted Regression with an MM-Estimator Isnaini, Bayutama; Isnandar Slamet; Sulandari, Winita; Khoirunissa, Husna Afanyn
PYTHAGORAS Jurnal Matematika dan Pendidikan Matematika Vol. 20 No. 2 (2025)
Publisher : Department of Mathematics Education, Faculty of Mathematics and Natural Sciences, UNY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/pythagoras.v20i2.90866

Abstract

The number of motor vehicles in each region is quite diverse, not least in East Java Province. The variety of number of motor vehicles can be affected by local factors that are important to study so that vehicle growth can be adequately anticipated. On the other hand, economic growth incentives are influenced by people's purchasing power. In addition, motor vehicles are one of the essential things in the sustainability of economic activities. This study aims to evaluate a robust, geographically weighted regression model with an MM-estimator (RGWR MM-estimator), which is considered suitable for analyzing the number of motor vehicles in East Java. The results showed that the RGWR MM-estimator model generates an estimate of the number of motor vehicles based on the HDI explanatory variables, road length, sex ratio, poverty gap index, and the number of colleges that is accurate compared to other models formed. In addition, there are significant differences in the influence of the five explanatory variables in each region. Districts/cities located near the capital of East Java Province tend to have many explanatory variables that have a significant effect compared to regencies/cities far from the provincial capital.

Page 1 of 1 | Total Record : 4

 1 

Filter by Year

2025 2025


Reset
Filter By Issues
All Issue Vol. 20 No. 2 (2025) Vol. 20 No. 1 (2025) Vol. 19 No. 2: December 2024 Vol. 19 No. 1: June 2024 Vol. 18 No. 2: December 2023 Vol. 18 No. 1: June 2023 Vol. 17 No. 2: December 2022 Vol. 17 No. 1: June 2022 Vol. 16 No. 2: December 2021 Vol 16, No 1: June 2021 Vol 15, No 2: December 2020 Vol 15, No 1: June 2020 Vol 14, No 2: December 2019 Vol 14, No 1: June 2019 Vol 13, No 2: December 2018 Vol. 13 No. 2: December 2018 Vol 13, No 1: June 2018 Vol. 13 No. 1: June 2018 Vol. 12 No. 2: December 2017 Vol 12, No 2: December 2017 Vol 12, No 1: June 2017 Vol 11, No 2: December 2016 Vol 11, No 1: June 2016 Vol 10, No 2: December 2015 Vol 10, No 1: June 2015 Vol 9, No 2: December 2014 Vol 9, No 1: June 2014 Vol 8, No 2: December 2013 Vol 8, No 1: June 2013 Vol. 7 No. 2: Desember 2012 Vol. 7 No. 1: Juni 2012 Vol 5, No 2: Desember 2009 Vol 5, No 1: Juni 2009 Vol. 4 No. 2: Desember 2008 Vol. 4 No. 1: Juni 2008 Vol. 3 No. 2: Desember 2007 Vol 3, No 1: Juni 2007 Vol 2, No 2: Desember 2006 More Issue