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Implementasi Model Pembelajaran Kooperatif Tipe TGT untuk Meningkatkan Hasil Belajar Matematika dengan Materi Satuan Baku dan Tidak Baku Kelas IV SDN 02 Nambangan Kidul Atikah Atikah; Candra Dewi; Ika Karlina
Awwaliyah: Jurnal Pendidikan Guru Madrasah Ibtidaiyah Vol. 9 No. 1 (2026): Awwaliyah: Jurnal PGMI
Publisher : Program Studi Pendidikan Guru Madrasah Ibtidaiyah (PGMI) Fakultas Tarbiyah dan Ilmu Keguruan (FTIK) Institut Agama Islam Tarbiyatut Tholabah (IAI TABAH) Lamongan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58518/awwaliyah.v9i1.5304

Abstract

Based on the observation results, the researcher found that fourth grade students felt bored with conventional mathematics learning methods so that their learning outcomes were less than optimal. To overcome this, a study was conducted using the TGT type cooperative learning model which includes educational games and tournaments. The purpose of this study was to improve the mathematics learning outcomes of fourth grade students at SDN 02 Nambangan Kidul Madiun. This study used a classroom action research approach involving 17 students in two cycles. The results showed that the application of the TGT model with the snakes and ladders game can significantly improve the average learning outcomes of students. In the initial stage, only 4 students completed the lesson, but after cycle I with lectures, 10 students completed the lesson. In cycle II with the TGT model, the number of students who completed the lesson increased to 15 students with an average score of 84.4. Therefore, the use of the TGT type cooperative learning model with games can improve students' mathematics learning outcomes and is suitable for implementation.
PENERAPAN MODEL PBL BERBANTUAN MEDIA PAPAN BAHAN BAKU DALAM MENINGKATKAN KEMAMPUAN BERPIKIR KRITIS SISWA KELAS V SD Sholikha, Dea Kamelia; Candra Dewi; Amalya Trisuci Dewanti
Pendas : Jurnal Ilmiah Pendidikan Dasar Vol. 9 No. 2 (2024): Volume 09 No. 2 Juni 2024
Publisher : Program Studi Pendidikan Guru Sekolah Dasar FKIP Universitas Pasundan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23969/jp.v9i2.15099

Abstract

The aim of this research is to improve the critical thinking skills of class V students at SDN 03 Madiun Lor by implementing the Problem Based Learning (PBL) model using raw material boards. This classroom action research (PTK) involved 25 students. Data analysis used quantitative data. Quantitative data is used to measure students' critical thinking abilities by using tests in the form of essay questions that are adjusted to indicators of critical thinking abilities. In the first cycle, the average critical thinking ability test score was 70.88 with a classical completion level of 48%. In the second cycle, the average score increased to 84.32 with a classical completion level of 88%. This shows that the application of the PBL model using raw material board media is effective in improving students' critical thinking skills.
MENINGKATKAN KETERAMPILAN MENULIS TEKS DESKRIPSI DENGAN MEDIA GAMBAR PADA SISWA SEKOLAH DASAR Nur Azizah, Fidya; Candra Dewi; Amalya Trisuci Dewanti
Pendas : Jurnal Ilmiah Pendidikan Dasar Vol. 9 No. 2 (2024): Volume 09 No. 2 Juni 2024
Publisher : Program Studi Pendidikan Guru Sekolah Dasar FKIP Universitas Pasundan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23969/jp.v9i2.15291

Abstract

This research aims to improve descriptive text writing skills using image media for elementary school students at SDN 03 Madiun Lor. This research was conducted using a collaborative classroom action research (PTK) method, consisting of two learning cycles. This research involved 29 fifth grade students at SDN 03 Madiun Lor as research subjects. The data analysis techniques used in this research include quantitative and qualitative analysis through tests and observations carried out before and after the learning cycle. Based on the research results, it can be concluded that in the pre-cycle, 10 students succeeded in achieving completion, while 19 other students did not complete. In the first cycle, there was an increase with 15 students achieving completion and 14 students still not completing it. In the second cycle, the number of students who achieved completion increased to 25 students, while only 4 students had not yet completed it. These results indicate that the level of mastery in writing skills began to increase after the application of image media since the pre-cycle.
Leveraging Stacked Vessel Segment and Channels of Fundus Image for Eye Disease Detection using Hybrid U-Net-Residual Convolutional Candra Dewi; Novanto Yudistira; Daffa Izzuddin; Nazura Wirayuda Tama; Fatyanosa, Tirana Noor
Journal of Information Technology and Computer Science Vol. 10 No. 2: August 2025
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jitecs.2025102928

Abstract

Fundus images can be used to identify symptoms of various eye diseases. However, a limitation of using fundus images for classification is the generalization of information from the entire image, which can reduce recognition accuracy. To address this, this study proposes a combination of RGB channels from fundus images and segmented images for the identification process. Segmentation is performed using U-Net, which produces a segmentation of the blood vessels from the retinal image. The combined image is then used as input for the identification process with different ResNet models, including ResNet18, ResNet34, ResNet50, ResNet101, and ResNet152. Three model tuning scenarios are explored to obtain an optimal hybrid segmentation and classification model: learning using ResNet without U-Net (nounet), learning using a ResNet model with a frozen U-Net model (frozenunet), and learning using both the U-Net and ResNet models (hotunet). Testing is carried out to recognize normal, cataract, and glaucoma classes. The results show that the highest accuracy of 0.82 is achieved with the hybrid U-Net and ResNet152 model using frozenunet learning. This indicates that the addition of segmented images can improve identification results, with the best performance for glaucoma having a precision of 0.90, recall of 0.86, and F1-score of 0.88.