Article Per Year (5 Year)
p-Index From 2021 - 2026
1.633
P-Index
This Author published in this journals
All Journal Jurnal Masyarakat Informatika Rekursif: Jurnal Informatika Jurnal Pseudocode Jurnal PGSD: Jurnal Ilmiah Pendidikan Guru Sekolah Dasar Jurnal Teknik Informatika (JUTIF) These proceedings represent the work of researchers participating in The International Conference on Elementary Education (ICEE) which is being hosted by the Elementary Education Study Programme School of Postgraduate Studies, Universitas Pendidikan Dikdimas : Jurnal Pengabdian Kepada Masyarakat The Indonesian Journal of Computer Science Jurnal Abdimas Bencoolen IJIS Edu : Indonesian Journal of Integrated Science Education
Endina Putri Purwandari
Program Studi Teknik Infomatika, Fakultas Teknik, Universitas Bengkulu
Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Nursing Public Health Social Sciences Other

Published : 12 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 12 Documents
Search

 1   2 
E-Worksheets Based on STEAM-PJBL with Local Coastal Wisdom to Improve Critical Thinking Skills Winarni, Endang Widi; Heryanto, Debi; Yusnia, Yusnia; Agusdianita, Neza; Purwandari, Endina Putri; Wijanarko, Andang
IJIS Edu : Indonesian Journal of Integrated Science Education Vol 8, No 1 (2026): January 2026
Publisher : UIN Fatmawati Sukarno Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29300/ijisedu.v8i1.7774

Abstract

This research aimed to investigate the influence of electronic student worksheets based on STEAM and Project-Based Learning (PjBL) with local coastal wisdom content to improve the critical thinking abilities of fifth-grade students in Group V elementary schools in Bengkulu City. This research employed a quantitative approach using a quasi-experimental method with a Matching Only Pretest-Posttest Control Group Design. The population of this study comprised all elementary schools in Group V, Bengkulu City. The sample consisted of fifth-grade students from SDN 09 and 02 Bengkulu City, selected using Cluster Random Sampling. The research instrument used was a critical thinking skills test with a pretest and posttest. The data was analysed quantitatively using descriptive statistics, prerequisite tests, and hypothesis testing. The t-test was used to test the hypothesis. The results showed that the significance value (2-tailed) was 0.000 < 0.05 at a 5% significance level. The average scores of the experimental class (80.28) and the control class (50.21) showed a significant difference between the learning outcomes of the experimental and control classes. It can be concluded that the experimental class using electronic student worksheets based on STEAM and PjBL significantly influenced the students' critical thinking abilities. In conclusion, this study found a significant influence of electronic student worksheets based on STEAM and PjBL on the critical thinking skills of fifth-grade students in Group V elementary schools in Bengkulu City.
Comparative Analysis of Machine Learning for Stroke Classification Using YOLOv11 Detection and a Radiomics-Based Two-Stage Model Manurung, Wahyu Ozorah; Ernawati, Ernawati; Oktoeberza, Widhia KZ; Andreswari, Desi; Purwandari, Endina Putri; Efendi, Rusdi
Jurnal Masyarakat Informatika Vol 17, No 1 (2026): May 2026 (Ongoing)
Publisher : Department of Informatics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jmasif.17.1.78464

Abstract

Stroke is a leading cause of disability and death worldwide, including in Indonesia. Rapid and accurate diagnosis is crucial, especially during the golden period (3–4.5 hours). CT scans are the primary imaging modality, but manual interpretation is often limited by time, subjectivity, and radiologist availability. This study proposes a two-stage model integrating YOLOv11 for lesion detection and machine learning for classification, using radiomics for feature extraction. In the first stage, YOLOv11 detects lesions and generates bounding boxes, which serve as Regions of Interest (ROIs). In the second stage, radiomics features are extracted and classified using Naïve Bayes, Support Vector Machine (SVM), and Random Forest. Results show YOLOv11 achieved an overall mAP@50 of 0.732, with the highest performance in hemorrhagic stroke (0.741). Radiomics-based classification further improves stability, achieving accuracies of 0.97–0.99 and precision, recall, and F1 scores≥0.94. Among classifiers, SVM performed best, with a test accuracy of 0.97, a false positive rate of 1.23%, total error 0.0218, generalization gap -0.0117, variance 0.0002, standard deviation 0.003635, confidence interval 0.9708 (+/-0.0073), and consistent fold accuracy between 96.5–97.5%, indicating stability without overfitting. These findings confirm that the combination of the YOLOv11 two-stage model, radiomics, and SVM provides a robust approach to support stroke diagnosis.
Co-Authors Abdul Muktadir, Abdul Agusdianita, Neza Ahmad, Mubarik Azmi, Yoan Leo Daimun, Daimun Dalifa, Dalifa Desi Andreswari Dianita, Meisy Endang Widi Winarni Ernawati Ernawati Harmika, Lita Harry Budi Santoso Heryanto, Debi Kasiyah Junus Lakshita, Stefanie Laksmi Manurung, Wahyu Ozorah Mochammad Yusa, Mochammad Muh Syaiful Romadhon Nabila, Nisreina Natalio, Jandri Niska Ramadani, Niska Noperman, Feri Nurul Renaningtias, Nurul Oktoeberza, Widhia KZ Rusdi Efendi Sucipto, Sigit Syahputra, Wahyu Wijanarko, Andang Yudi Setiawan Yusnia, Yusnia Zuliuskandar, Valleryan Virgil