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All Journal Nuansa Informatika JURNAL MEDIA INFORMATIKA BUDIDARMA bit-Tech Jurnal Abdi Insani Jurnal Teknimedia: Teknologi Informasi dan Multimedia Journal of Applied Data Sciences Jurnal Pengabdian Masyarakat Bangsa JGGAG (Journal of Games, Game Art, and Gamification)
Dede Husen
Universitas Kuningan
Agriculture, Biological Sciences & Forestry Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Medicine & Pharmacology Nursing Physics Public Health Social Sciences Other

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EVALUASI TEKNIK AUGMENTASI DATA UNTUK KLASIFIKASI TUMOR OTAK MENGGUNAKAN CNN PADA CITRA MRI Dede Husen
TEKNIMEDIA: Teknologi Informasi dan Multimedia Vol. 5 No. 2 (2024): Desember 2024
Publisher : Badan Penelitian dan Pengabdian Masyarakat (BP2M) STMIK Syaikh Zainuddin NW Anjani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46764/teknimedia.v5i2.220

Abstract

Brain tumor classification on Magnetic Resonance Imaging (MRI) scans poses a significant challenge in the fields of radiology and medical technology. To enhance diagnostic accuracy, Convolutional Neural Network (CNN) methods have shown great potential. However, the limitation of having an adequate training dataset remains a major obstacle in developing effective models. This study aims to evaluate the performance of CNN models by applying various data augmentation techniques for brain tumor classification and identifying the most effective augmentation techniques. The augmentation techniques tested include image scaling, random rotation, vertical and horizontal flipping, random brightness adjustments, and combinations of these various techniques. The results indicate that the scaling and vertical and horizontal flipping techniques yield the highest average accuracy of 92.97%, with a maximum accuracy of 100% achieved at the 20th epoch using the vertical and horizontal flipping technique. Thus, it is hoped that the findings of this study can be utilized by other researchers in selecting appropriate augmentation techniques for MRI images.
Predictive Modeling of Student Academic Performance Using Regression Methods Husen, Dede
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3391

Abstract

The advancement of digital technologies has strengthened the use of data driven approaches in understanding the factors that shape students’ academic achievement. This study aims to examine how daily habits, lifestyle patterns, and environmental conditions contribute to exam performance using the Student Habits vs Academic Performance dataset from Kaggle, which contains 1,000 student records covering behavioral, health related, and socioenvironmental attributes. Guided by the CRISPDM framework, the research includes data preparation, exploratory analysis, and predictive modeling using two regression techniques: Linear Regression and Random Forest Regressor. The predictive models were developed to estimate exam scores based on several key variables, including study duration, attendance rate, sleep quality, leisure activities, and parental education level. The results show that Linear Regression achieved the highest accuracy, with an MAE of 4.19, an RMSE of 5.15, and an R² of 0.897, indicating that approximately 89.65% of score variability can be explained by the selected features. Meanwhile, the Random Forest model recorded a slightly lower R² of 0.850, suggesting that the dominant relationships in the dataset follow a largely linear pattern. These findings highlight that consistent study routines, regular attendance, adequate sleep, and supportive home environments are strongly associated with improved academic outcomes. The study emphasizes the importance of interpretable machine learning models in educational analytics and offers insights that may support data informed interventions aimed at enhancing student performance.
Co-Authors Daswa Daswa, Daswa Dede - Sandi Erik Kurniadi Fahmi Yusuf Kusnawi Kusnawi Kusrini - - Kusrini Kusrini Nida Amalia Asikin RIO ANDRIYAT KRISDIAWAN Sepriadi - Bumbungan Toni Khalimi Yulyanto Yulyanto