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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
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Journal : bit-Tech

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Klasifikasi Citra MRI Tumor Otak Menggunakan Metode Convolutional Neural Network Dede Husen
bit-Tech Vol. 7 No. 1 (2024): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v7i1.1576

Abstract

This study aims to improve the accuracy of brain tumor classification using Convolutional Neural Network (CNN) method on MRI images. In this study, various experiments were conducted using the original dataset and data that had undergone augmentation to increase the amount and variety of data. This study shows that data augmentation, such as flipping, scaling, and rotation, significantly improves model accuracy. The best model was obtained using flip and scale augmentation techniques with an average accuracy of 92.97%. These results show that the use of data augmentation techniques can improve the performance of CNN models in classifying brain tumors, and reduce the risk of overfitting. This research makes an important contribution to the field of medical diagnosis by providing a more accurate and efficient model for detecting brain tumors.
Predictive Modeling of Student Academic Performance Using Regression Methods Dede Husen
bit-Tech Vol. 8 No. 3 (2026): bit-Tech - IN PROGRESS
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