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All Journal Jurnal INSYPRO (Information System and Processing) JURNAL INSTEK (Informatika Sains dan Teknologi) Journal Software, Hardware and Information Technology
DARMATASIA, DARMATASIA
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Journal : Jurnal INSYPRO (Information System and Processing)

 1 
Game Edukasi Siswa SLB Untuk Meningkatkan Literasi Matematika Berbasis Android Hasanuddin, Muhammad Hasrul; Indra Syahyadi, Asep; Darmatasia, Darmatasia; Ridwang, Ridwang
Jurnal INSYPRO (Information System and Processing) Vol 8 No 2 (2023)
Publisher : Prodi Sistem Informasi UIN Alauddin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24252/insypro.v8i2.41134

Abstract

The research aims to develop and test the effectiveness of an Android-based educational game designed specifically for students of the Extraordinary School (SLB) in improving their mathematical literacy. Mathematical literacy is an essential skill that supports an individual's ability to understand, analyze, and apply mathematical concepts in everyday life. However, SLB students often face the challenge of acquiring a sufficient understanding of mathematics due to various barriers in their learning. This research methodology covers the stages of development of educational games designed in accordance with the characteristics of SLB students and the principles of effective mathematical learning. After the game was developed, the study involved a group of SLB students in a field experiment using a quasi-experimental approach with the control group. During a certain period, students from the experimental group participated in learning sessions using educational games, while students from control groups received conventional mathematical learning. The results of this study are expected to provide insight into the extent to which this Android-based educational game is effective in improving the mathematical literacy of SLB students. Furthermore, the research also has the potential to provide valuable input for further development of similar educational games that can be used as an inclusive learning tool for students with special educational needs. Increased mathematical literacy among SLB students can provide long-term benefits in preparing them to face the demands of mathematics in their later lives
ANALISIS PERFORMA CONVOLUTIONAL NEURAL NETWORK DENGAN HYPERPARAMETER TUNING DALAM MENDETEKSI GAMBAR DEEPFAKE Darmatasia, Darmatasia; Ramli, Abdur Rahman; Salsabila, Azizah; Adiba, Fhatiah
Jurnal INSYPRO (Information System and Processing) Vol 9 No 2 (2024)
Publisher : Prodi Sistem Informasi UIN Alauddin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24252/insypro.v9i2.51928

Abstract

This research analyzes the performance of Convolutional Neural Network (CNN) in detecting deepfake images with a focus on hyperparameter tuning. The study consists of two classes: fake images and real images, with each class containing 5000 data samples. Hyperparameter tuning is conducted using the Keras-tuner library, a framework used for automatic hyperparameter tuning on models built with Keras, eliminating the need for manual trial and error tuning. The hyperparameter search strategy employed is random search. The results of the study indicate that hyperparameter tuning significantly improves the model's detection accuracy. Various experiments were conducted to evaluate the impact of hyperparameter settings, such as the number and size of filters, learning rate, and optimizer. Analysis of different optimizers showed significant variations in performance, with Adam Optimizer achieving the highest accuracy of 83% using a combination of 32 filters sized 3x3 in the first layer and 128 filters sized 5x5 in the second layer. RMSProp and AdamW each achieved 82% accuracy, SGD Optimizer achieved 75% accuracy, while Adadelta Optimizer achieved 71% accuracy. The findings of this study affirm that the selection of optimizer and appropriate hyperparameter settings have a significant impact on the model's performance in detecting patterns in the data. This study also emphasizes the importance of optimizing filters and sizes in each layer to enhance model accuracy.
Co-Authors A. Muhammad Syafar Adiba, Fhatiah Akbar , Muhammad Nur Hasanuddin, Muhammad Hasrul Indra Syahyadi, Asep Ramli, Abdur Rahman Ridwang Ridwang, Ridwang Salsabila, Azizah Syarif , Ririn Suharni