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All Journal JUITA : Jurnal Informatika METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi
Putu Desiana Wulaning Ayu
Politeknik Negeri Bali
Computer Science & IT Economics, Econometrics & Finance

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Classification Brain Tumor in HyperparameterOptimization of VGG-16 Model and Data Augmentation Analysis Putu Desiana Wulaning Ayu; I Gede Teguh Satya Dharma; I Wayan Rizky Wijaya; Made Agus Oka Gunawan; Ni Putu Eka Apriyanthi; Civica Moehaimin Dhewanty
JUITA: Jurnal Informatika JUITA Vol. 14 Issue 1, March 2026
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v14i1.27675

Abstract

This Advancements in computational technology have driven the development of Deep Learning, particularly Convolutional Neural Networks (CNN), in the classification and recognition of digital images. This research focuses on the classification of MRI brain tumor images using the VGG-16 architecture. The primary challenges include gradient vanishing and overfitting due to a small dataset. The objective of the study is to evaluate the performance of the model with various data augmentation techniques and to assess the impact of different dataset compositions (90:10 and 70:30) for training and testing. Two model configurations are used: Model A with 4096 neurons and Model B with 128 and 64 neurons in the first two Dense layers, respectively. The tested augmentation techniques include rotation, flip, Zoom , and their combinations. The results indicate that rotation and Zoom augmentations provide the best performance for both models and dataset compositions. Model A (90:10) achieved an accuracy of 96% with rotation and 92% with Zoom, while Model B (90:10) achieved 94% with rotation and 98% with Zoom. For the 70:30 composition, Model A achieved 94% (rotation) and 90% (Zoom ), while Model B achieved 95% (rotation) and 96% (Zoom ). This research provides valuable insights into optimizing VGG-16 architecture for brain tumor classification using limited datasets.
Prediksi IHSG Berbasis Web Menggunakan Metode Long Short-Term Memory (LSTM) I Komang Agus Arta Cahyana; Ni Gusti Ayu Putu Harry Saptarini; Putu Desiana Wulaning Ayu
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 10 No. 1 (2026): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol10No1.pp249-259

Abstract

The Indonesia Composite Stock Price Index (IHSG) is one of the primary indicators used to measure the performance and stability of the capital market in Indonesia. The dynamic and non-linear characteristics of IHSG data make stock market prediction a challenging task when using conventional statistical methods. This study aims to develop a web-based IHSG prediction system using the Long Short-Term Memory (LSTM) method to improve prediction accuracy and provide an interactive forecasting platform for users. Historical IHSG data were collected from Yahoo Finance API using OHLCV (Open, High, Low, Close, Volume) variables. The data preprocessing stage included data cleaning, normalization using Min-Max Scaling, and sequence generation with the sliding window technique. Hyperparameter tuning was conducted by testing several configurations of window size, hidden units, learning rate, and epoch values. The best model configuration was obtained using a window size of 60, hidden units of 128, a learning rate of 0.01, and 74 epochs, resulting in RMSE of 51.9821, MAE of 39.8241, and MAPE of 0.559 %. Unlike previous studies that mainly focused on offline model evaluation, this research integrates the LSTM model into an interactive web-based prediction system equipped with visualization, AI forecasting, statistical evaluation, and batch prediction simulation features. The results indicate that the LSTM model is capable of producing accurate IHSG predictions and can be effectively implemented in a real-time web-based forecasting system.
Co-Authors Civica Moehaimin Dhewanty I Gede Teguh Satya Dharma I Komang Agus Arta Cahyana I Made Agus Oka Gunawan I Wayan Rizky Wijaya Ni Gusti Ayu Putu Harry Saptarini Ni Putu Eka Apriyanthi