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All Journal JUITA : Jurnal Informatika METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Jurnal Teknologi Dan Sistem Informasi Bisnis
Putu Desiana Wulaning Ayu
Politeknik Negeri Bali
Computer Science & IT Economics, Econometrics & Finance Electrical & Electronics Engineering

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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.
Systematic Literature Review: Karakteristik UI/UX Efektif untuk Efisiensi Sistem Informasi UMKM Civica Moehaimin Dhewanty; Putu Desiana Wulaning Ayu; Ni Putu Eka Apriyanthi
Jurnal Teknologi Dan Sistem Informasi Bisnis Vol 8 No 1 (2026): Januari 2026
Publisher : Prodi Sistem Informasi Universitas Dharma Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jteksis.v8i1.2374

Abstract

Pengembangan Sistem Pengaduan Masyarakat Berbasis Citra Digital Menggunakan CNN dan Content-Based Filtering pada Kantor Kelurahan Peguyangan Putu Muna Saskara Widnyana; Ni Gusti Ayu Putu Harry Saptarini; Putu Desiana Wulaning Ayu
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 10 No. 2 (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.Vol10No2.pp947-954

Abstract

This study aims to develop a digital image-based road damage reporting system at the Peguyangan Subdistrict Office to assist in the automatic classification of road damage levels and detect duplicate reports. The system was built by integrating a MobileNetV2-based Convolutional Neural Network (CNN) for classifying road damage images and Content-Based Filtering (CBF) for identifying similar reports based on image similarity and proximity of location. Model evaluation was conducted using 5-fold cross-validation on a dataset of 500 images of damaged roads consisting of two classes: severe damage and minor damage. The test results showed that the model achieved an average accuracy of 89.40% with a best F1-score of 0.9194. The duplicate detection mechanism successfully grouped similar reports, thereby aiding in the prioritization of repairs. Additionally, black-box testing across 20 test scenarios showed that all system functions operated as designed with a 100% success rate. The research results indicate that the integration of CNN and CBF can support the management of road damage complaints in a more effective, structured, and responsive manner.
Web-Based Asset Management Information System at PT Astra Daihatsu Ngurah Rai Tuban Yohannes Putu Alvin Sutrisna; Ni Wayan Wisswani; Putu Desiana Wulaning Ayu
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 10 No. 2 (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.Vol10No2.pp810-822

Abstract

Effective asset management plays a crucial role in supporting organizational operations by ensuring the availability, accuracy, and accessibility of asset information. However, the asset recording process at PT Astra Daihatsu Ngurah Rai Tuban was previously conducted using a semi-manual approach, resulting in data redundancy, delays in report generation, and difficulties in obtaining real-time inventory information. This study aims to design and develop a web-based Asset Management Information System to improve the effectiveness and efficiency of asset management within the company. The system was developed using the Waterfall software development methodology, while data were collected through observation, interviews, and documentation. The application was implemented using the Laravel framework with a MySQL database and incorporates Role-Based Access Control (RBAC) to manage user permissions for Administrators, Warehouse Staff, Sales Staff, and Managers. The system provides integrated features for master data management, incoming and outgoing goods transactions, goods return processing, inventory reporting, and report export in PDF and Microsoft Excel formats. System evaluation was conducted using Black Box Testing and User Acceptance Testing (UAT). The testing results indicate that all functional requirements were successfully fulfilled, with all test cases achieving a Passed status. Furthermore, UAT results demonstrate that the developed system satisfies user requirements and effectively supports daily asset management activities. Therefore, the proposed system provides a reliable and efficient solution for improving asset management and supporting managerial decision-making at PT Astra Daihatsu Ngurah Rai Tuban.
Co-Authors Civica Moehaimin Dhewanty 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 Ni Putu Eka Apriyanthi Ni Wayan Wisswani Putu Muna Saskara Widnyana Yohannes Putu Alvin Sutrisna