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All Journal Jurnal Media Computer Science
M. Ikhsan Thohir
Prodi Teknik Informatika, Fakultas Teknik Komputer dan Desain, Universitas Nusa Putra
Computer Science & IT

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Penerapan Computer Vision Untuk Klasifikasi Penyakit Mata Menggunakan Arsitektur Vision Transformers Pada Citra Fundus Indra Yustiana; Irvan Yudistiansyah; M. Ikhsan Thohir
Jurnal Media Computer Science Vol 5 No 1 (2026): Januari
Publisher : LPPJPHKI Universitas Dehasen Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37676/jmcs.v5i1.9229

Abstract

Eye diseases such as cataracts, glaucoma, and diabetic retinopathy are leading causes of blindness that can be prevented if detected early. This study aims to develop an eye disease classification system using a vision transformer architecture using fundus images. The data used consisted of 4,028 fundus images evenly divided into four classes: cataracts, glaucoma, diabetic retinopathy, and normal. The vision transformer model underwent preprocessing, augmentation, fine-tuning, and evaluation using metrics such as accuracy, precision, recall, and f1-score. Test results showed that the vision transformer model was able to classify eye diseases with high accuracy and stable performance across all classes. This model was also able to effectively recognize the characteristics of each disease, demonstrating the superiority of the vision transformer in understanding the global visual context of medical images.This study suggests that the vision transformer can be effectively used in an automated eye disease detection system using fundus images, although further optimization is still needed for use with devices with limited resources. This system is expected to facilitate early detection and reduce the workload of medical personnel.
Gold Price Prediction Based On Long Short-Term Memory (LSTM) For Investment Decision-Making Indra Yustiana; Siti Khoerunisa; M. Ikhsan Thohir
Jurnal Media Computer Science Vol 5 No 1 (2026): Januari
Publisher : LPPJPHKI Universitas Dehasen Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37676/jmcs.v5i1.9233

Abstract

The movement of gold prices serves as a critical indicator in investment decision-making, especially in dynamic and uncertain market conditions. This study aims to develop a gold price prediction model based on Long Short-Term Memory (LSTM), a type of Recurrent Neural Network (RNN) capable of capturing temporal patterns in historical price data. The goal of implementing this model is to generate more accurate predictions compared to conventional methods, thereby supporting more informed investment decisions. The research utilizes daily gold price data over a specific period, combined with other economic indicators such as oil prices, exchange rates, and interest rates. The data undergoes preprocessing steps, including normalization and division into training, validation, and testing sets. The proposed model architecture consists of a single LSTM layer with 64 neurons and an output layer, trained over 50 epochs with a batch size of 32. The results show that the LSTM model achieves high prediction accuracy. This is demonstrated by evaluation metrics including a Mean Absolute Error (MAE) of 26.12, Mean Squared Error (MSE) of 1269.15, Root Mean Squared Error (RMSE) of 35.63, and an R² score of 0.9858, indicating that the model can explain 98.58% of the variance in actual gold price data. Furthermore, the visualization of the results indicates that the model is capable of closely following gold price trends and consistently predicting upward price movements over the next 30 days. The model has also been successfully implemented into a web-based platform using TensorFlow.js, enabling users to access real-time predictions in an efficient and responsive manner.
Co-Authors Indra Yustiana Irvan Yudistiansyah Siti Khoerunisa