Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer
Mamluatul Hani'ah
Politeknik Negeri Malang, Malang, Indonesia
Computer Science & IT

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Proliferative Diabetic Retinopathy Detection Using Convolutional Neural Network with Enhanced Retinal Image Wilda Imama Sabilla; Mamluatul Hani'ah; Ariadi Retno Tri Hayati Ririd; Astrifidha Rahma Amalia
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 25 No. 1 (2025)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v25i1.4976

Abstract

Proliferative Diabetic Retinopathy (PDR) is the most severe stage of Diabetic Retinopathy (DR), carrying the highest risk of complications. Automatic detection can help provide earlier and more accurate PDR diagnosis, but prediction accuracy may decline due to limitations in retinal images. Therefore, image enhancement techniques are often applied to improve DR classification. This study aims to detect PDR from retinal images using Convolutional Neural Networks (CNNs) and to evaluate the impact of three enhancement methods. This research method is based on a CNN architecture, including ResNet34, InceptionV2, and DenseNet121, as well as enhancement methods such as CLAHE, Homomorphic Filtering (HF), and Multiscale Contrast Enhancement (MCE). The results of this research show that CNN performance varies across architectures and enhancement methods. The highest performance was achieved using ResNet34 with HF, yielding an accuracy of 0.976, precision of 0.934, and recall of 0.904. CLAHE generally improved performance across architectures, achieving the best average accuracy of 0.953, whereas MCE decreased classification accuracy. Overall, the findings highlight the importance of selecting appropriate enhancement methods to improve PDR detection accuracy. Implementing such systems in clinical screening could help reduce the risk of vision impairment among diabetic patients.
Google Trends and Technical Indicator based Machine Learning for Stock Market Prediction Mamluatul Hani'ah; Moch Zawaruddin Abdullah; Wilda Imama Sabilla; Syafaat Akbar; Dikky Rahmad Shafara
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 22 No. 2 (2023)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v22i2.2287

Abstract

The stock market often attracts investors to invest, but it is not uncommon for investors to experience losses when buying and selling shares. This causes investors to hesitate to determine when to sell or buy shares in the stock market. The accurate stock price prediction will help investors to decide when to buy or sell their shares. In this study, we propose a new approach to predicting stocks using machine learning with a combination of features from stock price features, technical indicators, and Google trends data. Three well-known machine learning algorithms such as Support Vector Regression (SVR), Multilayer Perceptron (MLP), and Multiple Linear regression are used to predict future stock prices. The test results show that the SVR outperformed the MLP and Multiple Linear Regression to predict stock prices for Indonesian stocks with an average MAPE is 0.50%. The SVR can predict the stock price close to the actual price.
Co-Authors Ariadi Retno Tri Hayati Ririd Astrifidha Rahma Amalia Dikky Rahmad Shafara Moch Zawaruddin Abdullah Syafaat Akbar Wilda Imama Sabilla