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All Journal International Journal of Science and Environment Journal Information Technology Trends
Ulfah Siregar, Maria
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Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Mathematics Physics Other

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Classification of Senile Cataract Disease Using Convolutional Neural Network Method and Explainable Artificial Intelligence Gumilang Hardandrito, Awan; Ulfah Siregar, Maria
International Journal of Science and Environment (IJSE) Vol. 5 No. 3 (2025): August 2025
Publisher : CV. Inara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51601/ijse.v5i3.111

Abstract

Senile cataract is a major cause of visual impairment in the elderly that requires technology-based diagnosis to improve detection efficiency and accuracy. This study aims to classify the severity of senile cataracts in eye fundus images using a deep learning ensemble model approach consisting of CNN Custom and MobileNetV2, as well as Explainable AI methods in the form of Grad-CAM. The underlying theory is the Convolutional Neural Network architecture as the image feature extraction model, plus the transfer learning principle in MobileNetV2, as well as the visual interpretation of Grad-CAM to increase the transparency of the model. The research approach is experimental, with the data coming from the Senile Cataract dataset processed through augmentation and stratified division. A Custom CNN was built with four convolution blocks while MobileNetV2 was used as the pretrained feature extractor. Both were combined in the feature fusion stage and the prediction results were visualized with Grad-CAM. The evaluation results showed that this ensemble model achieved 95.6% accuracy, 95.4% macro F1-score, and an AUC-ROC area close to 1, and provided a clinically relevant heatmap of the lens opacity area. The contribution of this research is in combining two different CNN models with an interpretive approach that bridges the need for high accuracy and transparency in image-based medical applications, with potential applications in automated diagnosis systems and future telemedicine services.
Analisis Perbandingan Learnability antara Framework dan Native PHP pada Mahasiswa Informatika Universitas XYZ Jaya, Dery Yuswanto; Ulfah Siregar, Maria
Journal Information Technology Trends (JITRENDS) Vol 2 No 02 (2025): Journal Information Technology Trends Volume 02. No 02 Juni 2025
Publisher : mijournal.org

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51817/jitrends.v2i2.34

Abstract

Aplikasi tersebut merupakan salah satu syarat penyelesaian skripsi Mahasiswa Informatika Universitas XYZ. Dalam menyelesaikan skripsi, pengajuan skripsi harus cepat dan mudah karena waktu pengerjaan yang cukup singkat. Penelitian diperlukan karena akan membantu memberikan pemahaman tentang pembuatan website. Memiliki itu dapat membantu dalam mengimplementasikan situs web dengan cepat dan efektif. Dalam mencapai hal itu, siswa yang dituju akan dikonsep dan diarahkan dengan metode yang lebih tepat. Metode penelitian dilakukan dengan tinjauan pustaka, penyusunan instrumen, pengumpulan data, dan analisis data. Hasil yang diperoleh dari penelitian ini adalah secara umum Native PHP mempunyai tingkat Learnability yang lebih baik dibandingkan Framework. Namun perbedaannya praktis tidak signifikan. Dapat disimpulkan bahwa hasil penelitian perbandingan tingkat Learnability Framework dan Native PHP pada mahasiswa informatika menghasilkan sedikit perbedaan dikarenakan populasi yang sama yaitu mahasiswa informatika yang mempunyai pengalaman membuat aplikasi website dan sistem informasi dengan menggunakan keduanya platform.
Co-Authors Gumilang Hardandrito, Awan Yuswanto Jaya, Dery