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All Journal Jurnal Teknologi Informatika dan Komputer International Journal Of Computer, Network Security and Information System (IJCONSIST) Journal of Data Science Theory and Application
Dina Zatusiva Haq
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Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Social Sciences

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Classification of Eye Diseases Using the AlexNet Convolutional Neural Network Model Algorithm Pratama, Moch Deny; Sultoni, Royal Fajar; Wardhani, Adil Sandy; Sechuti, Maulana Hassan; Yerezqy Bagus; Dina Zatusiva Haq; Yoga Ari Tofan
IJCONSIST JOURNALS Vol 7 No 1 (2025): September
Publisher : International Journal of Computer, Network Security and Information System

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/ijconsist.v7i1.160

Abstract

This study uses the Convolutional Neural Network (CNN) method with the AlexNet model to classify eye diseases based on medical images. The dataset includes labeled images of three types of eye diseases: cataract, glaucoma, and diabetic retinopathy. The experimental results show that the model achieved an accuracy of 75.18%, which indicates that CNN with the AlexNet architecture can classify eye diseases quite well. This research shows that deep learning can be used to help doctors or health professionals in diagnosing eye diseases through automatic image analysis. Although the accuracy still needs to be improved, this study can serve as a reference for developing an automated diagnostic system in the future. Further research is expected to increase accuracy, expand the dataset, and apply other deep learning techniques to improve the performance of eye disease detection.
Digital Business Model Development through the Implementation of a Smart Tuition Payment System Fitrani, Laqma; Angga Lisdiyanto; Masti Fatchiyah Maharani; Yerezqy Bagus; Dina Zatusiva Haq
Jurnal Teknologi Informatika dan Komputer Vol. 12 No. 1 (2026): Jurnal Teknologi Informatika dan Komputer
Publisher : Universitas Mohammad Husni Thamrin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37012/jtik.v12i1.3282

Abstract

  The tuition payment system is an essential component of school financial administration that supports educational operations. However, many schools still rely on manual or semi-digital payment processes, which often result in delayed transaction recording, data entry errors, and limited transparency in financial reporting. This study aims to develop a web-based online tuition payment application to improve the efficiency, accuracy, and transparency of school financial management. The research employed a qualitative descriptive approach with data collected through observation, interviews, and literature review. System development was conducted using the Agile method, allowing the application to be refined iteratively according to user needs. The system was implemented using PHP and MySQL and includes features such as student data management, tuition billing generation, payment recording, digital receipt generation, and real-time financial reporting. The results indicate that the developed system enhances administrative efficiency, reduces recording errors, and improves the timeliness and transparency of financial reports. Furthermore, the implementation of this system supports the achievement of Sustainable Development Goal (SDG) 4: Quality Education by strengthening governance and sustainability in educational services.
PERBANDINGAN LSTM DAN PROPHET DALAM PREDIKSI TREN BAJU LEBARAN 2026 BERBASIS GOOGLE TRENDS Maharani, Masti Fatchiyah; Laqma Dica Fitrani; Yerezqy Bagus; Dina Zatusiva Haq
Journal of Data Science Theory and Application Vol. 5 No. 1 (2026): JASTA
Publisher : LP3M Universitas Putra Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32639/tgeq5784

Abstract

Tren busana Lebaran di Indonesia menunjukkan pola musiman yang kuat dan berulang setiap tahun, sehingga prediksi yang akurat menjadi penting bagi perencanaan produksi dan strategi bisnis industri fesyen. Penelitian ini bertujuan membandingkan kinerja metode Prophet dan Long Short-Term Memory (LSTM) dalam memprediksi tren busana Lebaran tahun 2026 menggunakan data Google Trends periode 2018–2025. Model dilatih menggunakan data hingga Desember 2024 dan dievaluasi pada periode pengujian tahun 2025 menggunakan metrik Mean Absolute Error (MAE) dan Root Mean Squared Error (RMSE). Hasil menunjukkan bahwa LSTM memiliki akurasi numerik yang lebih baik, sedangkan Prophet lebih konsisten dalam menangkap pola musiman tahunan. Temuan ini menunjukkan bahwa pemilihan metode prediksi perlu disesuaikan dengan tujuan analisis. Penelitian ini berkontribusi dalam mendukung pengambilan keputusan berbasis data pada industri fesyen serta selaras dengan Sustainable Development Goals (SDGs) 8 terkait pertumbuhan ekonomi yang inklusif dan berkelanjutan.
Co-Authors Angga Lisdiyanto Fitrani, Laqma Laqma Dica Fitrani Maharani, Masti Fatchiyah Masti Fatchiyah Maharani Pratama, Moch Deny Sechuti, Maulana Hassan Sultoni, Royal Fajar Wardhani, Adil Sandy Yerezqy Bagus Yoga Ari Tofan