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All Journal EDUMATIC: Jurnal Pendidikan Informatika Jurnal Riset dan Aplikasi Mahasiswa Informatika (JRAMI)
Triginandri, Rifqi
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Computer Science & IT Education

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Deteksi Dini Cacar Monyet menggunakan Convolutional Neural Network (CNN) dalam Aplikasi Mobile Triginandri, Rifqi; Subhiyakto, Egia Rosi
Jurnal Pendidikan Informatika (EDUMATIC) Vol 8 No 2 (2024): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v8i2.27625

Abstract

Monkeypox is a skin infection that has become a serious concern in Indonesia since the increase in cases in 2022. Diagnosis of monkeypox requires special expertise, laboratory tests, and clinical observations. Diagnosis generally uses PCR tests which are often not available in remote areas. This study aims to develop a deep learning-based mobile application for early detection of monkeypox through image classification of skin lesions. The CRISP-DM methodology is applied in developing this application, starting with collecting datasets from the Kaggle site consisting of 8,910 images and divided into 80% training groups, 10% validation, and 10% testing with augmentation techniques to improve model accuracy. The developed CNN model was implemented using Create ML on the iOS platform. The model evaluation uses several metrics such as accuracy, precision, recall, and F1 score, with the threshold being the highest probability of the model predicting model evaluation results show an accuracy of 81%, precision of 80.2%, recall of 76%, and F1 score of 0.78 for the test data. The resulting application allows rapid detection of monkeypox and is accessible to the wider community, thereby helping to reduce delays in diagnosis, especially in hard-to-reach areas. This study shows significant potential in supporting the health system in Indonesia through the application of artificial intelligence technology for infectious diseases.
Implementasi Firebase Realtime Pada Aplikasi Self-Order Restoran Berbasis iOS Triginandri, Rifqi; Septiani, Karlina Dwi; Subhiyakto, Egia Rosi; Rakasiwi, Sindhu; Astuti, Yani Parti
Jurnal Riset dan Aplikasi Mahasiswa Informatika (JRAMI) Vol 5, No 04 (2024): Jurnal Riset dan Aplikasi Mahasiswa Informatika (JRAMI)
Publisher : Universitas Indraprasta PGRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/jrami.v5i4.10680

Abstract

Perkembangan teknologi aplikasi memberikan dampak signifikan pada gaya hidup masyarakat modern, terutama dengan hadirnya aplikasi inovatif seperti Pick 'n Serve yang mengatasi antrian di restoran melalui platform mobile iOS. Dikembangkan dengan Firebase Realtime Database, aplikasi ini memastikan akses real-time terhadap informasi menu, pesanan, dan ketersediaan. Integrasi model bisnis, perhatian terhadap kebutuhan pelanggan, dan penerapan metode pengembangan Waterfall membuat Pick 'n Serve berhasil meningkatkan efisiensi dan pengalaman pemesanan. Metode Waterfall memberikan struktur pengembangan terorganisir dengan langkah-langkah jelas dari perencanaan hingga implementasi. Hasil pengujian mencerminkan kinerja baik, memudahkan pengguna dalam pemesanan, dan efektif mengurangi antrian di restoran. Skor tinggi dari uji penerimaan pengguna (UAT) mencerminkan penerimaan positif terhadap aplikasi ini. Dengan memanfaatkan teknologi Firebase dan pendekatan pengembangan yang terstruktur, Pick 'n Serve diharapkan memberikan kontribusi positif terhadap efisiensi operasional, kepuasan pelanggan, dan pertumbuhan bisnis restoran dalam era digital, menjadikannya solusi holistik untuk adaptasi terhadap perubahan kebutuhan konsumen dan tuntutan pasar yang terus berkembang.
Co-Authors Astuti, Yani Parti Egia Rosi Subhiyakto, Egia Rosi Septiani, Karlina Dwi Sindhu Rakasiwi