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All Journal Decode: Jurnal Pendidikan Teknologi Informasi Jurnal Elektrosista
Maukar
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Humanities Automotive Engineering Computer Science & IT Control & Systems Engineering Education Engineering Environmental Science Languange, Linguistic, Communication & Media Other

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PEMBUATAN WEBSITE ARTIKEL OTOMOTIF MENGGUNAKAN FRAMEWORK LARAVEL DAN DATABASE MYSQL Rizky Bagus Syahputra; Maukar
JURNAL ELEKTROSISTA Vol. 12 No. 1 (2024): DESEMBER 2024
Publisher : PPM Sdirjianbang Akademi Militer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63824/jtep.v12i1.249

Abstract

MotoOto dibuat dengan metode SDLC dan berhasil menjadi wadah bagi para jurnalis otomotif untuk membagikan artikel yang telah dibuat kepada masyarakat luas dengan memenuhi aspek sistem, pengguna, dan interaksi.Website tersebut diberi nama MotoOto yang dibuat menggunakan framework Laravel dan database MySQL. MotoOto memiliki fitur posting, kategori, gambar, dan tag yang dapat mempermudah pengguna dalam melihat artikel. Penelitian ini menghasilkan sebuah aplikasi tentang Pembuatan Website Artikel Otomotif Menggunakan Framework Laravel dan Database MySQL.Penelitian ini membahas tentang membuat sebuah website yang digunakan untuk memberikan kemudahan bagi para jurnalis otomotif yang masih menggunakan sistem manual atau belum memiliki domain sendiri
A Transfer Learning Approach Using EfficientNetB0 for Automated Eczema Severity Detection Susetianingtias, Diana Tri; Maukar; Rodiah
Decode: Jurnal Pendidikan Teknologi Informasi Vol. 6 No. 3: NOVEMBER 2026
Publisher : Program Studi Pendidikan Teknologi Infromasi UMK

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51454/decode.v6i3.1848

Abstract

Eczema (atopic dermatitis) is a chronic inflammatory skin condition whose severity can be difficult to assess consistently from images. Although convolutional neural networks have been applied to dermatological classification, evidence on three-class eczema severity classification using a lightweight transfer-learning model and its implementation as a research prototype remains limited. This study developed an EfficientNetB0 classifier for Normal, Mild, and Severe classes using 1,950 source images. The images were processed at 224 × 224 pixels, augmented only in the training set, and trained in two stages: feature extraction and fine-tuning. The generator produced 1,560 training images and 389 development-validation images. The best model was selected by validation AUC and achieved 89.97% validation accuracy and an AUC of 0.9716. Normal had the highest recall (0.99), while Severe had the highest precision (0.95); most errors involved the Mild and Severe classes. The model was integrated into a browser-based prototype using Flask, HTML, Tailwind CSS, and JavaScript. Because the same validation subset was used for early stopping, checkpoint selection, and final reporting, these results should not be interpreted as independent test performance. The prototype is intended for research and educational use and does not replace professional diagnosis.
Co-Authors Rizky Bagus Syahputra Rodiah Susetianingtias, Diana Tri