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All Journal RABIT: Jurnal Teknologi dan Sistem Informasi Univrab
Luluk Elvitaria
Abdurrab
Computer Science & IT Engineering

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PEMODELAN MOBILE GIS SEJARAH HIDUP NABI MUHAMMAD SAW BERBASIS ANDROID Luluk Elvitaria; LIza Trisnawati
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 4 No 1 (2019): Januari
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (338.533 KB) | DOI: 10.36341/rabit.v4i1.616

Abstract

Untuk mengambil suri teladan dari kehidupan nabi Muhammad SAW maka penulis tertarik untuk membuat sebuah aplikasi SIG untuk membantu pencarian tempat –tempat bersejarah dari perjalanan hidup nabi Muhammad disertai dengan keteladan yang dapat diambil dari setiap kejadian ditempat beliau berada dengan mudah, cepat dan informatif. Tujuan dalam penelitian ini adalah bagaimana membuat SIG tentang informasi Sejarah Hidup Nabi Muhammad SAW menggunakan sistem operasi Android. Hasil yang didapat dalam penelitian ini bagaimana menyajikan SIG Sejarah Hidup Nabi Muhammad SAW dapat diakses melalui mobile phone.
KLASIFIKASI JENIS JAMUR MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK (CNN) BERBASIS CITRA DIGITAL Luluk Elvitaria; Ira Puspita Sari; Lasiah Susanti; Zaerinisya Fitri
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 2 (2026): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i2.6933

Abstract

This reserach proposes a mushroom species classification method based on digital image processing using a Convolutional Neural Network (CNN). The EfficientNet-B4 architecture was adopted as the backbone model, employing a transfer learning approach followed by a fine-tuning process. The dataset consisted of 3,000 mushroom images categorized into 10 classes, with each class containing 300 images. The model implementation was carried out using Google Colab and the Python programming language. Model performance was evaluated using accuracy, precision, recall, and F1-Score metrics. Several model variations were examined by adjusting training parameters and data split ratios. The best-performing model, referred to as Model 1, utilized a customized freeze layer and applied an 80% training, 10% validation, and 10% testing data split, achieving the highest performance with 90.00% accuracy, 90.09% precision, 89.63% recall, and an 89.59% F1-Score. The findings indicate that applying a customized freeze layer effectively reduces the number of trainable parameters, leading to improved model accuracy. Furthermore, the selection of data split ratios contributes to performance differences during the training and testing phases.
Co-Authors Ira Puspita Sari Lasiah Susanti Liza Trisnawati Zaerinisya Fitri