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All Journal EKSAKTA: Journal of Sciences and Data Analysis SEINASI-KESI
-, Salsabila
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Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Earth & Planetary Sciences Materials Science & Nanotechnology

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Analisis Peminjaman Ruang Diskusi Perpustakaan Berbasis Web dengan Metode Objektif Oriented Sari, Rima Putri; -, Salsabila; Serinda, Fadila Asma; Lestari, Rizky Dwi; Rahayu, Tri
PROSIDING SEINASI-KESI Vol 2, No 1 (2023): SEMINAR NASIONAL INFORMATIKA, SISTEM INFORMASI, DAN KEAMANAN SIBER
Publisher : Fakultas Ilmu Komputer UPN Veteran Jakarta

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Abstract

AbstrakRuang diskusi merupakan sarana yang disediakan Universitas Pembangunan Nasional Veteran Jakarta dalam mendukung pembelajaran kolaboratif mahasiswa. Dalam peminjaman ruang diskusi sering kali timbul permasalahan, karena satu ruangan bisa digunakan untuk mengadakan banyak pertemuan dalam waktu yang bersamaan. Penelitian ini akan mengimplementasikan peminjaman ruang diskusi berbasis web menggunakan metode penelitian flowchart. Hasil penelitian metode ini terdapat Use Case Diagram, Activity Diagram, Class Diagram, State Diagram, Sequence Diagram, serta User Interface.Kata kunci:  Ruang Diskusi, UPNVJ, Universitas.
Optimasi Hyperparameter Model Klasifikasi Citra untuk Daging Sapi dan Babi Menggunakan Convolutional Neural Networks -, Salsabila; Anwar Fitrianto; Bagus Sartono
EKSAKTA: Journal of Sciences and Data Analysis VOLUME 6, ISSUE 2, October 2025
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/EKSAKTA.vol6.iss2.art6

Abstract

Deep learning classification network in one case, has different classification capabilities than the network in another. The classification method of deep learning using CNN has specific hyperparameters that can be adjusted to have good performance. These hyperparameters include the number of convolutional layers, the number of neurons in the convolutional and fully connected layers, kernel size, and activation functions. Deep Learning uses experimental principles in finding the best hyperparameter in various cases. The model architecture can be determined by choosing a different design. This research uses pork and beef images as the data for classification using CNN. The abstract textures of beef and pork may make it difficult for the CNN classification model to distinguish between them. Hence, 32 combinations of five hyperparameters were compared. It was found that these hyperparameters affect the model's performance. The best model has obtained 98,7% accuracy that uses 20 neurons both layers of the convolution was, kernel size of 5 × 5, ReLU activation function, and two fully connected layers with dropout 0.7 as a method of overfitting prevention. A significant difference also occurs in the application of the activation function, in which ReLU has a better performance than tanh function to increase the model's prediction.
Co-Authors Bagus Sartono Fitrianto, Anwar Lestari, Rizky Dwi Sari, Rima Putri Serinda, Fadila Asma Tri Rahayu