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All Journal SainsTech Innovation Journal Breakthroughs in Informatics, Networking, Algorithms, Research, and Yield (BINARY)
Yuan Sa’adati
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering

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Rancang Bangun Sistem Informasi Penjadwalan Berbasis Web Pada Laboratorium Kesehatan Universitas Qamarul Huda Badaruddin Kartiny Sinta Devi; Ramadhana Agung Pratama; Yuan Sa’adati; Fahmi Syuhada
SainsTech Innovation Journal Vol. 5 No. 1 (2022): SIJ Volume 5 Nomor 1 Mei 2022
Publisher : LPPM Universitas Qamarul Huda Badaruddin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37824/sij.v5i1.2022.421

Abstract

Laboratorium merupakan salah satu sarana belajar mahasiswa dan dosen untuk melakukan percobaan, penelitian yang terkait dalam kegiatan perkuliahan sesuai dengan kebutuhan bidang studi masing-masing. Fakultas Kesehatan Universitas Qamarul Huda Badaruddin belum mempunyai sistem informasi penjadwalan laboratorium, hal ini menyebabkan dosen yang melakukan permintaan jadwal secara langsung menemui pengelola laboratorium untuk melakukan pendaftaran praktikum. Selain itu pada aktivitas administratif berupa catatan laporan harian, mingguan dan bulanan yang berisi data dosen, dan permintaan jadwal praktikum menjadi kurang efisien dikarenakan masih menggunakan sistem manual. Penelitian ini merancang Sistem Informasi Penjadwalan Berbasis Web Pada Laboratorium Kesehatan Universitas Qamarul Huda Badaruddin. System dibuat menggunakan bahasa pemrograman html, php dan mysql sebagai database. Terdapat fitur yang dirancang pada system yaitu beranda, input data dosen, data dosen, menu validasi, laporan, permintaan jadwal, logout. Berdasarkan hasil suvei yang dilakukan ke pengguna didapatkan hasil pengujian yang baik dengan persentasi 80 % responden menjawab baik setelah melakukan uji coba.
Classification of Central Lombok Songket Motifs Using Convolutional Neural Network M. Danial Purna Fiki; Fahmi Syuhada; Yuan Sa’adati
Breakthroughs In Informatics, Networking, Algorithms, Research, And Yield (BINARY) Vol. 1 No. 1 (2025): Genesis of Innovation in Informatics and Networking
Publisher : Breakthroughs In Informatics, Networking, Algorithms, Research, And Yield (BINARY)

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Songket is a traditional Indonesian textile renowned for its high aesthetic and symbolic value. In Central Lombok, songket fabrics exhibit diverse motifs that reflect the region’s rich cultural identity. However, manual classification of these motifs is time-consuming and requires expert knowledge, limiting its scalability for digital preservation and cultural heritage documentation. This study proposes an automated classification system for Central Lombok songket motifs using a Convolutional Neural Network (CNN) based on the GoogLeNet (InceptionV3) architecture. The dataset comprises seven distinct motifs—namely Iket, Subhanale, Alang, Subhanale-Laek, Pangkeros, Mawar, and Merak—collected directly from the Sukarara weaving center. A total of 7000 images were used for training and 1400 for testing. The model was trained with 75% and 80% proportions of the dataset and evaluated using accuracy, precision, recall, F1-score, and confusion matrix metrics. Experimental results indicate that the CNN model achieved 97.97% accuracy with 75% training data and improved to 98.91% with 80% training data. These findings demonstrate that GoogLeNet is highly effective in classifying traditional songket motifs with high accuracy and computational efficiency. The proposed system offers significant potential for supporting the digital preservation of cultural assets and facilitating the development of AI-based tools for heritage documentation and creative industries.
Co-Authors Fahmi Syuhada Kartiny Sinta Devi M. Danial Purna Fiki Pratama, Ramadhana Agung