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All Journal OKTAL : Jurnal Ilmu Komputer dan Sains Global Journal of Educational Technology (GJET)
Ridwan Firdaus Haryono
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Astronomy Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Medicine & Pharmacology Nursing Social Sciences

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Literature Review: Penggunaan CNN dalam Klasifikasi Penyakit pada Tanaman Buah Apel Muhamad Choirul Anwar; Januardy Ahda Setia Murad; Ridwan Firdaus Haryono; Saddam Alifio
OKTAL : Jurnal Ilmu Komputer dan Sains Vol 3 No 10 (2024): OKTAL : Jurnal Ilmu Komputer Dan Sains
Publisher : CV. Multi Kreasi Media

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

Abstract

This study focuses on the classification of diseases in apple plants using deep learning methods, particularly Convolutional Neural Networks (CNN). A primary challenge in agricultural management is the early detection of plant diseases, as failure to identify them promptly can lead to significant losses in yield. In this study, various CNN methods were explored to enhance the accuracy of disease detection and computational efficiency. Data were collected from relevant scientific journals, and a literature review was conducted on five main journals that implemented CNN techniques and hybrid methods. The research findings indicate that data preprocessing techniques, such as data augmentation and image segmentation, play a critical role in improving model performance. Hybrid models that combine CNN with other methods, such as RNN, also showed improvements in accuracy and real-time detection capabilities. In conclusion, the implementation of CNN methods tailored to specific needs, combined with appropriate data preprocessing, can provide effective solutions for the rapid and accurate detection and classification of plant diseases.
Perancangan Sistem Kasir Berbasis Web Menggunakan Model Prototype Pada CV. Benua Battery Lestari Untuk Meningkatkan Efisiensi Transaksi Alessandro; Azzani Nurfadia Rizky; Ridwan Firdaus Haryono; Wasis Haryono
GJET : Global Journal of Educational Technology Vol. 1 No. 4 (2025): Desember 2025
Publisher : Perhimpunan Ahli Teknologi Informasi dan Komunikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.71234/gjet.v1i4.74

Abstract

CV. Benua Battery Lestari merupakan perusahaan yang bergerak di bidang distribusi aki kendaraan dan masih menerapkan sistem kasir manual dalam aktivitas penjualannya. Penggunaan sistem manual tersebut menimbulkan berbagai kendala, seperti lamanya proses transaksi, tingginya potensi kesalahan perhitungan, serta kesulitan dalam pemantauan stok dan penyusunan laporan keuangan secara real-time. Penelitian ini bertujuan untuk merancang sistem kasir berbasis web dengan pendekatan model prototype guna meningkatkan efisiensi transaksi. Pengembangan sistem dilakukan melalui tahapan identifikasi kebutuhan pengguna, pembuatan prototipe awal, evaluasi pengguna, perbaikan prototipe, hingga implementasi sistem final. Hasil pengujian menunjukkan bahwa sistem yang dibangun mampu mempercepat proses transaksi, meminimalkan kesalahan pencatatan, serta mempermudah pemantauan data penjualan dan stok barang secara daring. Selain itu, sistem ini memberikan fleksibilitas akses bagi pihak manajemen dan memiliki potensi untuk dikembangkan lebih lanjut melalui integrasi dengan modul laporan keuangan dan manajemen inventori
Co-Authors Alessandro Azzani Nurfadia Rizky Haryono, Wasis Januardy Ahda Setia Murad Muhamad Choirul Anwar Saddam Alifio