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All Journal G-Tech : Jurnal Teknologi Terapan
Agus Suhendar
Universitas Teknologi Yogyakarta, Indonesia
Computer Science & IT Decision Sciences, Operations Research & Management Energy Engineering

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Perancangan Aplikasi Pemesanan Tiket Pariwisata Berbasis Mobile di Kota Yogyakarta Dwiki Baskoro Pamungkas; Agus Suhendar
G-Tech: Jurnal Teknologi Terapan Vol 8 No 1 (2024): G-Tech, Vol. 8 No. 1 Januari 2024
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33379/gtech.v8i1.3973

Abstract

The development of the tourism sector in Sleman Regency is currently experiencing growth, which can be enhanced by increasing the number of tourists. One key factor supporting the growth of the tourism industry is the availability of comprehensive tourism information. Therefore, the idea has emerged to create an Android-based application with the aim of serving as a tourism promotion tool in Sleman. This application will provide information about tourist destinations in Sleman, including estimated costs required for visits. The application will be built using a Client-Server architecture, with the database server serving as the data storage location, the mobile application as the service receiver (Client), and the Webmaster as the service provider (Server). The application will also perform calculations for the ticket costs required by tourists when visiting specific destinations. This application offers services to visitors, information about the nearest routes to the location, ticket reservations, and online payments through the application.
Implementation of the Convolutional Neural Network (CNN) Algorithm for Pest Detection in Green Mustard Plants Gilang Wiwaha Soekarno; Agus Suhendar
G-Tech: Jurnal Teknologi Terapan Vol 9 No 1 (2025): G-Tech, Vol. 9 No. 1 January 2025
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/gtech.v9i1.6095

Abstract

Green mustard plants are of significant economic importance, making effective pest management essential. This study employed the Convolutional Neural Network (CNN) algorithm to detect pests on green mustard leaf images. The dataset, comprising 96 test images, was divided into two categories: pest-infested and healthy leaves. Using the NasNet Mobile architecture, the model was trained over 10 epochs with the Adam optimizer, achieving a training accuracy of 94.99% and a validation accuracy of 98.00%. Results indicate that CNN combined with NasNet Mobile effectively identifies pests, providing a robust and practical solution to enhance agricultural productivity and mitigate crop losses caused by pests. This study demonstrates the potential of leveraging deep learning for agricultural advancements, particularly in addressing pest-related challenges efficiently.
Co-Authors Dwiki Baskoro Pamungkas Gilang Wiwaha Soekarno