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PEMBUATAN WEBSITE MARKETPLACE IKAN CUPANG MENGGUNAKAN PHP DAN MYSQL Kuwat Setiyanto
Jurnal Ilmiah Multidisiplin Vol. 1 No. 01 (2022): Januari : Jurnal Ilmiah Multidisiplin
Publisher : Asosiasi Dosen Muda Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (903.521 KB) | DOI: 10.56127/jukim.v1i1.123

Abstract

Seiring dengan perkembangan teknologi saat ini, sarana yang dapat digunakan untuk mencari informasi dan melakukan jual beli pun berkembang menjadi lebih modern. E-commerce menjadi tren yang saat ini sedang berkembang dengan menciptakan peluang baru bagi perusahaan dan konsumen. Jenis e-commerce yang berkembang di Indonesia adalah marketplace. Ikan cupang adalah ikan hias yang dikenal luas dan diminati oleh masyarakat. Hal tersebut membuat ikan cupang menjadi ikan hias yang memiliki nilai ekonomis tinggi. Oleh karenanya dibutuhkan sebuah Marketplace untuk membantu transaksi jual beli dengan dilengkapi fitur blog untuk mempermudah jual-belikan ikan cupang sekaligus memberikan tempat informasi terkait ikan cupang. Tujuan penelitian ini untuk membuat aplikasi berbasis website Marketplace Ikan Cupang menggunakan PHP dan MySQL sebagai database. Website ini memiliki dua halaman, yaitu halaman pelanggan dan halaman admin. Halaman pelanggan memiliki dashboard dengan menu daftar, login, merchant, home, status transaksi. Pada halaman user terdapat menu keranjang belanja, pencarian, bantuan, status transaksi, dan buat toko. Halaman admin terdapat dashboard admin yang berisi menu blog, data member, konfirmasi, data toko, data pajak, pesanan, pendapatan merchant, dan bukadompet. Metode penelitian yang digunakan peneliti dalam pembuatan website adalah SDLC (Software Development Life Cycle), perancangan menggunakan Unified Modeling Language (UML) dan pengujian menggunakan metode blackbox testing untuk memastikan website dapat berjalan sesuai fungsi yang diinginkan. Website marketplace dapat dakses melalui https://betafisher.000webhostapp.com/.
APLIKASI PENJUALAN WAGHE COFFEE BERBASIS WEBSITE MENGGUNAKAN PHP AND MY SQL DENGAN FRAMEWORK BOOTSTRAP Kuwat Setiyanto; Hafidh Raihan Al Ghifari
Jurnal Ilmiah Teknik Vol. 2 No. 2 (2023): Mei : Jurnal Ilmiah Teknik
Publisher : Asosiasi Dosen Muda Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56127/juit.v2i2.784

Abstract

Website-Based Waghe Coffee Sales Application. In an era that is all modern like now, technology has a big role in society, people are very dependent on technology and because of that people want services that are easy to access, fast and efficient. The Waghe Coffee Sales Application was built using Visual Studio Code using PHP, MySQL Database and phpMyAdmin, XAMPP and using the Bootstrap Framework, as well as several research methods to get maximum results by means of data collection, application design, implementation and testing. This application has been tested with a blackbox and tested using a computer and mobile with each using 2 different web browsers and 2 different devices and testing the website display. The facilities provided by this application are baskets that assist customers in purchasing goods when purchasing products and there are contacts for customers to contact the admin as well as a payment method by cash on delivery (COD). 
ANALISIS PERBANDINGAN HASIL KLASIFIKASI JENIS PENYAKIT TANAMAN TOMAT MENGGUNAKAN ARSITEKTUR MOBILENET, DENSENET121, DAN XCEPTION Kuwat Setiyanto; Michael Bolang
Jurnal Teknik dan Science Vol. 3 No. 3 (2024): Oktober: Jurnal Teknik dan Science
Publisher : Asosiasi Dosen Muda Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56127/jts.v3i3.1898

Abstract

Machine learning can be applied in various needs, such as image classification. Plant disease classification is essential and significantly supports the agricultural sector in this modern era. With an application capable of classifying diseases in crops, farmers can accurately identify the diseases affecting their harvest and address them more efficiently and effectively compared to traditional methods, which can be more time-consuming. This research aims to determine the best TensorFlow architecture among the three architectures used in this study, namely MobileNet, DenseNet121, and Xception, to classify 9 types of tomato plant diseases and 1 healthy tomato plant. The study concludes that DenseNet121 is the best architecture for classifying the 9 types of tomato plant diseases and 1 healthy tomato plant. During testing, the DenseNet121 model achieved an accuracy, precision, recall, and F-1 score of approximately 0.987 or 98.7%. Xception ranked second with all four metrics scoring around 0.986 or 98.6%, while MobileNet ranked last with metrics scoring approximately 0.973 or 97.3%.