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All Journal OKTAL : Jurnal Ilmu Komputer dan Sains
Falah Nurdiansyah
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Astronomy Chemistry Computer Science & IT Electrical & Electronics Engineering Social Sciences

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LITERATUR RIVIEW: KLASIFIKASI PENYAKIT TANAMAN CABAI DENGAN PENDEKATAN CNN DAN TRANSFER LEARNING Falah Nurdiansyah; Leadrin Fandyani; Muhammad Ilyas Faisal; Reza Rohman Fadhilah
OKTAL : Jurnal Ilmu Komputer dan Sains Vol 3 No 11 (2024): OKTAL : Jurnal Ilmu Komputer Dan Sains
Publisher : CV. Multi Kreasi Media

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Diseases in chili plants are a serious problem that can significantly reduce crop yields and production quality, making early detection essential to assist farmers. This study uses a Convolutional Neural Network (CNN) approach with Transfer Learning methods to classify diseases on chili plant leaves. Infected chili leaf image data is processed and trained using a pre-trained CNN model to improve classification accuracy, even with limited data. The results show that this approach successfully identifies various types of diseases on chili leaves with a high level of accuracy. This approach is expected to be an effective solution for the agricultural sector to achieve faster and more efficient plant disease detection.
Sistem Informasi Manajemen Penjualan Berbasis Mern Stack untuk Konveksi Maxsupply Di Depok Anggio Marsoni; Falah Nurdiansyah; Muhammad Ilyas Faisal; Wasis Haryono
OKTAL : Jurnal Ilmu Komputer dan Sains Vol 4 No 07 (2025): OKTAL : Jurnal Ilmu Komputer Dan Sains
Publisher : CV. Multi Kreasi Media

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Abstract

Digital transformation in business operations emphasizes the need for integrated information systems, including in the garment industry. MaxSupply, a local garment business, requires a web-based sales information system to support more efficient and well-managed business processes. This study aims to design and develop a sales information system that facilitates product management, order processing, and digital payment. The system was developed using a prototyping method, allowing iterative improvements based on user feedback. It was built using the MERN Stack (MongoDB, Express.js, React.js, Node.js) and includes features such as user authentication, product management, transaction processing, and reporting. The results show that the system improves transaction speed and data accuracy, thereby supporting more structured and efficient operations for MaxSupply.
Co-Authors Anggio Marsoni Haryono, Wasis Leadrin Fandyani Muhammad Ilyas Faisal Reza Rohman Fadhilah