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All Journal OKTAL : Jurnal Ilmu Komputer dan Sains AI dan SPK : Jurnal Artificial Intelligent dan Sistem Penunjang Keputusan
Raihan Salman Al Parisy
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Aerospace Engineering Astronomy Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Social Sciences

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Klasifikasi Penyakit Bercak Daun Pada Tanaman Gandum Menggunakan Metode Convolutional Neural Network Raihan Salman Al Parisy; Damars Alfi Syahri; Reyvalqy; Chairina Fachrunnida
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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Wheat leaf diseases such as yellow rust and powdery mildew are very harmful to wheat yields worldwide. It is important to detect these diseases as early as possible so that losses can be minimized. In this work, we have used lightweight convolutional neural networks (CNNs) and Transformer-based methods to detect wheat leaf diseases under complex environmental conditions. In the first study, we tried several lightweight CNN models, such as MobileNetV3, ShuffleNetV2, GhostNet, MnasNet, and EfficientNetV2. These models were trained using different learning methods and achieved the highest accuracy of 98.65% using MnasNet and a fine-tuned learning rate. The second study focused on detecting yellow rust with UNET Segmentation and Swin Transformer classification methods. They achieved 95.8% accuracy in the field without manual intervention. These studies created a complete pipeline, including finding and delimiting wheat leaves from a complex background. They used YOLOv8 to quickly find leaves, then performed Segmentation and classification. The results showed that the combination of Segmentation, lightweight CNN, and Transformer techniques can handle leaf disease detection in nature with different backgrounds. This system has high accuracy and good efficiency for use in the field. This method can help the development of smart agricultural applications by accelerating and facilitating automatic detection of wheat leaf diseases. Using technologies such as Convolutional neural networks, Transformers, and Segmentation to overcome complex backgrounds.
Pengembangan Sistem Kasir Berbasis Web untuk Meningkatkan Efektivitas Transaksi pada Usaha Toko Kue Daffa Adhi Pramana Suwarno; Raihan Salman Al Parisy; Reyvalqy; Wasis Haryono
AI dan SPK : Jurnal Artificial Intelligent dan Sistem Penunjang Keputusan Vol. 3 No. 1 (2025): Jurnal AI dan SPK : Jurnal Artificial Inteligent dan Sistem Penunjang Keputusan
Publisher : CV. Shofanah Media Berkah

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Penelitian ini bertujuan untuk menerapkan sistem kasir bebasis web pada Toko Kue DAFFA sebagai upaya meningkatkan efisiensi dalam pencatatan transaksi serta pengelolaan persediaan produk. Model pengembangan sistem yang digunakan adalah metode waterfall, yang terdiri dari tahapan analisis kebutuhan, desain sistem, implementasi, dan pengujian. Aplikasi yang dirancang mendukung digitalisasi proses penjualan, pengelolaan stok barang, serta penyusunan laporan penjualan secara waktu nyata (real-time). Berdasarkan hasil pengujian, aplikasi ini terbukti mampu meminimalkan kesalahan pencatatan, mempercepat transaksi, dan meningkatkan ketepatan data dalam laporan penjualan. Penerapan sistem ini juga memberikan kontribusi terhadap digitalisasi UMKM agar lebih siap menghadapi dinamika bisnis di era modern.
Co-Authors Adhi Pramana Suwarno Chairina Fachrunnida Damars Alfi Syahri Haryono, Wasis Reyvalqy