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All Journal Prosiding SNATIKA Vol 01 (2011) Telematika Register: Jurnal Ilmiah Teknologi Sistem Informasi BERDIKARI : Jurnal Inovasi dan Penerapan Ipteks Jurnal Ilmu Komputer dan Desain Komunikasi Visual Jurnal Teknologi Informasi dan Pendidikan Insyst : Journal of Intelligent System and Computation
Nafi'iyah, Nur
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Arts Computer Science & IT Control & Systems Engineering Education Engineering Languange, Linguistic, Communication & Media

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Journal : Insyst : Journal of Intelligent System and Computation

 1 
Identifying Types of Corn Leaf Diseases with Deep Learning Firmansyah, Rahul; Nafi'iyah, Nur
Intelligent System and Computation Vol 6 No 1 (2024): INSYST: Journal of Intelligent System and Computation
Publisher : Institut Sains dan Teknologi Terpadu Surabaya (d/h Sekolah Tinggi Teknik Surabaya)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52985/insyst.v6i1.347

Abstract

The government is trying to increase corn yields to meet the Indonesian population's food needs and for export abroad. Some farmers have yet to gain experience with the types of diseases in corn, so they need tools or systems to guide and provide information to new farmers. Many previous studies have developed automatic systems to identify corn leaf diseases, with the goal of increasing corn crop production by early recognition and control. We propose a system for identifying types of corn leaf diseases using the CNN (Convolutional Neural Network) method to be more precise in recognizing corn diseases early on. The methods used in previous research mostly used deep learning with high accuracy results above 90%. CNN is one of the deep learning methods, so we use it to identify types of leaf diseases. Our data comes from Kaggle; we process it first. The Kaggle dataset has corn plants similar to those in Indonesia, so we use this data with identification classes (Blight, Common rust, Gray leaf spot, and Healthy). The training data is 2000 images with 500 images for each class, and the testing data is 120 images with 30 images for each class. The evaluation results show that the classification process using the CNN method has an accuracy of 84.5%. The results we produced for identifying types of corn leaf disease still lack accuracy in their prediction, indicating the need to improve the CNN architecture model.
Analisis Peramalan Stok Barang dengan Metode Weight Moving Average dan Double Exponential Smoothing pada Jovita Ms Glow Lamongan Nafi'iyah, Nur
Intelligent System and Computation Vol 1 No 1 (2019): INSYST: Journal of Intelligent System and Computation
Publisher : Institut Sains dan Teknologi Terpadu Surabaya (d/h Sekolah Tinggi Teknik Surabaya)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52985/insyst.v1i1.23

Abstract

Jovita MS Glow Lamongan merupakan agen yang menjual produk kecantikan dari brand MS Glow, produk yang dijual di antaranya perawatan wajah, tubuh, kosmetik dengan perkembangan penjualan dari bulan ke bulan semakin meningkat maka dibutuhkan perhitungan perkiraan jumlah barang yang akan dibeli untuk meramalkan persediaan barang bulan berikutnya. Persediaan barang yang tidak tepat dapat menimbulkan kerugian maka perlu adanya sistem peramalan. Oleh karena itu penelitian menggunakan metode Weight Moving Average dan Double Exponential Smoothing untuk menentukan nilai error yang lebih kecil. Data yang digunakan pada penelitian ini mulai bulan Januari 2015 sampai bulan Desember 2016. Metode Weight Moving Average yaitu metode yang memberikan bobot yang berbeda untuk setiap historis sedangkan Metode Double Exponential Smoothing merupakan metode yang memiliki nilai pemulusan dua kali pada waktu sebelum data sebenarnya. Hasil peramalan kedua metode ini menghasilkan nilai error Weight Moving Average yaitu 698.7180 dan Double Exponential Smoothing yaitu 1.429.1015, sehingga Weight Moving Average adalah metode yang tepat digunakan untuk meramalkan persediaan barang karena memiliki nilai error yang lebih kecil.
Co-Authors Endang Setyati Firmansyah, Rahul M., Kemal Farouq Maghfiroh, Syafaatul Nawafilah, Nur Qomariyah Retno Wardhani Sri Pamungkas, Arif Patriot Wardani, Retno Yosi Kristian