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All Journal Jurnal Nasional Komputasi dan Teknologi Informasi Smatika Jurnal : STIKI Informatika Jurnal
Nur Nafi'iyah
Universitas Islam Lamongan
Aerospace Engineering Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Neuroscience Transportation

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Penggunaan Metode CNN untuk Mengidentifikasi Penyakit Daun Anggur M Wahyu Anggara; Nur Nafi'iyah
Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) Vol 8, No 5 (2025): Oktober 2025
Publisher : Program Studi Teknik Komputer, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jnkti.v8i5.9818

Abstract

Abstrak - Penyakit daun anggur menjadi salah satu faktor utama yang menurunkan produktivitas dan kualitas tanaman anggur. Identifikasi manual yang dilakukan petani sering kali tidak akurat karena kemiripan gejala antar penyakit, sehingga diperlukan sistem otomatis yang cepat dan andal. Penelitian ini bertujuan mengembangkan sistem identifikasi penyakit daun anggur berbasis Convolutional Neural Network (CNN) dengan pendekatan pengolahan citra digital. Tiga arsitektur CNN dirancang dan dievaluasi menggunakan dataset PlantVillage yang dibagi menjadi 80% data latih dan 20% data validasi. Hasil pengujian menunjukkan Arsitektur 3 mencapai akurasi tertinggi sebesar 98%, diikuti Arsitektur 1 sebesar 97%, sementara Arsitektur 2 hanya 86%. Modifikasi dilakukan pada Arsitektur 2 dengan menambah jumlah filter konvolusi serta menghapus dropout, sehingga akurasinya meningkat signifikan menjadi 97%. Peningkatan terbesar terjadi pada kelas Healthy dari 62% menjadi 98%. Temuan ini membuktikan bahwa modifikasi arsitektur CNN mampu meningkatkan kinerja identifikasi penyakit daun anggur secara efektif.Kata kunci: CNN; daun anggur; identifikasi; pengolahan citra; penyakit tanaman; Abstract - Grape leaf diseases are one of the main factors that reduce the productivity and quality of grape plants. Manual identification by farmers is often inaccurate due to the visual similarity between symptoms, thus requiring a fast and reliable automated system. This study aims to develop a grape leaf disease identification system based on Convolutional Neural Networks (CNN) using a digital image processing approach. Three CNN architectures were designed and evaluated using the PlantVillage dataset, which was divided into 80% training data and 20% validation data. The experimental results show that Architecture 3 achieved the highest accuracy of 98%, followed by Architecture 1 with 97%, while Architecture 2 reached only 86%. Modifications were then applied to Architecture 2 by increasing the number of convolutional filters and removing the dropout layer, which significantly improved its accuracy to 97%. The most notable improvement occurred in the Healthy class, where accuracy increased from 62% to 98%. These findings demonstrate that modifying CNN architectures can effectively enhance the performance of grape leaf disease identification.Keywords: CNN; grape leaf; identification; image processing; plant disease;
Algoritma Backpropagation untuk Memprediksi Korban Bencana Alam Nur Nafi'iyah; Ahmad Ahmad Salaffudin1; Nur Qomariyah Nawafilah
SMATIKA JURNAL : STIKI Informatika Jurnal Vol 9 No 02 (2019): SMATIKA Jurnal : STIKI Informatika Jurnal
Publisher : LPPM UBHINUS MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/smatika.v9i02.400

Abstract

Indonesia is a country prone to natural disasters. Because Indonesia is a maritime country and its geographical area is Mount Merapi. In order to reduce victims of natural disasters or other disasters, we conducted research related to predictions of victims of natural disasters. The purpose of this study is to help the team or related parties in preparing themselves to deal with the victims of a growing natural disaster. The algorithm used in predicting victims of natural disasters is backpropagation. The data used in this study is the DIBI dataset taken from the Google dataset. The predicted impact was 5128 lines, 524 missing victims, 2653 injured, 941 lines dead. Each dataset with each category of disaster impacts, missing victims, injured victims, and death victims was made of 2 input variables. Input variables from each category are district code, and year and the output variable is the number of disaster victims. Neural network structure and architecture of this study, namely 2 input layer nodes, 2 hidden layer nodes, and 1 output layer node. From the architecture, training and testing were carried out, where the results of testing disaster impact data were 110 lines of MSE value of 0.0371, testing results of wounded victims data as much as 53 lines of MSE value of 0.0256, results of testing of missing victims as much as the 24 lines of the MSE value are 0.041, and the results of testing of the dead are 41 lines of the MSE value of 0.029.
Backpropagation untuk Memprediksi Jumlah Wisatawan Mancanegara ke Indonesia Kevin Aringgi Salim; Nur Nafi'iyah; Siti Mujilahwati
SMATIKA JURNAL : STIKI Informatika Jurnal Vol 11 No 02 (2021): SMATIKA Jurnal : STIKI Informatika Jurnal
Publisher : LPPM UBHINUS MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/smatika.v11i02.622

Abstract

Developing areas that have tourism potential is an effort to increase sources of income for villagers. Areas that have tourist areas can be a vehicle that attracts the attention of the public, both domestically and abroad. Tourists who come can provide income for tourist areas or the community. Therefore, predicting the number of incoming tourists can be predicted based on data from previous years. The goal is to make predictions to improve infrastructure and all needs for tourists. The purpose of this study is to apply the Backpropagation method to predict the number of foreign tourist visits to Indonesia. The dataset used in this study is 6000 lines and is divided into 4800 lines of training data, and 1200 lines of test data. The dataset is taken from the bps website, with the input variables being month, year, country of origin, tourist entrance to Indonesia, and the output variable being the number of tourists. The model of Backpropagation is evaluated by calculating MAE, and the architecture built is 4-9-1, 4 input layer nodes, 9 hidden layer nodes, and 1 output layer node. The test results of the MAE value of the Backpropagation method in predicting the number of tourists to Indonesia are 0.247.
Co-Authors Ahmad Ahmad Salaffudin1 Kevin Aringgi Salim M Wahyu Anggara Nawafilah, Nur Qomariyah Siti Mujilahwati