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Penerapan Algoritma Random Forest dengan Kombinasi Ekstraksi Fitur Untuk Klasifikasi Penyakit Daun Tomat Khultsum, Umi; Subekti, Agus
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 5, No 1 (2021): Januari 2021
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v5i1.2624

Abstract

The tomato plant is widely consumed by the community and is widely cultivated by farmers. Tomato plants are susceptible to disease attacks. Plant diseases cause a decrease in the quality and quantity of crops or agricultural produce. The idea of the 4.0 agricultural revolution emerged as a result of the 4.0 industrial revolution. Farmers are not ready to face increasingly rapid technological advances. It is important to identify the disease in tomato leaves correctly in the efficiency of disease management for efforts to control so that disease in tomato leaves does not develop. The main objective of the proposed method is to develop a technique for identifying foliar diseases in tomato plants by increasing the classification accuracy. The novelty of this research is a combination of several feature extractions to improve classification accuracy. The features used are the color feature, the Hu-Moment feature, and the firur haralick. In the classification process, the Random Forest algorithm and other classification algorithms are applied for comparison. In this study, the Random Forest method and the combination of extraction features have shown an increase in accuracy, the accuracy obtained is 96%.
Klasifikasi Gambar Palmprint Berbasis Multi-Kelas Menggunakan Convolutional Neural Network Hidayat, Taopik; Khasanah, Nurul; Saputri, Daniati Uki Eka; Khultsum, Umi; Pratiwi, Risca Lusiana
Jurnal Sistem Informasi Vol 11 No 1 (2022): JSI Periode Februari 2022
Publisher : LPPM STMIK ANTAR BANGSA

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (504.405 KB) | DOI: 10.51998/jsi.v11i1.474

Abstract

Abstract—Biometric technology is developing to be the most relevant mechanism in identity identification. The main purpose of an identity management system is to be able to establish a relationship between individuals and their identities when needed under certain conditions. Among the newly proposed identity verification and personal identification technologies, biometrics is rapidly becoming the most relevant mechanism for identity recognition. This study proposes a new biometric recognition method for authentication and personal identification. Palm image recognition based on image processing for authentication and personal identification is proposed, namely competitive coding using the Convolutional Neural Network (CNN) and Local Binary Pattern (LBP) texture extraction with hyperparameter modifications. The dataset used comes from the Birjand University Mobile Palmprint Database (BMPD) which consists of 20 classes with a total of 800 palm images. The research was conducted using a data distribution of 80% training data and 20% validation data. The tests carried out resulted in a good accuracy value of the proposed model of 93.3% for the training process and 90.6% for the validation process. Keywords: Biomethric, CNN, LBP Intisari— Teknologi biometrik berkembang menjadi mekanisme paling relevan dalam pengidentifikasi identitas. Tujuan utama dari sistem manajemen identitas adalah untuk dapat membangun hubungan antara individu dan identitas mereka ketika dibutuhkan dalam kondisi tertentu. Di antara verifikasi identitas yang baru diusulkan dan teknologi identifikasi pribadi, biometrik dengan cepat menjadi mekanisme yang paling relevan untuk pengenalan identitas. Penelitian ini mengusulkan metode pengenalan biometrik terbaru untuk otentikasi dan identifikasi pribadi. Pengenalan citra telapak tangan berbasis image processing untuk otentikasi dan identifikasi pribadi yang diusulkan yaitu pengkodean kompetitif menggunakan metode Convolutional Neural Network (CNN) dan ekstraksi tekstur Local Binary Pattern (LBP) dengan modifikasi hyperparameter. Dataset yang digunakan berasal dari Birjand University Mobile Palmprint Database(BMPD) yang terdiri dari 20 kelas dengan total 800 citra telapak tangan. Penelitian dilakukan dengan menggunakan distribusi data sebesar 80% data training dan 20% data validasi. Pengujian yang dilakukan menghasilkan nilai akurasi yang baik dari model yang diusulkan sebesar 93,3% untuk proses training dan 90,6% untuk proses validasi. Kata Kunci: Biometrik, CNN, LBP