scholarly journals Identifikasi Kematangan Cabai Menggunakan Operasi Morfologi (Opening dan Closing) dan Metode Backpropagation

SISTEMASI ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 96
Author(s):  
Khairullah Khairullah ◽  
Erwin Dwika Putra
Keyword(s):  
Image Processing ◽  
Digital Image Processing ◽  
Confusion Matrix ◽  
Training Data ◽  
Morphological Operations ◽  
Morphological Operation ◽  
Backpropagation Method ◽  
Grouping Method ◽  
Opening And Closing

AbstrakIdentifikasi kualitas buah cabai biasanya masih menggunakan cara visual secara langsung atau sortir secara manual oleh petani, dengan menggunakan sistem ini sering kali terjadi beberapa kesalahan setiap melakukan sortir yang disebabkan oleh petani yang melakukan sortir merasa terlalu lelah. Dengan menggunakan komputasi pengolahan citra digital, untuk melakukan identifikasi pengelompokan buah cabai yang matang dan mentah dapat membantu para petani, Teknik pengelompokan ini akan menggunakan metode pengelompokan berdasarkan warna. Metode pengelompokan tersebut sebelumnya akan dilakukan operasi morfologi pada citra yang telah diambil. Pendekatan operasi morfologi pada penelitian ini adalah Opening and Closing, pada operasi morfologi akan menghilangkan noise dan menebalkan objek dari inputan gambar. Metode Bacpropagatioan akan mengolah data latih sebanyak 10 data latih mendapatkan 6 iterasi perhitungan dan setelah diuji menggunakan data uji hasil yang didapatkan yaitu tingkat pengenalan rata-rat mendapatkan perhitungan sebanyak 7 iterasi metode Bacpropagation. Hasil dari penelitian ini juga dihitung menggunakan Confusion Matrix dimana nilai Precision 90%, Recall 74%, dan Accuracy 70%, maka dapat disimpulkan bahwa Operasi Morfologi dan Metode Backpropagation dapat digunakan untuk mengidentifikasi objek cabai.Kata Kunci: backpropagation, morfologi, identifikasi, opening and closing  AbstractIdentification of the quality of chili fruit is usually still using a visual way directly or sorting manually by farmers, using this system often occurs several errors, every sorting caused by farmers who do the sorting feel too tired. By using digital image processing computing, to identify the grouping of ripe and raw chili fruits can help farmers, this grouping technique will use a method of grouping based on color. The grouping method will previously perform morphological surgery on the image that has been taken. The morphological operation approach in this study is Opening and Closing, in morphological operations will eliminate noise and thicken objects from image input. Bacpropagatioan method will process training data as much as 10 training data get 6 iterations of calculations and after being tested using the test data obtained results that is the level of introduction of the average rat get a calculation of 7 iterations bacpropagation method. The results of this study were also calculated using Confusion Matrix where precision values of 90%, Recall 74%, and Accuracy 70%, it can be concluded that Morphological Operations and Backpropagation Method can be used to identify chili objects.Keywords: backpropagation, morfologi, identification, opening and closing

scholarly journals APLIKASI PENGENALAN PLAT NOMOR KENDARAAN DI UNIVERSITAS RIAU

2019 ◽  
Vol 9 (3) ◽  
pp. 47-52
Author(s):  
Noprizal ◽  
Feri Candra
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Image Processing ◽  
Artificial Neural Network ◽  
Digital Image Processing ◽  
Digital Image ◽  
Training Data ◽  
License Plate ◽  
Backpropagation Method ◽  
Artificial Neural

Abstract Vehicle license plate recognition application has been found in shopping centers, university, and other agency buildings with various methods of recognition. Some examples of methods used such as digital image processing techniques, neural networks and so forth. This study makes an application for the introduction of license plates, especially for student vehicle license plates in the university area. This application is developed with Digital Image Processing Methods and Artificial Neural Networks. In this study, 900 training data are used, taken from 200 photo vehicle number plates, to train 36 characters that contain 26 alphabets and 10 decimal numbers. The training data is used to test 30 photos of vehicle license plates. Plate photos used as training and testing data are the Indonesian standard with black and white plates. Artificial Neural Network used to recognize vehicle license plate by using the Backpropagation method with parameters Epoch 1000, Hidden layer1 with node 60, Hidden layer2 with node 55, Goal 0.001. The final conclusion of this Study shows that the use of Artificial Neural Network Backpropagation method is very good, with the best testing accuracy obtained, namely 98% and 1.25 error. Keywords : digital image processing, artificial neural networks, vehicle license plate Abstrak Aplikasi pengenalan plat nomor kendaraan sudah banyak ditemukan di pusat perbelanjaan, universitas, dan gedung instansi dengan berbagai metode pengenalan. Beberapa contoh metode yang digunakan seperti teknik pengolahan citra digital, jaringan syaraf tiruan dan lain sebagainya. Disini penulis membuat sebuah aplikasi pengenalan plat nomor kendaraan khususnya untuk plat nomor kendaraan mahasiswa yang ada dilikungan Universitas Riau. Aplikasi ini dikembangkan dengan metode pengolahan citra digital dan jaringan syaraf tiruan. Pada penelitian ini, digunakan 700 data pelatihan yang diambil dari 200 foto plat nomor, untuk melatih 36 karakter. Data pelatihan tersebut kemudian digunakan untuk menguji 30 foto plat nomor kendaraan. Foto plat yang dijadikan untuk data pelatihan dan pengujian yaitu plat standar indonesia yang berwarna hitam dan putih. Jaringan syaraf tiruan yang digunakan untuk melakukan pengenalan yaitu dengan Metode Backpropagation dengan parameter Epoch 1000, Hidden layer1 dengan jumlah node 60, Hidden layer2 dengan jumlah node 55, Goal  0,001. Kesimpulan akhir dari penelitian ini yaitu menunjukan bahwa penggunaan Metode Backpropagation jaringan syaraf tiruan ini sangat bagus, dengan akurasi pengujian terbaik yang didapat yaitu 98% dengan eror 1,25. Kata kunci: pengolahan citra digital, jaringan syaraf tiruan, Backpropagation, plat nomor  

IMPLEMENTASI METODE OPERASI MORFOLOGIS DALAM PERBAIKAN HASIL SEGMENTASI CITRA DIGITAL GORGA BATAK

2018 ◽  
Vol 2 (1) ◽  
Author(s):  
Ojahan Sihombing ◽  
Efori Buulolo ◽  
Henry Kristian Siburian
Keyword(s):  
Image Processing ◽  
Digital Image Processing ◽  
Digital Image ◽  
Operation Technique ◽  
Regional Culture ◽  
Human Beings ◽  
Morphological Operation ◽  
Research Technology ◽  
Operation Method ◽  
Image Improvement

As the development of research technology on Digital Image Processing continues to grow. Likewise, the improvement of the quality of sharpness / subtlety of the Gorga Batak images is an important thing to improve. This is one of the ways to preserve the Batak tribe area so that Gorga-gorga are still remembered and more interpreted. The cause of the need to be improved is the image of Gorga Batak is caused by several factors that cause the image to be less beautiful if it is interpreted by human beings such as the shape has been blurred (dark) due to shooting / shooting, has noise black spots on the image (noise), and the color is dull out of date. As an effort to improve image, the segmentation process is carried out by doing edge detection on the image, then the Morphological Operation Method will be implemented as one of the methods in Digital Image Processing that implements image quality improvement based on the shape and structure of the image. In this image processing, the Dilation Operation Technique and Operation Technique will be carried out. In Operation Dilation Techniques works by adding several segments (pixels) in the image so as to increase the integrity / sharpness of the structure of the image. While the Erosion Operation Technique will reduce / refine unnecessary parts / segments of the image so that the resulting image looks smoother, so that it can be more interpreted by humans and can be reused both as documentation of regional culture and so on. Using this method is expected to be able to improve and improve the quality / sharpness of Citra Gorga Batak. To facilitate the operation of the program design tools will be used, namely Matlab.Keywords: Image Improvement, Gorga Batak, Morphological Operation Method

scholarly journals Development of color guides to evaluate the maturity of cacao clones by digital image processing

2021 ◽  
Vol 51 ◽  
Author(s):  
Maria Cristina García-Muñoz ◽  
Martha Patricia Tarazona-Díaz ◽  
Nixon Andres Meneses-Marentes ◽  
Gabriela González-Sarmiento ◽  
Ana Sofía Pineda-Guerrero ◽  
...  
Keyword(s):  
Image Processing ◽  
Digital Image Processing ◽  
Digital Image ◽  
Raw Material ◽  
Maturity Index ◽  
Maturity Stage ◽  
Material Homogeneity ◽  
By Products ◽  
Production Industry

ABSTRACT Raw material homogeneity is one of the most requested characteristics in any production industry, and the cacao industry is no exception. However, there are many factors that affect the final quality of fruits, among them the variety and maturity stage. The present study aimed to create color tables for evaluating the maturity index of the ICS06, CCN51 and EET8 cacao clones, using digital image processing, in order to contribute for the quality and final homogeneity of the fruits and their by-products.

scholarly journals Quality Evaluation of Melinjo Seeds (Gnetum gnemon L.)Using Digital Image Processing

2018 ◽  
Vol 7 (2) ◽  
pp. 106 ◽  
Author(s):  
Slamet Widodo ◽  
Muhammad Kalili
Keyword(s):  
Image Processing ◽  
Digital Image Processing ◽  
Digital Image ◽  
Quality Evaluation ◽  
Seed Extract ◽  
Raw Material ◽  
Key Factors ◽  
Gnetum Gnemon ◽  
Lighting Systems

Some studies show that melinjo (Gnetum gnemon L.) seed extract contains various active ingredients that are beneficial to human health; even it has been commercialized as a health supplement product. Quality of seeds as raw material becomes one of key factors that determine the quality of product derived from melinjo seed extract. Therefore sorting becomes a critical process. However the sorting of good quality and broken seeds (moldy, chalky and perforated/infected insects) is still done manually with visual observations that tend to be inaccurate and inconsistent. This study aims to develop a new method for evaluation of quality of melinjo seeds based on digital image processing. The image is taken using two lighting systems i.e. frontlight and backlight. The results show that using color features (RGB and HSV) and certain threshold values, good quality and broken seeds can be distinguished by 92.5% and 100% accuracy using frontlight and backlight image respectively. It indicates that digital image processing can be used as an alternative for quality evaluation of melinjo seed.

scholarly journals The Segmentation of Neutron Digital Radiography Image through the Edge Detection Method

2020 ◽  
Vol 10 (1) ◽  
pp. 11
Author(s):  
Ayu Fitri Amalia ◽  
Widodo Budhi
Keyword(s):  
Image Processing ◽  
Edge Detection ◽  
Digital Image Processing ◽  
Neutron Beam ◽  
Digital Radiography ◽  
Digital Image ◽  
Digital Images ◽  
Neutron Radiography ◽  
Detection Methods

The digital image processing is one way to manipulate one or more digital images. Image segmentation has an essential role in the field of image analysis. The aim of this study was to develop an application to perform digital image processing of neutron digital radiographic images, hoping to improve the image quality of the digital images produced. The quality of edge detection could be used for the introduction of neutron digital radiographic image patterns through artificial intelligence. Interaction of neutrons with the matter mainly by nuclear reaction, elastic, and inelastic scattering. A neutron can quickly enter into a nucleus of an atom and cause a reaction. It is because a neutron has no charge. Neutrons can be used for digital imaging due to high-resolution information from deep layers of the material. The attenuated neutron beam in neutron radiography are passing through the investigated object. The object in a uniform neutron beam is irradiated to obtain an image neutron. The technique used in segmenting the neutron radiography in this study was a digital technique using a camera with a charge-coupled device (CCD), which was deemed more efficient technique compared to the conventional one. Through this technique, images could be displayed directly on the monitor without going through the film washing process. Edge detection methods were implemented in the algorithm program. It was the first step to complement the image information where edges characterize object boundaries. It is useful for the process of segmenting and identifying objects in neutron digital radiography images. The edge detection methods used in this study were Sobel, Prewitt, Canny, and Laplacian of Gaussian. According to the results of the image that have been tested for edge detection, the best image was carried out by the Canny operator because the method is more explicit. The obtained edges were more connected than the other methods which are still broken. The Canny technique provided edge gradient orientation which resulted in a proper localization.

Digital image processing for evaluating the quality of the film of a holographic plate

1995 ◽  
Author(s):  
Chern-Sheng Lin ◽  
I-Liang Chih ◽  
Hung J. Shieh ◽  
Rang-Seng Chang
Keyword(s):  
Image Processing ◽  
Digital Image Processing ◽  
Digital Image

scholarly journals Shrimps clusterization by size using digital image processing with CCA and DBSCAN

2020 ◽  
Vol 8 (2) ◽  
pp. 106-112
Author(s):  
Adri Priadana ◽  
Aris Wahyu Murdiyanto
Keyword(s):  
Image Processing ◽  
Light Intensity ◽  
Digital Image Processing ◽  
Digital Image ◽  
Spatial Clustering ◽  
Digital Camera ◽  
Harvest Time ◽  
Connected Component ◽  
Average Accuracy

The quality of farmed shrimps has several criteria, one of which is shrimp size. The shrimp selection was carried out by the contractor at the harvest time by grouping the shrimp based on their size. This study aims to apply digital image processing for shrimp clustering based on size using the connected component analysis (CCA) and density-based spatial clustering of applications with noise (DBSCAN) methods. Shrimp group images were taken with a digital camera at a light intensity of 1200-3200 lux. The clustering results were compared with clustering from direct observation by two experts, each of which obtained an accuracy of 79.81 % and 72.99 % so that the average accuracy of the method was 76.4 %.

scholarly journals Research on Germination Prediction and Varietal Classification for Rice Seed Quality

2019 ◽  
Vol 9 (1) ◽  
pp. 3956-3959
Keyword(s):  
Image Processing ◽  
Digital Image Processing ◽  
Digital Image ◽  
Seed Quality ◽  
Germination Rate ◽  
Rice Seed ◽  
Image Processing Techniques ◽  
Varietal Classification ◽  
Processing Techniques

In agriculture most of the task done manually by experienced persons. They made decision on the basis of what they feel and see. The prediction result also not giving expected results. For getting the best yield the selection of quality seed is mandatory. But the manual analysis cannot assure the best quality seed. Rice Seed quality estimation can be done by considering the textural features of rice seed image. For this we are going to propose Digital Image processing Techniques to classify and grade the quality of the seed. There are number of digital image processing techniques proposed for classifying the variety of seed and predicting the germination rate of seed. In this paper we are going to summarize the hardware setup, varieties, features extracted, methods or algorithms used and result they obtained. In future we are going to propose a simple grading system for the rice seed quality system can be used by formers.

scholarly journals Machine Learning Techniques for Prediction of Lung Cancer

2020 ◽  
Vol 8 (6) ◽  
pp. 241-249
Keyword(s):  
Machine Learning ◽  
Lung Cancer ◽  
Data Mining ◽  
Image Processing ◽  
Digital Image Processing ◽  
Digital Image ◽  
Learning Algorithm ◽  
Confusion Matrix ◽  
Machine Learning Techniques ◽  
Image Dataset

Lung cancer has been one of the deadliest diseases in today’s decades. It has become one of the causes of death in both man and woman. There are various reasons for which lung cancer occurs but classification of tumor and predicting it in the right stage is the most important part. This paper focused on the numerous approaches has been derived for lung cancer detection from different literature survey to advance the ability of detection of cancer. Digital image processing and data mining both are equally important because for prediction either image dataset or statistical dataset is used so for pre-processing the image dataset digital image processing is applied for statistical dataset data mining is applied. After pre-processing, segmentation and feature extraction we apply various machine learning algorithm for the prediction of lung cancer. So first we have provided a sketch of Machine learning and then various fields like in image data or statistical data where machine learning has been used for classification. Once the classification is done confusion matrix is generated for calculating accuracy, sensitivity, precision, these method is used to measure the rate of accuracy of the proposed model.

A Novel Modified Algorithm for Local Statistic Enhancement

2014 ◽  
Vol 926-930 ◽  
pp. 2910-2913
Author(s):  
Xiao Ling Song
Keyword(s):  
Genetic Algorithm ◽  
Image Processing ◽  
Digital Image Processing ◽  
Rubber Band ◽  
Conversion Algorithm ◽  
Restoration Techniques ◽  
Cultural Relic ◽  
Modified Algorithm ◽  
Novel Algorithm

Image repair using the digital image processing technology has become a new hot point in the cultural relic protection. To study of ancient fresco restoration techniques, A novel algorithm of local statistic enhancement image is proposed in this paper for the reparation of ancient fresco. The modified amplified function and the rubber band conversion algorithm are used as the jugement criterion, and the optimal paremeters are searched by the genetic algorithm (GA). Experimental results show that the quality of images is improved compared with the traditional.

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