Implementasi Penggabungan Prewitt dan Canny Edge Detection untuk Identifikasi Ikan Air Tawar

Laxmi, Gibtha Fitri; Eosina, Puspa; Fatimah, Fety

doi:10.32832/kreatif.v6i2.2185

Cited by 6 publications

(7 citation statements)

References 1 publication

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“…The Canny approach is the result of separating light and dark shadows, which gives the method the advantage of being able to lower the noise before performing edge detection calculations. This method can reveal the contents of objects in each image [38]. Food images, on the other hand, have less relevance in the research of object separation because the objects in them are so diverse and complex.…”

Section: Results and Discussion Resultsmentioning

confidence: 99%

Selection of Food Identification System Features Using Convolutional Neural Network (CNN) Method

Arnita

Marpaung

Koemadji

et al. 2023

SJI

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Purpose: The identification and selection of food to be consumed are critical in determining the health quality of human life. Our diet and the illnesses we develop are closely linked. Public awareness of the significance of food quality has increased due to the rising prevalence of degenerative diseases such as obesity, heart disease, type 2 diabetes, hypertension, and cancer. This study aims to develop a model for food identification and identify aspects that can aid in food identification.Methods : This study employs the convolutional neural network (CNN) approach, which is used to identify food objects or images based on the detected features. The images of thirty-five different types of traditional, processed, and western foods were gathered as the study’s input data. The image data for each type of food was repeated 100 times to produce a total of 3500 images.. Using the color, shape, and texture information, the food image is retrieved. The hue, saturation, and value (HSV) extraction method for color features, the Canny extraction method for shape features, and the gray level co-occurrence matrix (GLCM) method for texture features, in that sequence, were used to evaluate the data in addition to the CNN classification method.Result:The simulation results show that the classification model’s accuracy and precision are 76% and 78%, respectively, when the CNN approach is used alone without the extraction method. The CNN classification model and HSV color extraction yielded an accuracy and precision of 51% and 55%, respectively. The CNN classification model with the Canny texture extraction method has an accuracy and precision of 20% and 20%, respectively, while the combined CNN and GLCM extraction methods have 67% and 69% success rates, respectively. According to the simulation results, the food classification and identification model that uses the CNN approach without the HSV, Canny, and GLCM feature extraction methods produces better results in terms of accuracy and precision model.Novelty: This research has the potential to be used in a variety of food identification applications, such as food and nutrition service systems, as well as to improve product quality in the food and beverage industry.

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Section: Results and Discussion Resultsmentioning

confidence: 99%

Selection of Food Identification System Features Using Convolutional Neural Network (CNN) Method

Arnita

Marpaung

Koemadji

et al. 2023

SJI

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“…Metode prewitt ini menekankan pembobotan pada piksel-piksel yang lebih dekat. Metode prewitt hampir sama dengan metode sobel dimana metode sobel ini menggunakan HPF yang nilai konstantanya adalah Nol [3]. Dalam penelitian ini ada beberapa objek yang akan di uji dengan metode prewitt yang kemudian hasilnya dibandingkan dengan metode cany, dimana hasil dari kedua metode tersebut nantinya akan dibandingkan untuk mencari nilai terbaik.…”

Section: Pendahuluanunclassified

“…Cara kerja dari Algoritma Prewitt dengan cara menghitung hasil maksimum dari suatu kernel konvolusi untuk menemukan orientasi deteksi sisi disekitarnya pada tiap pixel. Algoritma ini juga disebut sebagai edge template matching, karena gambar mengalami proses matching dengan sebuah template yang berupa sisi [6].…”

Section: Pendahuluanunclassified

Rancang Bangun Aplikasi Deteksi Tepi Citra Digital Menggunakan Algoritma Prewitt

2021

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Edge detection is one of the algorithms used in Digital Image Processing. This algorithm serves to identify the line/edge of the image object to highlight the boundary lines of the image information. Edge is a set of connected pixels (connected pixels) that restricts the objects contained in the image. Senses the eye is one that is used by humans to see.However, the human eye has its limitations in capturing the electromagnetic signals.Therefore, created a computer or imaging machine that can capture almost the entire signal elektromagnetic. Imaging machines can work with imagery from sources that do not fit, do not fit or can not be captured by human vision. This is why digital image processing have very wide usefulness. Image processing technology can fit into a variety of fields such as medicine, geology, marine , industrial and others.Keywords:Image,Prewitt,Edge,Java,Netbeans

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“…Penggabungan kedua metode tersebut dapat digunakan juga dari prewitt ke canny. Deteksi tepi menggunakan canny dapat menyempurnakan deteksi tepi menggunakan prewitt [2].…”

Section: Pendahuluanunclassified

“…besaran magnitude (kekuatan garis tepi) dan orientation (arah garis tepi) dengan menggunakan operator sobel dengan persamaan 3 dan 4 berikut, dengan [ , ] adalah magnitude dan[ , ] adalah orientation, rumus magnitude dan orientation dapat dilihat pada persamaan 1 dan 2. [ , ] = √ [ , ] 2 + [ , ]2 …”

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Segmentasi Citra Ikan Arwana Super Red Berdasarkan Deteksi Tepi Menggunakan Algoritma Canny

Kusrini

Kurniawan

2019

JurTI

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- Ornamental fish is a fish that lived in the aquarium and for lovers of fish have ornamental fish is the most favored. One of the ornamental fish is pretty much in demand and has a fairly high selling price is super red Arowana fish. Super red Arowana fish also called dragon fish because it has scales resembling a dragon. In general, the selling price of super red Arowana fish is influenced by the size of the fish, such as the length and width of the fish. The high prices caused the farmers are very careful in treating the fish, so it is necessary there is a system that if it can measure the length and width of super red Arowana fish. The research groove generally consists of two phases. The initial phase of that region contributes to the best image segmentation and the second stage is to produce a long and wide super red Arowana fish. The current study focuses on the early stages, the best image segmentation. At this early stage, the detection process is carried out using the algorithm Canny edge of the fish so as to produce the best image segmentation. In this study, using three scenarios, the first scenario uses the Canny course, the second scenario using the canny and the median blur, the third scenario using the median blur and canny. Based on all these three scenarios obtained image segmentation fish with the best scenario, the scenario-2 using the canny and the median blur with MSE value of 26494.56, PNSR value of 41.89 dB, and MAPE value of 99.28%.Keywords - Canny Edge Detection, Super Red Arowana Fish, Image Segmentation, Ornamental fish Abstrak - Ikan hias merupakan ikan yang dihidupi didalam aquarium dan bagi para pecinta ikan memiliki ikan hias adalah hal yang paling disenangi. Salah satu ikan hias yang cukup banyak diminati dan memiliki harga jual yang cukup tinggi adalah ikan arwana super red. Ikan arwana jenis super red biasa juga disebut dengan ikan naga karena memiliki sisik yang menyerupai naga. Pada umumnya harga jual ikan arwana super red ini dipengaruhi oleh ukuran ikan, seperti panjang dan lebar ikan. Harga jual yang tinggi tersebut menyebabkan para pembudidaya sangat berhati-hati dalam memperlakukan ikan tersebut, sehingga dirasa perlu terdapat sebuah sistem yang sekiranya dapat mengukur panjang dan lebar ikan arwana super red. Alur penelitian secara umum terdiri dari dua tahap. Tahap awal yaitu meghasilkan segmentasi citra terbaik dan tahap kedua adalah menghasilkan panjang dan lebar ikan arwana super red. Penelitian yang dilakukan saat ini berfokus pada tahap awal, yaitu segmentasi citra terbaik. Pada tahap awal ini dilakukan proses deteksi tepi ikan menggunakan algoritma canny sehingga menghasilkan segmentasi citra terbaik. Pada penelitian ini menggunakan 3 skenario, skenario pertama menggunakan canny saja, skenario kedua menggunakan canny dan median blur, skenario ketiga menggunakan median blur dan canny. Berdasarkan ke-3 skenario tersebut diperoleh segmentasi citra ikan dengan skenario terbaik, yaitu skenario ke-2 menggunakan canny dan median blur dengan nilai MSE sebesar 26494.56, nilai PNSR sebesar 41.89 dB, dan nilai MAPE sebesar 99.28%. Kata kunci - Deteksi Tepi Algoritma Canny, Ikan Arwana Super Red, Segmentasi Citra, Ikan Hias

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Implementasi Penggabungan Prewitt dan Canny Edge Detection untuk Identifikasi Ikan Air Tawar

Cited by 6 publications

References 1 publication

Selection of Food Identification System Features Using Convolutional Neural Network (CNN) Method

Selection of Food Identification System Features Using Convolutional Neural Network (CNN) Method

Rancang Bangun Aplikasi Deteksi Tepi Citra Digital Menggunakan Algoritma Prewitt

Segmentasi Citra Ikan Arwana Super Red Berdasarkan Deteksi Tepi Menggunakan Algoritma Canny

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