Segmentasi Citra Sapi Berbasis Deteksi Tepi  Menggunakan Algoritma Canny Edge Detection

Mustafid, Ahmad; Uyun, Shofwatul

doi:10.24002/jbi.v8i1.1074

Cited by 4 publications

(8 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Furthermore, to determine the ideal body weight, use the calculation of Body Mass Index (BMI) according to formula (9). The results of the calculation of ideal body weight algorithm are compared with the table of standard BMI for women and men in Table 4, while the calculation of the actual ideal body weight uses the conventional formula in formula (1). Sample data from the calculation of Algorithm A are shown in Table 5.…”

Section: Resultsmentioning

confidence: 99%

“…There are several methods that are usually used to do edge detection, such as Robert, Sobel, Prewitt, and Canny, But, Canny is the best edge detection method for edge detection. In Research [1] Digital image processing is used to determine the weight of cow from image acquisition. This study is focused on segmentation and image processing of cows to get the best edge detection from several preprocessing scenarios.…”

Section: Introductionmentioning

confidence: 99%

“…The results of the study conclude that scenario 3 (median blur and canny) has the best results with MSE values of 230,051 and PSNR of 24,524 db. Then research [2], which is the second stage of research [1] is focused on the selection of the formula for determining the weight of cow and proposed algorithms to determine the weight of cow from image acquisition. The results of image calculation are not different significantly with MAE (Mean Absolute Error) that is equal to 8.15% and 4.10% for body length and chest circumference, respectively.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Classification of Human Weight Based on Image

Uyun

Efendi

2019

Indonesian J. Comput. Cybern. Syst.

Self Cite

View full text Add to dashboard Cite

Classification of human weight can be determined by body mass index. The body mass index can be calculated by dividing the height by the square of the body weight. According to researchers, this is less practical, so it needs to make a tool that can be used to determine ideal body weight more practically. One way is to use an Android smartphone camera. The camera is used to capture the image of the human body. Then the image is processed by using digital image processing and by using certain algorithms, so it may conclude the person's ideal weight category. The data used in this study are human photos, body weight and height. There are four stages to determine the weight and height based on the image. First, performing an analysis of the calculation of the derived formulas. Second, analyzing the edge detection algorithm. Third, conducting unit convertion, and fourth, proposing several algorithms to calculate the height and weight used to determine the ideal body weight. The results of the evaluation show that Algorithm C (measuring the width of an object starting with the height of the image adjusting half of the height of the object in the image) is the best algorithm with deviation value of 1.85% of the height and 8.87% of the weight, while the system accuracy rate in determining the ideal body weight has reached 78.7%.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Classification of Human Weight Based on Image

Uyun

Efendi

2019

Indonesian J. Comput. Cybern. Syst.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Hasil penelitian ini berupa segmentasi citra sapi dengan menggunakan 5 skenario. Berdasarkan hasil evaluasi skenario yang telah dilakukan diperoleh skenario terbaik adalah skenario ke-3 (median blur dan canny) dengan MSE sebesar 230.051 dan PNSR sebesar 24.524 dB [3].…”

Section: Pendahuluanunclassified

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

Kusrini

Kurniawan

2019

JurTI

View full text Add to dashboard Cite

- 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

show abstract

“…Pada penelitian sebelumnya (Mustafid & 'Uyun, 2017), telah dibahas tentang segmentasi berbasis deteksi tepi terhadap citra sapi dengan menggunakan kombinasi antara algoritma Canny dengan operator Median Blur serta Sharp, kemudian dilakukan ujicoba beberapa langkah dan cara untuk menghasilkan citra dengan deteksi tepi terbaik dengan cara menghitung MSE (Mean Square Error) dan PSNR (Peak Signal to Noise Ratio) pada masingmasing percobaan deteksi tepi. Hasil deteksi tepi terbaik dari penelitian yang pertama telah menghasilkan citra yang merupakan tahap preprocessing.…”

Section: Pendahuluanunclassified

Sistem Pengolahan Citra Digital untuk Menentukan Bobot Sapi Menggunakan Metode Titik Berat

Mustafid¹,

Uyun

2018

JTIIK

Self Cite

View full text Add to dashboard Cite

AbstrakPenentuan harga sapi umumnya disepakati melalui tawar menawar dan interaksi antara permintaan dan penawaran untuk menentukan harga bukan didasarkan pada bobot sapi yang dijual. Kebanyakan menggunakan perhitungan secara kasar maupun secara kira-kira. Terdapat rumus untuk menghitung bobot sapi, rumus yang ada memerlukan informasi terkait lingkar dada dan panjang badan. Untuk mendapatkan nilai lingkar dada dan panjang badan perlu dilakukan pengukuran secara manual, namun di lapangan hal tersebut tidak mudah dilakukan karena sapi sulit dikondisikan. Oleh karena itu diperlukan alat yang dapat mengukur secara mudah. Tulisan ini merupakan tahap kedua dari penelitian untuk menentukan bobot sapi dari hasil akuisisi citra sapi. Oleh sebab itu pada tahap kedua ini difokuskan pada pemilihan rumus penentuan bobot sapi dan usulan algoritma untuk menentukan bobot dari gambar hasil akuisisi citra. Hasil analisis penentuan bobot sapi menggunakan rumus Schoorl dan rumus Modifikasi memiliki nilai penyimpangan bobot badan sebesar 16,87% untuk rumus Schoorl dan nilai penyimpangan bobot badan sebesar 10,58 % untuk rumus Modifikasi. Hasil perhitungan citra tidak berbeda secara signifikan yaitu dengan faktor ketelitian secara statistis dengan nilai MAE (Mean Absolute Error) sebesar 8,15% untuk panjang badan dan sebesar 4,10% untuk lingkar dada. Aplikasi pengolahan citra digital yang dibagun dapat mengetahui berat badan/bobot sapi dengan nilai MAE (Mean Absolute Error) sebesar 8,97% terhadap rumus Modifikasi. Abstract The price determination of cows is generally agreed through bargaining and interacting with demand and supply to establish the general level of the price but it is not based on the weight of the cow itself. The tool that the most commonly used is by rough calculation or approximation. There were formulas to measure the weight, but it required chest circumference and the length of the body information. The values ware obtained manually using the measuring tool, but the reality is inconvenient to do, because of the difficulty conditioning the cows. Therefore, it required a tool that can calculate easily. This article represented the second stages of the research to determine the weight of cows from the image acquisition. Consequently, at this second stage has been focused on the selection of the cow weighting formula and the proposed algorithm to determine the weight from the result of images that had been processed in the early stages. The result of cow weighting analysis using Schoorl formula and Modification/Lambourne formula had the value of body weight deviation of 16.87% and 10.58. The results of image calculation did not differ significantly with MAE (Mean Absolute Error) equal to 8,15% and 4,10% for body length and chest circumference, respectively. Digital image processing application that has been built was able to know the weight of cow with MAE (Mean Absolute Error) equal to 8,97% towards Modification/Lambourne formula.

show abstract

Segmentasi Citra Sapi Berbasis Deteksi Tepi Menggunakan Algoritma Canny Edge Detection

Cited by 4 publications

References 0 publications

Classification of Human Weight Based on Image

Classification of Human Weight Based on Image

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

Sistem Pengolahan Citra Digital untuk Menentukan Bobot Sapi Menggunakan Metode Titik Berat

Contact Info

Product

Resources

About