Karakteristik Morfologi Tanaman Selada Menggunakan Pengolahan Citra Digital

Fadjeri, Akhmad; Saputra, Bayu Aji; Ariyanto, Dicki Kusuma Adri; Kurniatin, Lisna

doi:10.30646/sinus.v20i2.601

Cited by 8 publications

(10 citation statements)

References 11 publications

(13 reference statements)

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“…Digital or analog images are different from thermal-based images, digital or analog images are images that are representations/illustrations of real objects, while thermal-based images are images resulting from temperature detection emitted by objects captured by a thermal camera, so the resulting image is a processed beam object heat is captured and produces certain colors according to the heat emitted (Fadjeri et al 2022).…”

Section: Image Digitalmentioning

confidence: 99%

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Identification of Tempe Fermentation Maturity Using Principal Component Analysis and K-Nearest Neighbor

Istiadi

Rahman

Wisnu

2023

SinkrOn

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Tempe is one of the traditional foods in Indonesia which has nutritional content and benefits that are very much favored by all Indonesian people. To determine the maturity of tempe, it is generally done by fermenting it into tempeh using a certain temperature and usually tempe entrepreneurs are done traditionally. But in this way, tempe producers do not know what temperature and humidity are right for tempeh maturity. In this study, researchers used the MATLAB R2018a application with a total data set of 137 raw data, 137 ripe data and 136 rotten data, totaling 410 tempe image data. The purpose of this research is to produce a system that can detect the ripeness of tempe using the KNN (K-Nearest Neighbor) method which is equipped with GLCM texture feature extraction, with extraction of 8 color features, using the PCA (Principal Component Analysis) selection feature. And compare the results with the same method, namely KNN (K-Nearest Neighbor) without using the PCA (Principal Component Analysis) selection feature with the required running time between the two. KNN with PCA selection feature gets an average accuracy value of 80.63% and takes 1.06 seconds. Compared with the same method, namely KNN without using the selection feature, it gets an average accuracy value of 81.67% with a time of 1.18 seconds.

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Section: Image Digitalmentioning

confidence: 99%

“…Each pixel requires 8bits of memory. Figure 2 shows a close-up grayscale image with several pixel intensity values taken (Fadjeri et al 2022).…”

Section: Grayscale Imagementioning

confidence: 99%

Identification of Tempe Fermentation Maturity Using Principal Component Analysis and K-Nearest Neighbor

Istiadi

Rahman

Wisnu

2023

SinkrOn

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“…Akuisisi citra merupakan proses dalam mendapatkan representasi digital dari sebuah keadaan, representasi ini disebut dengan citra, dan elemen-elemen pembentuknya disebut dengan pixel [14]. Akuisisi data citra dilakukan pada tanaman caisim yang sudah dilakukan pengelompokkan secara manual.…”

Section: Metode Pengambilan Dan Akuisisi Gambarunclassified

Penerapan Metode Digital Untuk Mengukur Indeks Luas Daun Tanaman Sawi Caisim (Brassica Juncae L.)

Nurholis

Umam

Syafii

et al. 2023

JPP

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Tujuan dari penelitian ini adalah untuk mengetahui perbandingan luas daun sawi caisim dari beberapa dosis nutrisi menggunakan software ImageJ. Dalam perhitungan luas daun menggunakan objek referensi sebagai perbandingan dalam input nilai luas daun dalam bentuk pixel yang kemudian diubah dalam bentuk centimeter. Setelah itu, citra gambar diubah ke dalam bentuk citra biner (0 dan 1). Kemudian tahap akhirnya dengan memilih gambar daun untuk mengetahui luasannya. Pada penelitian ini proses pengambilan gambar dilakukan dua kali untuk melihat tingat error pada pengambilan pertama dan kedua. Metode pengukuran luas area daun yang diusulkan mendapatkan tingkat akurasi dan presisi yang tinggi sehingga dapat menghemat waktu, tenaga, serta biaya. Pengujian menggunakan ANOVA mendapatkan hasil tidak berbeda nyata sehingga pengambilan gambar sebanyak apapun tidak akan berpengaruh pada perhitungan luas area daun.

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“…Pengolahan citra terbagi 2 tahap yaitu citra digital dan citra analog. Citra digital diolah didalam komputer, sedangkan citra analog digunakan untuk pengopian dalam bentuk print out [5].…”

Section: Pengolahan Citraunclassified

Analisis Teks Bahasa Indonesia Dan Inggris Dari Sebuah Citra Menggunakan Pengolahan Citra Digital

2022

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The purpose of this research is to analyze Indonesian text and English text from images using digital image processing. The method used in this research is an experimental method such as: literature review , identification problems, hypotheses, analyzing materials, designing programs, conducting tests and drawing conclusions. The result of this research is to detect the text contained in the image. This research result a digital image processing program in text that is containing from an image that has text in it, using input or input in form of a character image. The image that will be recognized into text with Tesseract OCR and output the text detected by the program. This program can show the level of accuracy to recognize characters and words. It conducted kind of testing on 8 images use several of text characters on it. In this case, the system can recognize characters on the text form, with an average accuracy 96% of the test data taken on images sourced from the internet. According to the testing results, this level of accuracy indicates that this application is quite accurate and efficient in identification the image of certain word or text character.

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Karakteristik Morfologi Tanaman Selada Menggunakan Pengolahan Citra Digital

Cited by 8 publications

References 11 publications

Identification of Tempe Fermentation Maturity Using Principal Component Analysis and K-Nearest Neighbor

Identification of Tempe Fermentation Maturity Using Principal Component Analysis and K-Nearest Neighbor

Penerapan Metode Digital Untuk Mengukur Indeks Luas Daun Tanaman Sawi Caisim (Brassica Juncae L.)

Analisis Teks Bahasa Indonesia Dan Inggris Dari Sebuah Citra Menggunakan Pengolahan Citra Digital

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