Deteksi Kelayuan Pada Bunga Mawar dengan Metode Transformasi Ruang Warna HSI Dan HSV

Wandi, Dede; Fauziah, Fauziah; Hayati, Nur

doi:10.30998/string.v5i3.8464

Cited by 3 publications

(4 citation statements)

References 5 publications

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“…Seperti penelitian tentang deteksi kelayuan bunga mawar dengan metode transformasi ruang warna Hue Saturation Intensity (HSI) dan Hue Saturation Value (HSV) [9], [10]. Penelitian ini menghasilkan akurasi dari 2 perbandingan antara HSI dan HSV yakni masing masing bernilai 92,2% dan 93,3% [11]. Penelitian lain juga memanfaatkan computer vision untuk mengklasifikasi bunga dengan ekstraksi fitur warna RGB dengan total data 360 yang terdiri dari 4 kelas yaitu bunga coltsfoot, bunga daisy, bunga delion dan bunga matahari.…”

Section: Pendahuluanunclassified

Pemodelan Warna Pada Dataset Baru Citra Bunga Lantana Camara Menggunakan Algoritma Yolov5

Putu Sugiati Keraf,

Aristo Jansen Sinlae,

Batarius

2024

Mnemonic

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Lantana camara merupakan salah satu tumbuhan tropis invasif yang berpotensi sebagai tanaman hias yang memiliki keunikan pada kelopak bunga berwarna-warni dan dapat diklasifikasikan menjadi beberapa jenis warna. Meskipun demikian tumbuhan ini dapat ditemukan di pekarangan rumah dan pengetahuan masyarakat tentang tumbuhan ini terkait manfaat khususnya masih terbatas. Selain itu, proses identifikasi warna pada bunga lantana camara dalam produksi massal masih sulit dan memakan waktu serta pemilihan warna secara manual dapat menyebabkan kelelahan pada mata manusia. Oleh karena itu, diperlukan solusi yang memanfaatkan computer vision, seperti penerapan algoritma YOLOv5 untuk pemodelan warna. Metode yang digunakan melibatkan pengumpulan data berupa citra gambar bunga lantana sebanyak 1266 citra yang terbagi dalam 6 jenis warna (merah, putih, kuning, jingga, ungu, merah muda), penyimpanan data berupa citra gambar lantana dilakukan anotasi dan penyesuaian ukuran piksel, pelatihan data dilakukan pada citra gambar yang sudah dianotasi dan ukurannya sama untuk mendapatkan dataset menggunakan roboflow dan google collab, dan evaluasi model untuk mendapatkan hasil prediksi berdasarkan putaran pelatihan sebanyak 50 epoch dan membutuhkan waktu selama 0.483 jam. Hasil yang diperoleh berupa nilai persentase precision sebesar 95%, recall sebesar 99%, dan nilai MAP sebesar 99%, sehingga nilai rata-rata akurasinya di atas 90%. Berdasarkan hasil tersebut penelitian ini berhasil menghasilkan model pada dataset citra bunga lantana camara dengan rata-rata parameter teruji mencapai 90%.

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Section: Pendahuluanunclassified

Pemodelan Warna Pada Dataset Baru Citra Bunga Lantana Camara Menggunakan Algoritma Yolov5

Putu Sugiati Keraf,

Aristo Jansen Sinlae,

Batarius

2024

Mnemonic

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“…Thus. it is necessary to normalize the RGB first based on the equations ( 7), ( 8), (9). out, then the RGB to HSV conversion process is carried out using the equation ( 13), ( 14), ( 15), (16).…”

Section: Preprocessingmentioning

confidence: 99%

“…Color and texture qualities are used to do research on beef and pork in the field of digital imaging. According to a study [9] that examined Hue saturation intensity (HSI) and hue saturation value (HSV) color features in detecting rose withering, HSV color features performed better. The textural features GLCM, LBP, LBGLCM, GLRLM, and SFTA were compared in a study conducted by [10].…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Learning Vector Quantization for Classification of Mixed Meat Image Based on Character of Color and Texture

Ningsih

Buono

Mushthofa

et al. 2022

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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Beef consumption is quite high and expensive in the world. In Indonesia, beef prices are relatively expensive because the meat supply chain from farmers to the market is quite long. The high demand for beef and the difficulty of obtaining meat are factors in the high price of meat. This makes some meat traders cheat by mixing beef and pork (oplosan). Mixing beef and pork is detrimental to beef consumers, especially those who are Muslim. In this paper, we proposed a new strategy for identifying beef, pig, and mixed meat utilizing Fuzzy learning vector quantization (FLVQ) Based on the color and texture aspects of the meat. The HSV (Hue saturation value) approach is used for color features, whereas the GLCM (Gray level co-occurrence matrix) method is used for texture features. This study makes use of primary data collected from the Pasar Bawah Tourism and Cipuan Market in Pekanbaru, Riau Province. The data set consists of 600 photos, 200 each of beef, pork, and mixed. Based on the test scenario, the coefficient of fuzzyness and learning rate affect the accuracy of meat image identification. The proposed strategy has succeeded in classifying pork, beef and mixed meat with the best percentage of accuracy results in theclasses of beef and pork, beef and mixed, pork and mixed meat, respectively, at 100%, 97.5%, and 95%. This demonstrates that the proposed strategy has succeeded in classifying the image of pork, beef, and mixed.

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“…Research [7] Comparing the Hue Saturation Intensity (HSI) and Hue Saturation Value (HSV) (HSV) color features in detecting rose flowers. HSV color features get better accuracy compared to HSI.…”

Section: Tomatoesmentioning

confidence: 99%

Classification Of Tomato Maturity Levels Based on RGB And HSV Colors Using KNN Algorithm

Ningsih

Cholidhazia²

2022

RIGGS

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Tomatoes (Lycopersiconeculentum Mill) are vegetables that are widely produced in tropical and subtropic areas. Accordingto (Harllee) tomatoes are grouped into 6 levels of maturity, namely green, breakers, turning, pink, light red, and red. One waythat can be used to classify the level of maturity of tomatoes in the field of informatics is to utilize digital image processingtechniques. This study classifies the maturity of tomatoes using K-Nearest Neighbor (KNN) based on the Red Green Blue andHue Saturation Value color features. The KNN algorithm was chosen as a classification algorithm because KNN is quite simplewith good accuracy based on the minimum distance using Euclidean Distance. The research conducted received the highestaccuracy result of 91.25% at the value of K = 7 with the test data 80. This shows that the KNN algorithm successfully classifiedthe maturity of tomatoes by utilizing the color image of RGB and HSV.

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Deteksi Kelayuan Pada Bunga Mawar dengan Metode Transformasi Ruang Warna HSI Dan HSV

Cited by 3 publications

References 5 publications

Pemodelan Warna Pada Dataset Baru Citra Bunga Lantana Camara Menggunakan Algoritma Yolov5

Pemodelan Warna Pada Dataset Baru Citra Bunga Lantana Camara Menggunakan Algoritma Yolov5

Fuzzy Learning Vector Quantization for Classification of Mixed Meat Image Based on Character of Color and Texture

Classification Of Tomato Maturity Levels Based on RGB And HSV Colors Using KNN Algorithm

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