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Otomatisasi klasifikasi kematangan buah mengkudu berdasarkan warna dan tekstur Kusuma, Selvia Ferdiana; Pawening, Ratri Enggar; Dijaya, Rohman
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol 3, No 1 (2017): Januari-Juni (3/7)
Publisher : Prodi Sistem Informasi - Universitas Pesantren Tinggi Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1141.772 KB) | DOI: 10.26594/register.v3i1.576

Abstract

 Buah Mengkudu merupakan komoditi ekspor yang sedang berkembang di Indonesia. Proses pengklasifikasian kematangan buah Mengkudu perlu dilakukan agar kualitas buah Mengkudu yang di ekspor dapat terjamin. Proses klasifikasi dengan jumlah yang banyak akan sulit apabila dilakukan secara manual. Oleh karena itu, penelitian ini diperlukan untuk menghasilkan proses otomatisasi klasifikasi kematangan buah Mengkudu. Metode yang diusulkan untuk melakukan otomatisasi klasifikasi adalah proses pengenalan karakteristik buah Mengkudu berdasarkan fitur tekstur dan warna. Fitur tektur dan fitur warna didapatkan melalui proses pengolahan citra digital buah Mengkudu. Penelitian ini membuktikan bahwa pengklasifikasian buah Mengkudu dengan algoritma Support Vector Machines (SVM) menghasilkan nilai persentase lebih tinggi dari pada menggunakan algoritma k-Nearest Neighbors (KNN). Hasil persentase tertinggi yang didapatkan yaitu sebesar 87.22%.   Noni fruit is an export commodities that were flourishing in Indonesia. Noni fruit maturity classification process should be done in order the quality of the noni fruit which is exported can be guaranteed. Classification process in large quantities will be difficult if it is done manually. Therefore this research is needed to produce an automation classification process of noni fruit ripeness. The proposed method is characteristic introduction of noni fruit based on texture and color features. Texture and color features are obtained from digital image processing of noni fruit. This research proves that the classification of noni fruit with SVM algorithm produces better accuracy than using KNN algorithm. The highest accuracy is equal to 87.22%.
KOMBINASI FITUR BENTUK, WARNA DAN TEKSTUR UNTUK IDENTIFIKASI KESUBURAN TELUR AYAM KAMPUNG SEBELUM INKUBASI Dijaya, Rohman; Suciati, Nanik; Herumurti, Darlis
Jurnal Buana Informatika Vol 7, No 3 (2016): Jurnal Buana Informatika Volume 7 Nomor 3 Juli 2016
Publisher : Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (428.984 KB) | DOI: 10.24002/jbi.v7i3.659

Abstract

Abstract. In the chicken nursery industry (doc) hatching efficiency is obtained by observing the eggs through candling before the incubation process. To sort out infertile eggs the use of fertility image identification thought egg candling is needed before incubation. The focus of this study is to combine the features of shape, texture and color to the area and egg yolk to determine the most dominant features in the image representing firtile egg candling. Features used in this study are the feature of forms: roundness, elongation, Index, Ellips Varriance and Circularity Ratio, moment invariant texture features of the area and the egg yolk, and features HSI color in egg yolks area. The test results show that the highest accuracy is on the features of the new forms of egg yolk with an accuracy of 76.67%. The second highest is shown by the combination of form features (Circularity Ratio, Ellips Varriance) and texture features in the area moment yolk color features HSI with 81.67% accuracy using SVM classification method.Keywords: Egg candling imagery, fertile, infertile, incubation Abstrak. Pada industri pembibitan ayam (doc) efisiensi penetasan telur ayam didapatkan dengan melakukan candling (peneropongan telur) sebelum proses inkubasi menggunakan mesin tetas. Untuk mengklasifikasikan telur fertile dan infertile dibutuhkan identifikasi kesuburan telur menggunakan citra candling sebelum inkubasi. Fokus dari penelitian ini adalah mengkombinasikan fitur bentuk, tekstur dan warna pada area kuning telur dan telur untuk mengetahui fitur yang paling dominan dalam merepresentasikan citra candling telur ayam kampung. Fitur yang digunakan dalam penelitian ini adalah fitur bentuk (Roundness, Elongation, Index, Ellips Varriance dan Circularity Ratio), fitur tektur moment invarian dari area telur dan kuning telur dan fitur warna HSI pada area kuning telur. Hasil pengujian menunjukkan akurasi tertinggi pada fitur bentuk kuning telur baru dengan akurasi 76,67% dan kombinasi fitur bentuk (Circularity Ratio, Ellips Varriance), fitur tekstur moment pada area kuning telur dengan fitur warna HSI dengan akurasi 81,67 % menggunakan metode klasifikasi SVM. Kata Kunci: Citra candling telur, fertile, infertile, inkubasi.
Quality of Genom in Type II Diabetes Mellitus Patients in Viewed of Temperature, Storage Duration, Number of Leukocyte Puspitasari, Puspitasari; Rinata, Evi; Dijaya, Rohman; Aprilia, Siska; Trikumalasari, Dina; Nur Azzah, Livia; Fanani, Qilmia; Mushlih, Miftahul; Aliviameita, Andika; Delta, Dian
Medical Laboratory Technology Journal Online First Article
Publisher : Poltekkes Kemenkes Banjarmasin Jurusan Analis Kesehatan

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (602.348 KB) | DOI: 10.31964/mltj.v0i0.229

Abstract

Type II Diabetes Mellitus is a disorder caused by genetic and environmental factors. Molecular analysis of T2DM abnormalities has been carried out. But the analysis in sample preparation especially the stages of DNA isolation has not been done much. The aims of this study are to investigate the effect of temperature, storage duration and level of white blood cell (WBC) with Genome Quality in T2DM. The treatment (n:10) which were divided into several tubes and then stored at 4 °?, 25 °? and 32 °? for 21 days. To determining storage duration effect, we use periodically isolation of DNA (3, 15 and 30 days) after sampling. The effect of WBC with DNA quality was carried out using 17 samples. DNA isolation was done by the DNA Isolation Kit manual without modification and then tested qualitatively and quantitatively. Based on this research, it can be concluded there is a correlation between the numbers of WBC with DNA quality. The higher the number of WBC, the higher DNA concentration (r: 0.818. p value: 0.000). The concentration of DNA at a temperature of 4°? (135.1 ± 165.2 ng / µl) was higher compared with the temperature treatment 25 ° ? (29.7 ± 36.5 ng / µl) and 32 ° ? (22.14 ± 7.13 ng / µl) (p