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All Journal Jurnal Teknologi dan Manajemen Informatika BAREKENG: Jurnal Ilmu Matematika dan Terapan ILKOM Jurnal Ilmiah Jurnal Literasiologi Jurnal Mantik Journal Dynamic SainT Teknika Journal of System and Computer Engineering INFINITY : UKI TORAJA JOURNAL OF INFORMATION TECHNOLOGY J-Icon : Jurnal Komputer dan Informatika TONGKONAN: Jurnal Pengabdian Masyarakat
Michael, Aryo
Universitas Kristen Indonesia Toraja
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation Other

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DETERMINATION OF COFFEE FRUIT MATURITY LEVEL USING IMAGE HISTOGRAM AND K-NEAREST NEIGHBOR Damayanti, Irene Devi; Michael, Aryo
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 2 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss2pp0785-0796

Abstract

Coffee has a very important role in the Indonesian economy, as one of the country's foreign exchange contributors in the plantation sector. Therefore, coffee processing is very important in determining the quality of coffee. The procedure for choosing and evaluating the coffee fruit's physical quality is one of the most crucial steps. The step of determining the maturity level of coffee fruit is carried out using the image histogram and K-Nearest Neighbor (KNN) method. This research uses the KNN algorithm with classification stages that will show the level of accuracy value according to the value of k = 5 used when processing the classification of coffee fruit image data. In order to complete this step, the features of the coffee fruit are identified using its color. The qualities of quality coffee fruit, which is flawlessly red in color. Twenty images total—ten of which are of ripe coffee fruit and ten of which are of raw coffee fruit—were used in this study. The test results were carried out using rapidminer tools using 40% training data and 60% testing data from the total data set. Based on the test results, it gives an accuracy value of 100%, meaning that the data set can be used in the next stage as valid data to be used.
Klasifikasi Citra Daging Babi dan Daging Kerbau Menggunakan Histogram Citra dan GLCM Damayanti, Irene Devi; Michael, Aryo; Fridolin, Fridolin; Piopadang, Helce K. Y.; P., Setriyanti
Journal of System and Computer Engineering Vol 4 No 2 (2023): JSCE: Juli 2023
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v4i2.878

Abstract

Due to high consumer demand, some traders use the high price of meat to make a profit by mixing pork and buffalo meat. Some consumers are not aware of this, because in plain view buffalo meat with pork meat is difficult to distinguish, especially for some ordinary people. This action is very detrimental and disturbing the local community, especially Muslims. At present, technological advances in the field of digital image processing are increasing rapidly, especially in food products. In general, this research was conducted in 2 (three) stages. The first stage, namely the stage of image data collection of pork and buffalo meat. The second stage, namely the classification of pork and buffalo meat images using image histogram analysis and the Gray Level Co-occurrence Matrix (GLCM) method based on the color and texture of the meat. In this study using the Red Green Blue (RGB) color image method and GLCM texture extraction, namely contrast, homogeneity, energy, and correlation. The study was conducted using 20 samples of meat images (10 images of pork and 10 images of buffalo meat, respectively). Based on the results of the research that has been done, it was found that the image of buffalo meat has a higher percentage value of the Red (R) color component when compared to the pork image, whereas the percentage value of the Green (G) and Blue (B) color components is lower when compared to the image pork. Then, if the value between pixels is not homogeneous (small homogeneity value), then the contrast value is large, and vice versa if the value between pixels is homogeneous (large homogeneity value) then the contrast value is small. The image of buffalo meat has a small homogeneity value compared to the image of pork, so the variation in intensity (contrast) in the image of buffalo meat is high.
Co-Authors Adda, Muhammad Sofwan Amarya Sula Damayanti, Irene Devi Dudung, Marni Fridolin, Fridolin Garoga, Melki Garonga, Melki Gidion Aryo Nugraha Pongdatu Haryati, Berlian Zetikarya Irene Devi Damayanti Juprianus Rusman K, Gabriel Maharani, Fanny Marchelin Nofrianto Pasae P, William P., Setriyanti Padang, Samuel Yakobus Padang, Semuel Yakobus Patalo, Enohk Eightry Piopadang, Helce K. Y. Saladan, Fidryani Salu, Nerwan Srivan Palelleng Tumanan, Reynaldi Ary