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All Journal JISA (Jurnal Informatika dan Sains) NERO (Networking Engineering Research Operation)
Sahenda, Lalitya Nindita
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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PERHITUNGAN KOLONI BAKTERI SUSU SEGAR PADA RUANG WARNA YCBCR Fitri, Zilvanhisna Emka; Sahenda, Lalitya Nindita; Holili, Rexy Solehudin Abdi; Rukmi, Dyah Laksito
NERO (Networking Engineering Research Operation) Vol 8, No 2 (2023): Nero - 2023
Publisher : Jurusan Teknik Informatika Fakultas Teknik Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/nero.v8i2.19094

Abstract

The problem with fresh milk on the SPR farm is the manual milking process, which causes the milk to be less hygienic and becomes an ideal growing medium for microbes. Therefore, it is necessary to carry out a procedure for checking the microbiological status as an indicator of food safety. To test for microbial contamination in fresh milk, namely the Total Plate Count (TPC) test, but in this study the focus is on the calculation of bacterial colonies using digital image processing techniques. The stages of the research carried out are the preprocessing process (cropping and color conversion to YCbCr space), image enhancement (addition of brightness and inverse image), the segmentation combination process (gray degree and channel area thresholding) and colony calculation using labeling based on the proximity of 8 neighbors to the feature area. From the results of the study, it was found that bacterial colonies had a wide area range of 150 ≤ area ≤ 8000. A comparison of manual TPC calculations with the system has been carried out on 5 test samples and obtained an average error difference of 0.176.Keywords : channel area thresholding, bacterial colonies, fresh milk, TPC, YCbCr
Application of Feature Selection for Identification of Cucumber Leaf Diseases (Cucumis sativa L.) Sahenda, Lalitya Nindita; Ubaidillah, Ahmad Aris; Fitri, Zilvanhisna Emka; Madjid, Abdul; Imron, Arizal Mujibtamala Nanda
JISA(Jurnal Informatika dan Sains) Vol 4, No 2 (2021): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v4i2.1046

Abstract

According to data from BPS Kabupaten Jember, the amount of cucumber production fluctuated from 2013 to 2017. Some literature also mentions that one of the causes of the amount of cucumber production is disease attacks on these plants. Most of the cucumber plant diseases found in the leaf area such as downy mildew and powdery mildew which are both caused by fungi (fungal diseases). So far, farmers check cucumber plant diseases manually, so there is a lack of accuracy in determining cucumber plant diseases. To help farmers, a computer vision system that is able to identify cucumber diseases automatically will have an impact on the speed and accuracy of handling cucumber plant diseases. This research used 90 training data consisting of 30 healthy leaf data, 30 powdery mildew leaf data and 30 downy mildew leaf data. while for the test data as many as 30 data consisting of 10 data in each class. To get suitable parameters, a feature selection process is carried out on color features and texture features so that suitable parameters are obtained, namely: red color features, texture features consisting of contrast, Inverse Different Moment (IDM) and correlation. The K-Nearest Neighbor classification method is able to classify diseases on cucumber leaves (Cucumis sativa L.) with a training accuracy of 90% and a test accuracy of 76.67% using a variation of the value of K = 7. 
Co-Authors Abdul Madjid Arizal Mujibtamala Nanda Imron Fitri, Zilvanhisna Emka Holili, Rexy Solehudin Abdi Rukmi, Dyah Laksito Ubaidillah, Ahmad Aris