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All Journal Lontar Komputer: Jurnal Ilmiah Teknologi Informasi Applied Technology and Computing Science Journal JISA (Jurnal Informatika dan Sains)
Lalitya Nindita Sahenda
Politeknik Negeri Jember
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Social Sciences

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The The Classification of Acute Respiratory Infection (ARI) Bacteria Based on K-Nearest Neighbor Zilvanhisna Emka Fitri; Lalitya Nindita Sahenda; Pramuditha Shinta Dewi Puspitasari; Prawidya Destarianto; Dyah Laksito Rukmi; Arizal Mujibtamala Nanda Imron
Lontar Komputer : Jurnal Ilmiah Teknologi Informasi Vol 12 No 2 (2021): Vol. 12, No. 02 August 2021
Publisher : Institute for Research and Community Services, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/LKJITI.2021.v12.i02.p03

Abstract

Acute Respiratory Infection (ARI) is an infectious disease. One of the performance indicators of infectious disease control and handling programs is disease discovery. However, the problem that often occurs is the limited number of medical analysts, the number of patients, and the experience of medical analysts in identifying bacterial processes so that the examination is relatively longer. Based on these problems, an automatic and accurate classification system of bacteria that causes Acute Respiratory Infection (ARI) was created. The research process is preprocessing images (color conversion and contrast stretching), segmentation, feature extraction, and KNN classification. The parameters used are bacterial count, area, perimeter, and shape factor. The best training data and test data comparison is 90%: 10% of 480 data. The KNN classification method is very good for classifying bacteria. The highest level of accuracy is 91.67%, precision is 92.4%, and recall is 91.7% with three variations of K values, namely K = 3, K = 5, and K = 7.
Application of Feature Selection for Identification of Cucumber Leaf Diseases (Cucumis sativa L.) Lalitya Nindita Sahenda; Ahmad Aris Ubaidillah; Zilvanhisna Emka Fitri; Abdul Madjid; Arizal Mujibtamala Nanda Imron
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. 
Red Dragon Fruit (Hylocereus costaricensis) Ripeness Color Classification by Naïve Bayes Algorithm Zilvanhisna Emka Fitri; Mega Silvia; Abdul Madjid; Arizal Mujibtamala Nanda Imron; Lalitya Nindita Sahenda
TEKNOLOGI DITERAPKAN DAN JURNAL SAINS KOMPUTER Vol 5 No 1 (2022): June
Publisher : Unusa Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33086/atcsj.v5i1.3690

Abstract

Dragon fruit is a unique fruit that is popular in Indonesia. besides having a sweet taste, this fruit also contains fiber, vitamins and minerals that are good for health. Dinas Pertanian Kabupaten Banyuwangi noted that the total dragon fruit production was 906,511.61 tons and the total productivity was 261.14 Kw/Ha in 2018. This shows that Kabupaten Banyuwangi is one of the largest producers of red dragon fruit in East Java Province. One of the problems in determining the quality of dragon fruit is choosing the harvest time, considering that dragon fruit is a non-climatic fruit. Non-climateric fruit is when we harvest fruit in its raw state, the fruit will never become ripe, so determining the harvest time for dragon fruit is very important. The determination made by paying discoloration and sizes of dragon fruit that is considered less effective. To overcome this, a system was created that was able to determine the level of dragon fruit maturity automatically by utilizing digital image processing techniques and intelligent systems. The parameters used are color features and GLCM texture features using angles 0°, 45°, 90° and 135° These features are parameters in the classification process using the Naïve Bayes method. Naïve bayes is able to classify the level of maturity of red dragon fruit (Hylocereus costaricensis) with an accuracy rate of 87.37%.
Co-Authors Abdul Madjid Ahmad Aris Ubaidillah Dyah Laksito Rukmi Imron, Arizal Mujibtamala Nanda Mega Silvia Pramuditha Shinta Dewi Puspitasari Prawidya Destarianto Zilvanhisna Emka Fitri