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All Journal JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Informasi dan Teknologi
Dwiza Riana
Universitas Nusa Mandiri, Jakarta
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Klasifikasi Varietas Buah Kiwi dengan Metode Convolutional Neural Networks Menggunakan Keras Aldi Jakaria; Sofiyatul Mu’minah; Dwiza Riana; Sri Hadianti
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 5, No 4 (2021): Oktober 2021
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v5i4.3166

Abstract

Kiwi fruit is known as a fruit rich in benefits because it contains many nutrients, sources as well as high antioxidants. In Indonesia, there are two varieties of kiwi fruit sold in the market, namely green kiwi and golden kiwi and there is one more variety, namely red kiwi. The content of the three varieties is different and the price is also different. Gold kiwi has the highest nutritional content so that the price is above other kiwi varieties, but from the outside the appearance of this kiwi fruit at a glance is the same and many people do not recognize the kiwi variety they will buy even though these three kiwi varieties have different tastes and nutritional content. For this reason, the researcher proposes a classification system for kiwi fruit varieties using the hard CNN method. The CNN method is one of the deep learning methods that can be used to recognize and classify an object in a digital image. Then the preprocessing process is carried out using labeling on the data. Then the CNN architecture is designed with Input containing 320x258x3 neurons. The data was then trained using 25 epochs with an accuracy rate of 0.98. Then the test data using test data get an average accuracy value of 0.987, while for precision and recall it is also the same at 0.987
Pemisahan Objek Sel Tumpang Tindih pada Citra Pap Smear dengan Metode Deep Learning dan Watershed Muh. Jamil; Dwiza Riana
Jurnal Informasi dan Teknologi 2022, Vol. 4, No. 4
Publisher : SEULANGA SYSTEM PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/jidt.v4i4.243

Abstract

The object observed in the Pap Smear image is Cervical Cancer which forms overlapping cells. This cancer must be observed early because it is a disease that causes the death of thousands of women worldwide every year. The death rate from this disease is the fourth highest among women. One way to be aware of this disease is to do an early check on the Pap Smear test image. This cell separation process uses the image segmentation method. This method is one of the important steps to be able to identify existing cell objects. This study proposes a segmentation method to separate 2 overlapping cells in the RepomedUNM dataset. The dataset is engineered in the manufacture of synthetic Pap Smear images. The segmentation method proposed is a Deep Learning-based method so that it can identify 2 overlapping cells in one area. The level of accuracy of the test with an average score of Intersection over Union (IoU) is 0.9003. And the results of segmentation with Deep Learning can be divided into all areas using the Watershed segmentation method. So that this research becomes a reference in the early identification of Cervical Cancer.
Co-Authors Aldi Jakaria Muh. Jamil Sofiyatul Mu’minah Sri Hadianti