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All Journal JURIKOM (Jurnal Riset Komputer) KLIK: Kajian Ilmiah Informatika dan Komputer
Umi Khultsum
Universitas Bina Sarana Informatika, Pontianak
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Penerapan Metode Mobile-Net Untuk Klasifikasi Citra Penyakit Kanker Paru-Paru Umi Khultsum; Fajar Sarasati; Ghofar Taufik
JURIKOM (Jurnal Riset Komputer) Vol 9, No 5 (2022): Oktober 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v9i5.4918

Abstract

Lung cancer is the main cause of all cancer diagnoses which reaches 13 percent in the world. According to WHO, lung cancer is the most common type of cancer in men in Indonesia and the fifth most common for all types of cancer in women. This is because the majority of smoking is experienced by men, causing lung cancer. Until now there is no suitable screening method for lung cancer in general. Screening methods that have been recommended for the detection of lung cancer are limited to high-risk patient groups. Whereas the risk of the severity of lung cancer will be even greater if it is not detected early, so as a result lung cancer patients are increasingly difficult to treat. The development of this biomedical technology can be used to assist the early detection process in patients suffering from lung cancer regardless of the criteria for the high risk group first, because if cancer is detected early, the death rate decreases. So in this study the researchers segmented and classified lung cancer using one of the architectures of the CNN algorithm, namely Mobile-Net to facilitate the classification and detection process of lung cancer. As the results of the research conducted by the author on the image of lung cancer with the K-Means segmentation process and classification using the CNN method with the Mobile-Net model, the accuracy is 96.70% and the validation accuracy is 90.45%. This shows that the classification using Mobile-Net with the segmentation process first on the lung cancer image can properly classify the type of disease well
Komparasi Kinerja DenseNet 121 dan MobileNet untuk Klasifikasi Citra Penyakit Daun Kentang Umi Khultsum; Ghofar Taufik
JURIKOM (Jurnal Riset Komputer) Vol 10, No 2 (2023): April 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v10i2.6047

Abstract

Potato plants are one of the plants that are included in horticultural commodities that are widely cultivated by farmers. Potatoes are the part produced from this plant and are the fourth largest agricultural food crop in the world after corn, wheat and rice. Potato plants are susceptible to attacks by various diseases in the leaf area, resulting in delays in potato production. This disease can be recognized by farmers visually, because the infected leaves have a different color and texture from healthy or fresh leaves. However, it was found that detection using the naked eye by farmers required more processing time and often gave inappropriate results. Methods in the field of image processing can be applied, namely by using pattern recognition or characteristics from the image of diseased potato leaves. Through this technique it is hoped that it can detect diseases on potato leaves correctly and accurately. Based on this description, this study aims to design a Convolution Neural Network (CNN) model and evaluate the performance of two architectures, namely DenseNet 121 and MobileNet. From the results of research conducted by the author on potato leaf disease images, it shows that the MobileNet algorithm is better than the DenseNet 12 algorithm. The MobileNet algorithm produces an accuracy of 98.00%, therefore the MobileNet algorithm has better performance for image classification of potato leaf disease
Implementasi Metode TOPSIS dalam Penentuan Sanksi Pelanggaran Siswa di Sekolah Umi Khultsum; Ghofar Taufik
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 3 (2023): Desember 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i3.1393

Abstract

Technology has a tremendous impact on education in terms of obtaining information for both teachers and students in the form of technological system assistance for calculating and analyzing the information obtained as well as student performance reports in computerized form and easily accessible for supervision. This decision support system for determining student violation sanctions is implemented in order to overcome possible problems that occur if there are errors in the form of errors in calculating student violation points which are done manually. This research aims to support the school's decision and help determine appropriate sanctions for students who commit violations. The website-based decision support system that will be developed in this research uses the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method. In implementing the system creation, a software development method was used, namely the waterfall method. The results of manual calculations and calculations carried out by the decision support system that had been created obtained the same results, namely Erlangga Kurniawan (A5) with a preference value of 0.8371256, Erika Kusumawati (A6) with a preference value of 0.16521256, Dani Syahputra (A3) with a preference value of 0.10663703, Wawan Hidayat (A9) with a preference value of 0.10091779, and Andika Putra (A1) with a preference value of 0.06921825 received heavy sanctions. The Decision Support System using the TOPSIS Method can help schools to determine which students will receive heavy sanctions and light sanctions effectively and efficiently.
Co-Authors Fajar Sarasati Ghofar Taufik Ghofar Taufik