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All Journal Indonesian Journal of Informatic Research and Software Engineering PREDATECS: Public Research Journal of Engineering, Data Technology and Computer Science
Nurhaliza, Nana
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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Pengelompokan Data Kasus Covid-19 di Dunia Menggunakan Algoritma DBSCAN: Clustering of Data Covid-19 Cases in the World Using DBSCAN Algorithms Nurhaliza, Nana; Mustakim, Mustakim
Indonesian Journal of Informatic Research and Software Engineering (IJIRSE) Vol. 1 No. 1 (2021): Indonesian Journal of Informatic Research and Software Engineering
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (848.445 KB) | DOI: 10.57152/ijirse.v1i1.42

Abstract

World Health Organization (WHO) telah mendeklarasikan Virus Corona sebagai pandemi global pada tanggal 11 Maret 2020. Covid-19 sejak awal tahun 2020 hingga akhir April 2020 telah menyebar ke 210 negara dan menghasikan tiga juta kasus positif setiap harinya. Berbagai upaya dilakukan pada setiap negara dalam penanggulangan pandemi ini. Penelitian ini menggunakan teknik clustering untuk mengelompokkan negara-negara dengan pola kasus serupa yang dapat dijadikan rekomendasi untuk acuan penanganan pada suatu negara dengan mengamati negara lainnya yang berada pada satu kelompok. Algoritma DBSCAN diterapkan pada penelitian ini untuk mendapatkan hasil klasterisasi dan validitas cluster diuji dengan Silhouette Index. Dilakukan sebanyak 22 percobaan dengan rentang nilai Eps 0,1 hingga 0,2 dan nilai Minpts yaitu 3 dan 4. Pada penelitian ini diperoleh percobaan dengan nilai Eps 0,2 dan MinPts 3 yang mengasilkan sejumlah 3 cluster memperoleh hasil validitas cluster terbaik dengan nilai Silhouette Index sebesar 0,3624.
Application of Convolutional Neural Network ResNet-50 V2 on Image Classification of Rice Plant Disease Hastari, Delvi; Winanda, Salsa; Pratama, Aditya Rezky; Nurhaliza, Nana; Ginting, Ella Silvana
Public Research Journal of Engineering, Data Technology and Computer Science Vol. 1 No. 2: PREDATECS January 2024
Publisher : Institute of Research and Publication Indonesia (IRPI).

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/predatecs.v1i2.865

Abstract

Rice is the most important crop in global food security and socioeconomic stability. A part of the world's population makes rice a food requirement but the problem is found that all rice varieties suffer from several diseases and pests. Therefore, it is necessary to ensure the quality of healthy and proper rice growth by detecting diseases present in rice plants and treatment of affected plants. In this study, the Convolutional Neural Network (CNN) algorithm was applied in classifying diseases on the leaves of rice plants by experimenting with several parameters and architecture to get the best accuracy. This study was conducted image classification of rice plant disease using CNN architecture ResNet-50V2 with data using preprocessing Augmentation. The test was conducted with three optimizers such as SGD, Adam, and RMSprop by combining various parameters, namely epoch, batch size, learning rate, and SGD and RMSprop optimizers. Division of image data with 70:30 ratio of training data and test data; 80:20; 90:10. From these results, it was found that Adam was the best optimizer in the 80:20 data division in this study with an accuracy level of 0.9992, followed by the SGD optimizer with an accuracy level of 0.9983, while the RMSProp optimizer was ranked third with an accuracy level of 0.9978.
Co-Authors Ginting, Ella Silvana Hastari, Delvi Mustakim Mustakim Pratama, Aditya Rezky Winanda, Salsa