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All Journal PREDATECS: Public Research Journal of Engineering, Data Technology and Computer Science
Hastari, Delvi
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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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.
Application of The Fuzzy Mamdani Method in Determining KIP-Kuliah Recipients for New Students Ardiansah, Yoga; Luchia, Nanda Try; Hastari, Delvi; Rifat, T. M. Fathin; Rachfaizi, Rendhy; Putri, Nanda Aulia; Ginting, Ella Silvana
Public Research Journal of Engineering, Data Technology and Computer Science Vol. 2 No. 1: PREDATECS July 2024
Publisher : Institute of Research and Publication Indonesia (IRPI).

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/predatecs.v2i1.1087

Abstract

Lectures are the last level of education passed. However, the opportunity to obtain further education cannot be owned just like that by everyone because of the economic factors they experience. Therefore, an assessment method is needed to support the decision of KIP-Kuliah recipients at the lecture level for new students within the Faculty of Science and Technology, Sultan Syarif Kasim Riau State Islamic University. This research applies the Fuzzy Mamdani algorithm with Fuzzy Logic and is expected to be able to provide recommendations for worthy scholarship recipients so that the assistance provided is right on target. The results showed that 26,7% of students received the rejected status. Several experiments conducted, illustrate the performance of Fuzzy Logic in this research is very powerful in determining policies and as decision support. The implementation of the research results recommends the best selection from a series of decisions making.
Implementation of Naïve Bayes Classifier for Classifying Alzheimer’s Disease Using the K-Means Clustering Data Sharing Technique Putri, Wildani; Hastari, Delvi; Faizah, Kunni Umatal; Rohimah, Siti; Safira, Devy
Public Research Journal of Engineering, Data Technology and Computer Science Vol. 1 No. 1: PREDATECS July 2023
Publisher : Institute of Research and Publication Indonesia (IRPI).

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/predatecs.v1i1.803

Abstract

Alzheimer's disease is a neurodegenerative disease that is very universal and characterized by memory loss and cognitive function decline which ultimately leads to dementia. In 2015, it is estimated that around million people worldwide will suffer from Alzheimer's disease or dementia. Globally, the number of Alzheimer's diseases will increase from 26.6 million in 2006 to 106.8 million cases in 2050. Due to the large number of people with Alzheimer's disease, it is necessary to classify symptoms that lead to indicators of Alzheimer's disease, so that data mining methods are used for data processing. Alzheimer's data taken from Kaggle amounted to 373 records, through the stages of data preprocessing, data sharing using the Hold-Out method and clustering with AK-Means algorithm. The data is processed using data mining techniques using NBC algorithms. Validation testing the accuracy value obtained the result that the NBC algorithm with K-Means Clustering data sharing has relatively better accuracy than the hold-Out method of 91.89%.
Co-Authors Ardiansah, Yoga Faizah, Kunni Umatal Ginting, Ella Silvana Luchia, Nanda Try Nurhaliza, Nana Pratama, Aditya Rezky Putri, Nanda Aulia Putri, Wildani Rachfaizi, Rendhy Rifat, T. M. Fathin Safira, Devy Siti Rohimah Winanda, Salsa