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All Journal Jurnal Sistem Cerdas PREDATECS: Public Research Journal of Engineering, Data Technology and Computer Science
Winanda, Salsa
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Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education 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.
User Experience Evaluation of M-Passpor Using User Experience Questionnaire Winanda, Salsa; Tengku Khairil Ahsyar; Angraini; Megawati
Jurnal Sistem Cerdas Vol. 7 No. 3 (2024)
Publisher : APIC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37396/jsc.v7i3.474

Abstract

This study evaluates UX of the M-Passpor application, an m-government application to facilitate the online passport administrative process and reduce queues at the immigration office. Although this application offers convenience in making passports, since its release M-Paspor has received many complaints from users regarding the services provided. A UX evaluation was conducted using the UEQ method, which measures six of these aspects aspects: Attractiveness, Perspicuity, Efficiency, Dependability, Stimulation, and Novelty. The outputs presented that Perspective and Attractiveness aspects had the highest scores, indicating that the app is quite easy to understand and visually appealing. However, the Novelty aspect had a low score, indicating a lack of innovation and new experiences for users. Based on these findings, the researcher recommends improving efficiency, simplifying processes, notifications and feature innovation to improve UX. The outcome of this study is expected to become the basis for improving the UX quality of the M-Passpor application so as to create better and satisfying services for users in the future
Co-Authors Angraini Ginting, Ella Silvana Hastari, Delvi Megawati Nurhaliza, Nana Pratama, Aditya Rezky Tengku Khairil Ahsyar