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All Journal Techno Nusa Mandiri : Journal of Computing and Information Technology Journal of Applied Engineering and Technological Science (JAETS)
Rifki Sadikin
Universitas Nusa Mandiri
Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Industrial & Manufacturing Engineering

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COMPARATIVE CLASSIFICATION OF LUNG X-RAY IMAGES WITH CONVOLUTIONAL NEURAL NETWORK, VGG16, DENSENET121 Muhammad Ilham Prasetya; Yuris Alkhalifi; Rifki Sadikin; Yan Rianto
Techno Nusa Mandiri: Journal of Computing and Information Technology Vol 19 No 1 (2022): TECHNO Period of March 2022
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/techno.v19i1.3010

Abstract

Lungs are one of the organs of the human body, and lung tissue will ultimately affect human abilities. The respiratory system exchanges oxygen and carbon dioxide in the blood. Problems that often occur are polluted air quality, many bacteria that attack the lungs, and lung disease can cause shortness of breath, mobility difficulties, and hypoxia, so that if not detected immediately it can cause death. In this regard, the aim of this study is to compare the classification of normal lungs with those of those suffering from Cardiomegaly. The preparation of this dataset is a form of contribution in improving the quality of the disease classification system on X-ray images. CNN, VGG 16 and DenseNet methods were chosen as classification methods to ensure performance and which method is the best for classifying Lung Diseases. It can be concluded that by using the DenseNet121 model, X-Ray images in this research dataset get an accuracy of 67.06%, for the VGG16 model it gets an accuracy of 68.94% and for the CNN model it gets the highest accuracy of 80.54%.
Hybrid Between PIECES Framework and Technology Acceptance Model (TAM) in Quality Testing Of Mobile Application Office Automation System (eKEMENKEU) Ade Achmad Zulfahmi; Rifki Sadikin; Eni Heni Hermaliani
Journal of Applied Engineering and Technological Science (JAETS) Vol. 4 No. 1 (2022): Journal of Applied Engineering and Technological Science (JAETS)
Publisher : Yayasan Riset dan Pengembangan Intelektual (YRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (580.029 KB) | DOI: 10.37385/jaets.v4i1.1087

Abstract

Digital transformation is a necessity that must be carried out by the government that provides public services. The success of digital transformation is to innovate the Office Automation System Mobile Application System (eKemenkeu application) based on Android/IOS to support simplification of business processes so that they can be easily accessed via smartphone devices. To get feedback on the quality of the eKemenkeu application, testing was carried out using the PIECES Framework and by selecting the Technology Acceptance Model (TAM) variable, as well as the Smart PLS 3.0 tool. The study analyzed 100 respondent data using 7 hypotheses with the results of 2 accepted hypotheses, namely H1 Original Sample value (0.335) and t-statistics (2.864) and H7 Original Sample (0.748) and t-statistics (10.401), and the second significance level is above 0, 05. Meanwhile, 5 hypotheses were rejected, because the t-statistic value was > 1.96 and the significance level of the p-value was below 0.05. The results of the Discriminate Validity test ranged in value (0.786 - 0.949). Composite Reliability ranges in value (0.828 - 0.949) or is accepted because it is above 0.7. Cronbach's Alpha range of values (0.721-0.920) or satisfactory > 0.7 .
Co-Authors Ade Achmad Zulfahmi Eni Heni Hermaliani Muhammad Ilham Prasetya Yan Rianto Yuris Alkhalifi