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All Journal Jurnal Pekommas CogITo Smart Journal ILKOM Jurnal Ilmiah JurTI (JURNAL TEKNOLOGI INFORMASI) Joined Journal (Journal of Informatics Education Jurnal Pengabdian Inovasi dan Teknologi Kepada Masyarakat Agents: Journal of Artificial Intelligence and Data Science Journal Entrepreneurship Digital
ST. Aminah Dinayati Ghani
Universitas Dipa Makassar
Arts Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Social Sciences Other

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Journal : CogITo Smart Journal

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Aplikasi Pengenalan Pola Penyakit Kulit Menggunakan Algoritma Linear Discriminant Analysis ST. Aminah Dinayati Ghani; Indo Intan; Nur Salman
CogITo Smart Journal Vol. 8 No. 1 (2022): Cogito Smart Journal
Publisher : Fakultas Ilmu Komputer, Universitas Klabat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31154/cogito.v8i1.365.206-218

Abstract

Sometimes, someone underestimates to check up skin disease unless the disease has affected his face or body in severe condition. The checkup fees for skin diseases are relatively expensive because they require a specialist. While the reach of society, in general, is the lower community. The purpose of this study is to bridge the gap between the patient and the examination of the disease based on the patient's skin image. The methods used in feature extraction and classification are Linear Discriminant Analysis LDA and Euclidean Distance respectively. LDA performs image feature extraction through a matrix operation process and distinguishing features in the same class and different classes. Classification will give the output of disease: abscess, eczema, ringworm, and urticaria. The accuracy results obtained are 80%. The next research is on adding features in the form of skin color so that it can be an input feature in the image as well as to improve its performance in the future. This application can be an alternative initial checkup for patients. It will detect the type of skin disease be suffered before consulting an expert.
Implementasi Convolutional Neural Network terhadap Citra X-Ray Dada COVID-19 Berbasis Mobile Indo Intan; Suryani Suryani; ST Aminah Dinayati Ghani; Moh. Rifkan; Syamsul Bahri
CogITo Smart Journal Vol. 10 No. 1 (2024): Cogito Smart Journal
Publisher : Fakultas Ilmu Komputer, Universitas Klabat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31154/cogito.v10i1.640.625-641

Abstract

The COVID-19 pandemic outbreak is the most significant event from 2019 until 2021. A medical examination of radiological images is carried out to check the condition of the patient's lungs. The limitations of this examination need alternative computer-assisted applications for patient CXR. This research aims to implement a back-end and front-end-based Convolutional Neural Network (CNN) model. Its advantage is that it can detect CXR images in real-time and non-real-time using multi-classification, namely normal, pneumonia, and COVID-19. The CNN model carries out the process of convolutional feature extraction and multi-layer perceptron classification at the back-end stage. In contrast, it uses an Android mobile-based application at the front-end stage. The research results show that the non-real-time condition has an accuracy of 98%, while the real-time is 95% lower. This research produces model and application performance that is flexible for user needs. The results can be recommended for developing applications for more comprehensive users.
Co-Authors Andi Alfian Aswar TC. Koswara Donny Liklikwatil , Rudy Farhan Wahyuta Kusuma, Farhan Wahyuta Kusuma Fatmasari , Fatmasari Indo Intan Maria Nova Sarembona Moh. Rifkan Muhammad Agus Muljanto MUHAMMAD RIZAL Nurdin Nurdin Nurdin Nurdin Rismayani Rismayani Rudy Donny Liklikwatil Salman, Nur Siti Hajratul Aswa Sitti Aisa Suryani Suryani Syamsul Bahri