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All Journal JURIKOM (Jurnal Riset Komputer) Jurnal Sistem Komputer dan Informatika (JSON)
Ardhian Ekawijana
Politeknik Negeri Bandung, Bandung
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Implementasi Aplikasi Kepengaturan Dokumen Akreditasi Program Studi dengan Metode Borg and Gall Ardhian Ekawijana; Bambang Wisnuadhi
JURIKOM (Jurnal Riset Komputer) Vol 9, No 2 (2022): April 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v9i2.4015

Abstract

The Study Program Accreditation Document Arrangement Application is an application that helps to arrange accreditation supporting evidence documents. Accreditation is often a tiring job because all supporting evidence is scattered, both in physical or digital format. The focus of this research is on making your own application product. The research method is based on the Borg and Gall method. This method has several stages to make a product. The programming language used is NodeJS. NodeJS has a small execution time and is light, so it does not burden the user's device.The result of this research is in the form of an application, according to this method the results of this application have only reached the stage of Develop preliminary form of product.
Deteksi Dini Anak Disleksia dengan metode Support Vector Machine Ardhian Ekawijana; Akhmad Bakhrun; Zulkifli Arsyad
Jurnal Sistem Komputer dan Informatika (JSON) Vol 4, No 1 (2022): September 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v4i1.4776

Abstract

Dyslexia is a brain disorder caused by genetics. People with dyslexia can live a normal life and even have certain advantages if they get the correct education. People with dyslexia often get the predicate stupid because teachers do not know the case of their students. Early detection of dyslexic children can be done with a series of tests so that the system can conclude that the data is dyslexic or not. Support Vector Machine is a data classification method to share dyslexia test results or not. This system is trained with test results data that are already available using the SVM method. This study uses gamification data to detect dyslexic children or not. SVM proves a good level of accuracy in predictions up to 94%.
Co-Authors Akhmad Bakhrun Wisnuadhi, Bambang Zulkifli Arsyad