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All Journal JUITA : Jurnal Informatika Jurnal Informatika Universitas Pamulang Abdimas Umtas : Jurnal Pengabdian kepada Masyarakat Jurnal Pendidikan dan Pengabdian Masyarakat
Theresia Ratih Dewi Saputri
Universitas Ciputra Surabaya
Religion Agriculture, Biological Sciences & Forestry Arts Computer Science & IT Education Social Sciences Other

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Journal : Jurnal Informatika Universitas Pamulang

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Comparative Study on Regression Algorithms for Predicting Price of Online Course: Udemy Case Study Maximus Aurelius Wiranata; Theresia Ratih Dewi Saputri
Jurnal Informatika Universitas Pamulang Vol 8, No 2 (2023): JURNAL INFORMATIKA UNIVERSITAS PAMULANG
Publisher : Teknik Informatika Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/informatika.v8i2.30562

Abstract

Talent in the field of information technology is much needed. However, studying in the field of information technology requires a sizable fee. Online courses are a cost-effective option for learning. Online course sites like Udemy provide and sell hundreds of thousands of courses and have thousands of trusted instructors. With so many Udemy instructors, prices vary widely because the course pricing system is completely set by the teaching instructor. This means that the selling price of the course is not affected by the quality of the course, so not all courses are recommended to be purchased. To overcome this problem, a system is needed that can predict course prices so that it can advise instructors in determining selling prices. To compare the best algorithms used to create this system, three algorithms are used in this study: multiple linear regression, polynomial regression, and K-Nearest Neighbors Regression. The researcher uses 1200 data sample from web scraping results from the Udemy site, with one test for each algorithm. As a result, the K-Nearest Neighbors Regression got the best evaluation results with a root mean squared error value of 231659.49, a mean absolute percentage error of 0.43, and a coefficient of determination of 0.18.
Penerapan Metode Image-to-Speech melalui Kamera dalam Aplikasi berbasis Kecerdasan Buatan untuk Orang dengan Disleksia Daniel Aprillio; Anna Bella Atmadjaja; Bryan; Mychael Wijaya; Theresia Ratih Dewi Saputri
Jurnal Informatika Universitas Pamulang Vol 9 No 1 (2024): JURNAL INFORMATIKA UNIVERSITAS PAMULANG
Publisher : Teknik Informatika Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/informatika.v9i1.39173

Abstract

Dyslexia occurs worldwide despite the culture or language. Dyslexia affects about 9% - 12% of the population, with 2% - 4% of the population experiencing significant reading impairments. This research aims to develop an artificial intelligence-based application using the Image-to-Speech method that can convert digital text into audible sound for individuals with dyslexia without requiring their brain to process the writing. This method can assist people with dyslexia in daily life challenges such as reading traffic signs, books, or documents. Results from 10 experiments on the implementation of the proposed method indicate that individuals with dyslexia can scan the text they want to read using a camera from a smartphone or laptop. The expirements also shows that the application can convert text in image form into sound comprehensible to those with dyslexia, thus facilitating their recognition of digital writing with 90% accuracy. The application also demonstrates efficiency in terms of data processing time. The average time required for image to audio conversion is 0.22 seconds, with an average memory usage of 163.2 MiB.
Co-Authors Anna Bella Atmadjaja Bryan Daniel Aprillio Evan Tanuwijaya Felicia Graciella Laura Mahendratta Tjahjono Lestari, Caecilia Citra Maximus Aurelius Wiranata Michelle Chandra Mychael Maoeretz Engel Mychael Wijaya Rinabi Tanamal Salmon Charles Siahaan Trianggoro Wiradinata Yosua Soekamto Yuwono Marta Dinata