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All Journal Jurnal Informatika Universitas Pamulang JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Maximus Aurelius Wiranata
Universitas Ciputra Surabaya
Computer Science & IT

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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 YOLOv5 untuk Klasifikasi Gambar dalam Sistem Estimasi Kandungan Kalori Masakan Indonesia Maximus Aurelius Wiranata; Caecilia Citra Lestari
Jurnal Teknik Informatika dan Sistem Informasi Vol 11 No 1 (2025): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v11i1.10284

Abstract

In this era of continuously evolving technology, calorie counting applications have become crucial for individuals who are concerned about their eating habits and health. However, most of these applications have not fully accommodated the variety of dishes commonly consumed in Indonesia, especially the popular dishes in Java Island, which has the largest population in Indonesia. To address this limitation, this research introduces an innovative solution in the form of an Indonesian Cuisine Classification and Calorie Content Estimation System using YOLOv5 technology. In this approach, the YOLOv5 object classification technology is used to identify various types of Indonesian dishes, including eight classes such as satay, meatball soup, traditional soup, fried rice, mixed vegetables salad, fried chicken, beef soup, and beef stew. This system is not only capable of accurately classifying dishes but also provides calorie content estimation based on the composition of the classified food ingredients. The implementation of this research combines YOLOv5 to apply the Indonesian cuisine classification model using the nutrition API from API Ninjas to obtain the required nutrition data. This research uses datasets obtained from Kaggle website, Mendeley Data, and Roboflow, with a total of 303 images for each class of dishes. As a result, the model achieved an accuracy score of 94.2%, precision of 94.3%, recall of 93.8%, and an F1 Score of 93.8%.
Co-Authors Caecilia Citra Lestari Theresia Ratih Dewi Saputri