Open AccessCooking Class Recommendation Using Content Based Filtering for Improving Chef Learning Practical Skill
Author(s) -
Budi Wibowotomo,
Eris Dwi Septiawan Rizal,
Muhammad Akbar,
Dediek Tri Kurniawan
Publication year - 2021
Publication title -
international journal of interactive mobile technologies
Language(s) - English
Resource type - Journals
ISSN - 1865-7923
DOI - 10.3991/ijim.v15i08.21581
Subject(s) - validator , class (philosophy) , computer science , recommender system , white box testing , selection (genetic algorithm) , tutor , test (biology) , usability , relevance (law) , collaborative filtering , information retrieval , world wide web , multimedia , artificial intelligence , human–computer interaction , paleontology , software construction , software , biology , software system , political science , law , programming language
Koolinera is a web-based e-learning application about learning to cook Indonesian culinary dishes. Users are free to choose cooking classes. Culinary in Indonesia is very diverse, so many users feel confused in choosing a cooking class. No specific guidance is given to users on tips for choosing a cooking class. Therefore, it is important to develop a feature that can help users to guide the selection of cooking classes, namely by building a cooking class selection recommendation system. Class recommendations are obtained based on the last class taken by the user. The criteria used to determine the recommendations are the similarity of class names, dominant taste of cuisine, category of cuisine, area of origin, and tutor. The algorithm used is Content-Based Filtering with TF-IDF calculations. The recommendations given to users are a list of six cooking classes. Testing is carried out based on black box testing, expert validation, and user testing. The blackbox test carried out states that all functions are running well. The validity test of the media by the validator got a percentage of 96.52%. User testing in the Usability Tetsing Experience section got a percentage of 85.73%, User Acceptance Testing got a percentage of 83.89% and testing the relevance of the recommendation system got a percentage of 88.69%