MACHINE LEARNING JOURNAL ARTICLE RECOMMENDATION SYSTEM USING CONTENT BASED FILTERING

Afika Rianti, Nuur Wachid Abdul Majid, Ahmad Fauzi

Abstract


Indonesia is a country that hasn’t studied much about artificial intelligence. This has resulted in a small number of publications related to that field including areas within such as machine learning. For that reason, it caused difficulties in finding relevant journal articles. The purpose of this study is to know the performance of the Content Based Filtering method in providing machine learning journal article recommendations. The research procedure used is CRISP-DM with algorithms used are TF-IDF and Cosine Similarity. The dataset used consists of 100 machine learning journal articles. Based on the research that has been done, it’s concluded that the performance of the Content Based Filtering method in providing machine learning journal article recommendations as measured using the precision evaluation matrix showed a score of 76%, which means the result is quite good. However, the model couldn’t be used properly for some data due to the small number of datasets which affects the limited recommendations. 


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DOI: http://dx.doi.org/10.12962/j24068535.v22i1.a1193

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