Klasifikasian abstrak paper menggunakan algoritma KKN

  • Farid Ardianto Program Studi Teknik Informatika, Universitas Islam Negeri Maulana Malik Ibrahim Malang
Keywords: K-Nearest Neighbor, Abstract Classification, Distance Metrics, Accuracy Evaluation


The K-Nearest Neighbors (KNN) algorithm was used in this study to categorize research abstracts according to their topic. This research was created to assist writers and researchers in choosing a place for journal publication that is appropriate to the topic in the abstract. The dataset was collected manually through restrictions by only searching Sinta 2-indexed journals for related abstracts in the trial. The relevant topics are then extracted from the abstract using natural language processing (NLP) techniques. Abstracts are categorized into certain subject groups using the KNN algorithm. This research was conducted by adjusting the variables of the parameters used. By setting random_state values, determining n-neighbors values, and determining the best distance matrix, which will produce high precision, recall, and a good F1-score rating. High precision, recall, and F1-score ratings in system performance evaluation are used as indicators of how well research topics are categorized and as a measure of efficiency.


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How to Cite
Ardianto, F. (2024). Klasifikasian abstrak paper menggunakan algoritma KKN. Maliki Interdisciplinary Journal, 2(6). Retrieved from http://urj.uin-malang.ac.id/index.php/mij/article/view/5564