Klasifikasian abstrak paper menggunakan algoritma KKN
Abstract
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.
Downloads
References
Arora, G. (2020). iNLTK: Natural Language Toolkit for Indic Languages. 66–71. https://doi.org/10.18653/v1/2020.nlposs-1.10
Björk, B. C. (2017). Scholarly journal publishing in transition- from restricted to open access. Electronic Markets, 27(2), 101–109. https://doi.org/10.1007/S12525-017-0249-2/FIGURES/1
Blei, D., Ng, A., research, M. J.-J. of machine L., & 2003, undefined. (2003). Latent dirichlet allocation. Jmlr.Org, 3, 993–1022. https://www.jmlr.org/papers/volume3/blei03a/blei03a.pdf?ref=https://githubhelp.com
Bornmann, L., & Daniel, H. D. (2010). The Usefulness of Peer Review for Selecting Manuscripts for Publication: A Utility Analysis Taking as an Example a High-Impact Journal. PLOS ONE, 5(6), e11344. https://doi.org/10.1371/JOURNAL.PONE.0011344
Gasparyan, A., … L. A.-J. of K., & 2013, undefined. (2013). Multidisciplinary bibliographic databases. Synapse.Koreamed.Org. https://synapse.koreamed.org/articles/1022051
Gusenbauer, M., methods, N. H.-R. synthesis, & 2020, undefined. (2020). Which academic search systems are suitable for systematic reviews or meta‐analyses? Evaluating retrieval qualities of Google Scholar, PubMed, and 26 other. Wiley Online Library, 11(2), 181–217. https://doi.org/10.1002/jrsm.1378
Hanani, L., Nugrahaeni, E., & Mustofa, K. (2018). Analisis Perbandingan Algoritma K-Nearest Neighbor (K-NN) dan Naïve Bayes pada Klasifikasi Data Siswa. Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer, 2(6), 2449–2456.
Hastuti, S. D. (2016). Penerapan Metode k-Nearest Neighbor (k-NN) dalam Klasifikasi Dini Pneumonia pada Anak Menggunakan Fitur Gejala. Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer, 1(2), 315–322.
Heryadi, Y., & Wahyono, T. (2020). Machine learning : (konsep dan implemetasi) / penulis, C. https://www.researchgate.net/publication/344419764_Machine_Learning_Konsep_dan_Implementasi
Hidayat, R., & Nurhayati, R. (2019). Penerapan Metode k-Nearest Neighbor (k-NN) dalam Klasifikasi Tingkat Kecanduan Gadget pada Remaja. Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer, 3(11), 4612–4619.
Kusumawardani, R. A., & Mariana, M. (2019). Klasifikasi Topik Bahasa Indonesia dengan Algoritma k-Nearest Neighbor dan TF-IDF. Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer, 3(6), 2582–2589.
Mongeon, P., & Paul-Hus, A. (2016). The journal coverage of Web of Science and Scopus: a comparative analysis. Scientometrics, 106(1), 213–228. https://doi.org/10.1007/S11192-015-1765-5
Pedregosa et al. (2011). Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research.
Pradana, A. W., & Hayaty, M. (2019). The Effect of Stemming and Removal of Stopwords on the Accuracy of Sentiment Analysis on Indonesian-language Texts. Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, 4(4), 375–380. https://doi.org/10.22219/KINETIK.V4I4.912
Purnomo, H., & Hartanto, R. (2017). Penerapan Metode k-Nearest Neighbor dalam Klasifikasi Berita Kriminal (2nd ed., Vol. 1). Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer.
Rachmawati, D., & Prasetio, A. P. (2019). Klasifikasi Teks Bahasa Indonesia Menggunakan Metode k-Nearest Neighbor dan Seleksi Fitur Chi-Square. Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer, 3(12), 5112–5119.
Copyright (c) 2024 Farid Ardianto
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work’s authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal’s published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.