The Implementation of Multi Label K- Nearest Neighbor Algorithm To Classifying Essay Answers
Keywords:
multi label classification, essay, multi label k-nearest neighborAbstract
One way of assessing essays is to use the STAR (Situation, Task, Action, Result) method. This method aims to classify whether the essay already reflects the values of the situation, task, action, and outcome labels presented by the prospective driving teacher. Multi-label classification refers to a classification task in which each data sample can be classified into multiple categories or labels simultaneously. Various methods have been developed to perform multi-label text classification, such as MLKNN, MLTSVM, and MLP. This research aims to determine the value of the accuracy of the score. Therefore, the Multi-Label K-Nearest Neighbor, or MLKNN, algorithm can be implemented to classify essays with five labels: no star, situation, task, action, and result. With a total dataset of 110, which will be obtained from one of the assessors from the driving teacher. The MLKNN algorithm produces an accuracy rate with an average value of 15.6%.
References
Jefriyanto, J., Ainun, N., & Ardha, M. A. A. (2023). Application of Naïve Bayes Classification to Analyze Performance Using Stopwords. Journal of Information System, Technology and Engineering, 1(2), 49–53.
Cupian, C., Zaky, M., Nurjaman, K., & Kurnia, E. (2020). Analysis of the Implementation of Recruitment, Selection and Placement Based on the Perspective of Islamic Human Capital. Komitmen: Jurnal Ilmiah Manajemen, 1(1), 50–63. https://doi.org/10.15575/jim.v1i1.8289
Ramdani, H. T., Ainun, N., & Muktamar B, A. (2023). Implementation of Progressive Web App on Dropship Data Management Application to Anticipate Product Order Errors. Journal of Information System, Technology and Engineering, 1(2), 38–42.
Fitriani, E. E., & Yustanti, W. (2022). Comparison of the Performance of the Problem Transformation-KNN Method and the Adaptation-KNN Algorithm on the Multi-Label Classification of Question Boxes. Journal of Emerging Information Systems and Business Intelligence, 3(3), 122–129.
Magalhaes, A. D. J., Sopwandin, I., & Bakri, A. A. (2023). Online Training Application Design with Website-Based Blended Learning System Method. Journal of Information System, Technology and Engineering, 1(2), 43–48.
Guterres, A., Gunawan, & Santoso, J. (2019). Tetun Language Stemming Using Rule Based Approach. Teknika, 8(2), 142–147. https://doi.org/10.34148/teknika.v8i2.224
Ainun, N., & Jefriyanto, J. (2023). Development of Kirchoff’s Law Drawing Tools to Improve Student’s Science Skills in Learning Process of Direct Flow Circuits. Journal of Information System, Technology and Engineering, 1(2), 32–37.
Hasanah, M. A., Soim, S., & Handayani, A. S. (2021). Implementation of the CRISP-DM Model Using the Decision Tree Method with the CART Algorithm for Prediction of Potential Flood Rainfall. Journal of Applied Informatics and Computing, 5(2), 103–108. https://doi.org/10.30871/jaic.v5i2.3200
Muktamar B, A., Lumingkewas, C. S., & Rofi’i, A. (2023). The Implementation of User Centered Design Method in Developing UI/UX. Journal of Information System, Technology and Engineering, 1(2), 26–31.
Rizkyani, E., Ernawati, I., & Chamidah, N. (2022). Multi-Label Classification Using the K-Nearest Neighbor (Ml-Knn) Multi-Label Method in Cervical Cancer. JIPI (Jurnal Ilmiah Penelitian Dan Pembelajaran Informatika), 7(4), 1281–1293. https://doi.org/10.29100/jipi.v7i4.3260
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Copyright (c) 2023 John Cheno Lerian, Georgio Chenayan
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