Prediction Prediction of librarian interest in library management in Pamekasan with comparison of SVM and KNN algorithms
DOI:
https://doi.org/10.18592/pk.v13i1.15839Keywords:
interest classification, library organization, support vector machine (SVM), k-nearest neighbors (KNN)Abstract
This study was conducted to determine the prediction of librarian interest in joining a library organization. Using survey data and interviews with librarians that produced 130 test data then divided into two groups of data, namely "interested" and "not interested". Using the Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) models as a comparison of the performance of the two algorithms in classifying librarian interests. The results of the test data were then evaluated using a confusion matrix to assess the accuracy, precision, and recall of each model. The results of the interest predictions tested showed that the use of the SVM model was more consistent in classifying librarian interests with high accuracy, although there were some errors in the "Not Interested" category. While the results of interest predictions using the KNN model tended to dominate the prediction of the "Interested" category, there were more errors in identifying the "Not Interested" category. Both models show their respective advantages and disadvantages in classifying librarian interest predictions. From the results of this study, it can be a picture and insight into the effectiveness of using the two models in classifying librarian interest predictions in joining a library organization and as a guide in choosing the right algorithm in similar research.References
Afrash , M.R., Shafiee , M., & Kazemi-Arpanahi , H. (2023). Building a machine learning model to predict early risk of gastric cancer based on lifestyle factors. BMC Gastroenterology , 23 (1).
Anggoro , D.A., & Permatasari , D. (2023). Comparison of Kernel Performance of Support Vector Machine Algorithm for Diabetes Mellitus Classification. International Journal of Advanced Computer Science and Applications , 14 (1), 580–585.
Anin Nazura, I., & Budi Susanto, A. (2023). Analysis of Student Satisfaction Package C PKBM Siliwangi Pamulang Accepted in State Universities Using Support Vector Machine Algorithm and K-Nearest Neighbor Method (Case Study: PKBM Siliwangi School) . 1 (4), 39–49.
Aslam, M. (2018). Current trends and issues affecting academic libraries and leadership skills. Library Management , 39 (1–2), 78–92.
Helmy, Y., Khedrb , A.E., Koliefa , S., & Haggag, E. (2019). An enhanced business intelligence approach to improve customer satisfaction using mining techniques. International Journal of Computer Science and Information Security (IJCSIS ) , 17 (4), 159–175.
Hmeidi , I., Hawashin , B., & El- Qawasmeh , E. (2008). Performance of KNN and SVM classifiers on full Arabic articles . Volume 22 (Issue 1), Pages 106-111.
Manimaran, P., Priyadharshini, G., & Yamuna Devi, N. (2022). Predicting placement eligibility for students using data mining. International Journal of Health Sciences , 6 (May), 460–467.
Massis, B. E. (2012). The use of predictive analytics in libraries. New Library World , 113 (9), 491–494.
Nurul Hayati. (2014). Communication in library organizations. Communication in library organizations Nurul , 2 (1), 49–59.
Parul Sinha, & Poonam Sinha. (2015). Comparative Study of Chronic Kidney Disease Prediction Using KNN and SVM. International Journal of Engineering Research and Development , V4 (12).
Raj Kumar, MAS (2016). Comparative analysis of SVM & KNN for academic prediction of students . Volume -- 1 , 15–19.
Saputra, D., & Ary, M. (2020). Prediction of Customer Interest in Savings Products Using Polynomial Kernel Parameter SVM Algorithm. E-Proceedings Information System (POTENSI) , 1 (1), 162–171.
Sharma, N., Appukutti, S., Garg, U., Mukherjee, J., & Mishra, S. (2023). Analysis of Students’ Academic Performance Based on Time Spent on Extracurricular Activities Using Machine Learning Techniques. International Journal of Modern Education and Computer Science , 15 (1), 46–57.
Tabrizi, S. S., & Cavus, N. (2016). Hybrid KNN-SVM Model for Vehicle License Plate Recognition in Iran. Procedia Computer Science , 102 (August), 588–594.
Thanh Noi, P., & Kappas, M. (2017). Comparison of Random Forest, k-Nearest Neighbor, and Support Vector Machine Classification for Land Cover Classification Using Sentinel-2 Imagery. Sensors (Basel, Switzerland ) , 18 (1).
Weiner, SG (2017). Academic Library Leadership: A Literature Review. Educational Libraries , 26 (2), 5.
Wiyono, S., & Abidin, T. (2019). Comparative Study of KNN, SVM, and Decision Tree Machine Learning Algorithms to Predict Student Learning Achievement. International Research Journal - GRANTHAALAYAH , 7 (1), 190–196.
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