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All Journal Substantia: Jurnal Ilmu-Ilmu Ushuluddin Sinkron : Jurnal dan Penelitian Teknik Informatika EDUCATIO : Journal of Education JURNAL INTEGRASI BAREKENG: Jurnal Ilmu Matematika dan Terapan JURNAL PENDIDIKAN TAMBUSAI Indonesian Journal on Learning and Advanced Education (IJOLAE) Jurnal Promotif Preventif Journal of Applied Data Sciences JOLLT Journal of Languages and Language Teaching Pustaka Karya : Jurnal Ilmiah Ilmu Perpustakaan dan Informasi Indonesian Journal of Elementary Education (IJEE) Jurnal Riset Ilmiah El-Sirry: Jurnal Hukum Islam dan Sosial Jurnal Teras Pengabdian Masyarakat
Lathifah, Lathifah
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Journal : Pustaka Karya : Jurnal Ilmiah Ilmu Perpustakaan dan Informasi

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Prediction of librarian interest in library management in Pamekasan with comparison of SVM and KNN algorithms Lathifah, Lathifah; Yuadi, Imam
Pustaka Karya : Jurnal Ilmiah Ilmu Perpustakaan dan Informasi Vol. 13 No. 1: Juni 2025
Publisher : S1 Ilmu Perpustakaan dan Informasi Islam FTK UIN Antasari Banjarmasin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18592/pk.v13i1.15839

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.
Co-Authors Abdul jalil Aceng Jaelani, Aceng Aisida, Sufinatin Alkhair, Alkhair Anam, M Khairul Anam, M. Khairul Apriliani, Yenni Aries Fachriza Arini Arini Bambang Harmanto, Bambang Darmin Darmin, Darmin Dina Agustina, Dina Efendy, Muhammad Yusuf Efrizoni, Lusiana Erlik Widiyani Styati, Erlik Widiyani Firdaus, Muhammad Bambang Firmasnyah, Muhd Hamdani Hamdani Harija, Akas Harun Joko Prayitno Imam Yuadi Irwanda Syahputra Khoiriyah Khoiriyah Kustiyono, Kustiyoni Kuswanti, Nurilah Lestari, Eka Puzi M. Noris Malik, Agus Mastorat Mualim, Mohamad Munawir Munawir Muzar, Missriani Nidhom, Khoirun Nurikan, Nurikan Nurul fadillah, Nurul Oktaviani, Zennith Dwipa Patimah Patimah Pradipta, Rahman Prafanto, Anton purwanti, rina Puzi Lestari, Eka Rosmasari Rosmasari, Rosmasari Salsabiila, Salsabiila Sari, Atalya Kurnia Sidik, Zam Resa Simpol, Wanida Suandi, Fadli Susanti Susanti Syaputra, Rofi Anwar Tamrin, Agusti Tri Putri Lestari, Tri Putri Wirta Agustin