Implementation of a priori algorithm for book lending at state high school library I Silima Pungaga-Punga Parongil
DOI:
10.33395/sinkron.v7i1.11257Keywords:
Data Mining, Lending Patterns, Books, Libraries, Algorithms A prioriAbstract
The library plays a role in helping students to enjoy reading books. The availability of books in various fields motivates students to come to visit the library, students / i can read or borrow library books. For this reason, the purpose of this research was carried out including helping library officials apply the rules of how to visit the libraries.
In carrying out research methods, including conducting direct observations, conducting interviews to collect the necessary data. The pattern that will be analyzed is the pattern of borrowing what books are often borrowed so that library officials know the information of books that are often borrowed. The result obtained is with the application of a priori algorithms, book data is processed to produce a pattern of borrowing books. After all high frequency patterns are found, then the association rules are sought that meet the minimum requirement for associative confidence A→B minimum confidence = 25%. The final association rules are ordered based on minimum support and minimum confidence, if borrowing IPA, then borrowing MTK Support = 15%, Confidence = 42.8%.
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References
Badrul, M. (2016). Algoritma Asosiasi Dengan Algoritma Apriori Untuk Analisa Data Penjualan. None.
Dengan, S., Algoritma, C., & Hidayati, W. (2018). Data Mining Penentuan Tenaga Perawat Di RSUD Sultan. 1(2), 1–7.
Esling, P., & Agon, C. (2012). Time-series data mining. ACM Computing Surveys. https://doi.org/10.1145/2379776.2379788
Febrivani, E., & Winanjaya, R. (2021). Penerapan Data Mining Asosiasi Pada Persediaan Obat. 3(3), 354–365.
Fricles A Sianturi, Hasugian, Paska Marto, Simangunsong Agustina, N. B. (2019). Data Mining |Teori dan Aplikasi Weka. In -: Vol. (Issue).
Fricles Ariwisanto Sianturi, Petti Indrayati Sijabat, Amran Sitohang, R. M. S. (2020). APRIORI ALGORITHM FOR THE DETERMINATION OF THE. Infokum, Jurnal, 9(1), 11–20.
Mardi, Y. (2017). Data Mining : Klasifikasi Menggunakan Algoritma C4.5. Jurnal Edik Informatika.
Meilina, P. (2015). Penerapan Data Mining dengan Metode Klasifikasi. Jurnal Teknologi Universitas Muhammadiyah Jakarta.
Sianturi, F. A., Informatika, T., & Utara, S. (2018). Penerapan Algoritma Apriori Untuk Penentuan Tingkat. Mantik Penusa, 2(1), 50–57. http://e-jurnal.pelitanusantara.ac.id/index.php/mantik/article/view/330
Sowmya, R., & Suneetha, K. R. (2017). Data Mining with Big Data. Proceedings of 2017 11th International Conference on Intelligent Systems and Control, ISCO 2017. https://doi.org/10.1109/ISCO.2017.7855990
Sulastri, H., & Gufroni, A. I. (2017). Penerapan Data Mining Dalam Pengelompokan Penderita Thalassaemia. Jurnal Teknologi Dan Sistem Informasi, 03(02), 299–305. https://doi.org/10.25077/TEKNOSI.v3i2.2017.299-305
Suyanto. (2017). Data mining Untuk Klasifikasi dan Klasterisasi Data. SpringerReference.
Witten, I. H., Frank, E., Hall, M. A., & Pal, C. J. (2016). Data Mining: Practical Machine Learning Tools and Techniques. In Data Mining: Practical Machine Learning Tools and Techniques. https://doi.org/10.1016/c2009-0-19715-5
Wu, X., Zhu, X., Wu, G. Q., & Ding, W. (2014). Data mining with big data. IEEE Transactions on Knowledge and Data Engineering. https://doi.org/10.1109/TKDE.2013.109
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Copyright (c) 2022 Andini Yulistia Ningsih, Volvo Sihombing, Sahat Parulian
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Muhammad Khoiruddin harahap
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