Analisis Pola Peminjaman Buku di Perpustakaan UIN Imam Bonjol Padang Menggunakan Algoritma Apriori Untuk Rekomendasi Penambahan Koleksi

Afriwaningsih, Lidya (2026) Analisis Pola Peminjaman Buku di Perpustakaan UIN Imam Bonjol Padang Menggunakan Algoritma Apriori Untuk Rekomendasi Penambahan Koleksi. Skripsi thesis, UIN Imam Bonjol Padang.

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Abstract

This study aims to analyze book borrowing patterns at the Library of UIN Imam Bonjol Padang using the Apriori algorithm and to generate recommendations for more relevant collection development. The main problem in library collection management is the limited utilization of borrowing transaction data as a basis for decision-making, resulting in collection development practices that tend to be subjective and less targeted. This research employed a quantitative descriptive approach using data mining techniques through the Knowledge Discovery in Databases (KDD) process, which consists of data selection, preprocessing, transformation, data mining, and evaluation and interpretation stages. The dataset used comprised 105,694 book borrowing transactions from 2022 to 2024 obtained from the Library Information System (SLiMS) of UIN Imam Bonjol Padang. The Apriori algorithm was applied using a minimum support value of 0.02 and a minimum confidence value of 0.5. The analysis produced 54 association rules and identified several classifications with high borrowing frequencies, including 001.4 (Research) with a support value of 18.7%, 2X7.3 (Islamic Education) with 16.3%, 2X4 (Islamic Jurisprudence) with 14.8%, 001.42 (Research Methods) with 13.9%, 371.3 (Teaching Methods) with 12.7%, and 371.1 (Teachers and Learning Activities) with 12.6%. Strong associations were found among research-related classifications (001.4 and 001.42), educational classifications (371.1 and 371.3), and Islamic education classifications, indicating that these subjects are frequently borrowed together. Based on these findings, collection development recommendations were formulated by prioritizing core collections, integrated Islamic studies collections, classifications with strong association relationships, and highly borrowed classifications that reflect individual user needs. The results demonstrate that borrowing transaction data can be effectively utilized to support objective, data-driven collection development policies in Islamic State Higher Education Institution (PTKIN) libraries.

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