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All Journal GEMA TEKNOLOGI Jurnal Algoritma JUSTIFY : Jurnal Sistem Informasi Ibrahimy COREAI: Jurnal Kecerdasan Buatan, Komputasi dan Teknologi Informasi JAR'S (Journal of Advanced Research in Informatics)
Miftahul Arifin
Universitas Wiraraja
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Mechanical Engineering Transportation

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Deteksi Anomali Peminjaman Buku Menggunakan Algoritma Isolation Forest untuk Meningkatkan Efisiensi Layanan Miftahul Arifin; Rizal Sapta Dwi Harjo; Ilman Firmansa; Muhammad Faizal Ilham
Jurnal Algoritma Vol 23 No 1 (2026): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.23-1.3117

Abstract

University libraries require systems capable of detecting abnormal book borrowing behavior to maintain service effectiveness. Without an anomaly detection mechanism, libraries are prone to various operational issues, such as increased manual verification workload for librarians, delays in identifying problematic borrowings, and discrepancies in book inventory records. Based on preliminary data prior to this study, an average of 8–12% of transactions exhibited irregularities, such as extreme delays, borrowings outside operational hours, or unusually large numbers of books borrowed in a single transaction. This condition highlights the need for an analytical approach that can provide faster and more consistent detection than manual inspection. This study applies the Isolation Forest algorithm to detect anomalies in the book borrowing dataset of the Wiraraja University Library. The borrowing data are processed through data cleaning, feature extraction, and standardization before being used for model training. The results show that the model achieves an accuracy of 90% in identifying normal borrowing patterns and successfully detects transactions with extreme characteristics as anomalies. These findings confirm that a machine learning–based approach can improve library operational efficiency while minimizing the risk of abnormal transactions that were previously difficult to detect through manual processes.
Co-Authors Aam Shodiqul Munir Ahmad Sururi Alamsyah, Dafiq Fajri Arief Wibowo Fauzi Helmi Fauzi Helmi Fauzi Helmi Haninda Bharata Helmi, Fauzi Hikmawansyah, R Bagus iddrus Iddrus Ilman Firmansa Muhammad Faizal Ilham Rakhmat Rakhmat Ratna Ayu Sekarwati Rizal Sapta Dwi Harjo Rizal Sapta Dwi Harjo Undang Rosidin