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Zalukhu, Indri Feni Asih
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Computer Science & IT Economics, Econometrics & Finance

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Perancangan Data Warehouse Untuk Analisis Peminjaman Dan Pengembalian Buku Di Perpustakaan Silaban, Bintang Jelita Nasrani; Zalukhu, Indri Feni Asih; Wijaya, Andri
Journal Of Informatics And Busisnes Vol. 3 No. 3 (2025): Oktober - Desember
Publisher : CV. ITTC INDONESIA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jibs.v3i3.3837

Abstract

The current library environment is experiencing rapid data collection, particulary from the daily activity of borrowing and returning books. This vast amount of data could potentially be utilized for analysis, but in practice, library information systems are often used solely for operational purposes. Consequently, opportunities to extract valuable insights from this data are limited. Given the situation, this study attempts to design and implement a data warehouse focused on analyzing book borrowing and returning patterns, utilizing PostgreSQL as the primary platform. The research process involved several stages, starting with data collection, needs analysis, data warehouse model design using the star schema, and implementation into PostgreSQL. Afterward, an ETL (Extraction, Transformation, and Loading) process was performed to able to combine library transaction data into a more structured and ready for analysis. From this data, the system was able to generate various insights, such as patterns of books that were borrowed most, medim, and least.
Penerapan Data Mining Untuk Klasterisasi Buku Di Perpustakaan Menggunakan Algoritma K-Means Zalukhu, Indri Feni Asih; Silaban, Bintang Jelita Nasrani; Wijaya, Andri
Journal Of Informatics And Busisnes Vol. 3 No. 3 (2025): Oktober - Desember
Publisher : CV. ITTC INDONESIA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jibs.v3i3.3904

Abstract

Libraries expand their book collections every year, making managing and organizing shelves increasingly challenging. This makes finding and grouping relevant books quite time – consuming, especially when the data is already quite large. Therfore, this study attemps to utilize data mining methods, specifically the K-Means algorithm, to help group books based on certain similarities, such as category and borrowing. Before the grouping process is carried out, the book data first goes through preprocessing and normalization stages to make data look neat and ready to be processed. Furthermore, the K-Means algorithm is used to generate several groups of books with similar characteristics. From the data processing results, K-Means has been proven to be able to form several fairly clear clusters, this sifnificantly assisting libraries in organizing books, providing reading recommendations, and improving the quality of service for students and lecturers. Overall, the implementations of the K-Means algorithm in this library can accelerate collection management work and support a more data – driven decision – making process.
Co-Authors Andri Wijaya Silaban, Bintang Jelita Nasrani