K-Means++ and TF-IDF for Grouping Library Books by Topic

Jessicha Putrianingsih  Pamput; Aindri Rizky Muthmainnah; Andi Akram Nur Risal; Dewi Fatmarani Surianto

doi:10.31294/p.v27i2.8272

Authors

Jessicha Putrianingsih Pamput State University of Makassar Author
Aindri Rizky Muthmainnah State University of Makassar Author
Andi Akram Nur Risal State University of Makassar Author
Dewi Fatmarani Surianto State University of Makassar Author

DOI:

https://doi.org/10.31294/p.v27i2.8272

Keywords:

Cluster, K-Means , Library, TF-IDF, Silhoutte Coefficient

Abstract

The grouping of library materials in the Department of Informatics and Computer Engineering (JTIK) at Universitas Negeri Makassar (UNM) is still conducted using a conventional system that relies on predefined categories and librarian intuition. This approach often leads to inconsistencies in book categorization, making it difficult for users to find relevant references efficiently. To address this issue, this research applies the K-Means++ clustering method, which optimizes centroid initialization for more accurate cluster formation. Books are grouped based on the TF-IDF weighting matrix, resulting in six distinct clusters characterized by unique centroid values. Analysis of the top 10 words per cluster highlights dominant topics within each group. The clustering quality was evaluated using the Silhouette Coefficient, with the highest value of 0.04299, indicating a well-separated cluster structure. These findings demonstrate that K-Means++ effectively organizes books based on word similarity, enhancing library material management and improving information retrieval in the JTIK library.

Downloads

Download data is not yet available.

References

Aditomo Mahardika Putra, Rio, Dian Pratiwi, Galuh Pramita, and Fajar Dewantoro. 2023. “Implementasi Perpustakaan Digital Di SMK Negeri 1 Trimurjo, Kabupaten Lampung Tengah.” JEIT-CS 1(3):180–86. doi: 10.33365/jeit-cs.v1i3.230.

Aggarwal, Charu C., and Chandan K. Reddy. 2018. Data Clustering Data Clustering Algorithms and Applications Chapman & Hall/CRC Data Mining and Knowledge Discovery Series Chapman & Hall/CRC Data Mining and Knowledge Discovery Series. Taylor & Francis Group.

Anggi Riyanto, Alfathan, Daryanto Daryanto, and Ginanjar Abdurrahman. 2022. “Text Mining Untuk Clustering Buku Di Perpustakaan Menggunakan Metode K-Means.” National Multidisciplinary Sciences 1(6):835–45. doi: 10.32528/nms.v1i6.239.

Anggraeni, Diah Bekti, Widyastuti Widyastuti, Fitri Puji Rahmawati, and Madya Giri Aditama. 2021. “Pengembangan Sistem Klasifikasi Kepustakaan Dengan Dewey Decimal Classification (DDC).” Buletin KKN Pendidikan 3(2):152–60. doi: 10.23917/bkkndik.v3i2.15734.

Anggraini, Tripani, Solihah Titin Sumantri, and Jamil. Khoirul. 2024. “Implementasi Pengklasifikasian Dan Penataan Bahan Pustaka Di Perpustakaan Sekolah Menengah Pertama It Al-Hijrah 2 Kecamatan Percut Sei Tuan Kabupaten Deli.” AL-IMAN: Jurnal Keislaman Dan Kemasyarakatan 8(2):491–516.

Apriliyani, Meli, Mirza Izzal Musyaffaq, Siti Nur’Aini, Maya Rini Handayani, and Khotibul Umam. 2024. “Implementasi Analisis Sentimen Pada Ulasan Aplikasi Duolingo Di Google Playstore Menggunakan Algoritma Naïve Bayes.” AITI 21(2):298–311. doi: 10.24246/aiti.v21i2.298-311.

Bashir, Abubakar Salisu, Abdulkadir Abubakar Bichi, and Alhassan Adamu. 2024. “Automatic Construction of Generic Hausa Language Stop Words List Using Term Frequency-Inverse Document Frequency.” Journal of Electrical Systems and Information Technology 11(1):58. doi: 10.1186/s43067-024-00187-5.

Daoudi, Sara, Chakib Mustapha Anouar Zouaoui, Miloud Chikr El-Mezouar, and Nasreddine Taleb. 2021. “Parallelization of the K-Means++ Clustering Algorithm.” Ingenierie Des Systemes d’Information 26(1):59–66. doi: 10.18280/isi.260106.

Dea Mustika, Rizky, and Ahmad Zakir. 2022. Jurnal Media Informatika [JUMIN] Implementasi Algoritma K-Means Untuk Clustering Judul Skripsi Universitas Harapan Medan.

Dea Mustika, Rizky, Ahmad Zakir, and Alkhowa Rizmi. 2022. “Implementasi Algoritma K-Means Untuk Clustering Judul Skripsi Universitas Harapan Medan.” Jurnal Media Informatika 4(1):40–47. doi: 10.55338/jumin.v4i1.405.

Du, Guoyu, Xuehua Li, Lanjie Zhang, Libo Liu, and Chaohua Zhao. 2021. “Novel Automated K-Means++ Algorithm for Financial Data Sets.” Mathematical Problems in Engineering 2021:1–12. doi: 10.1155/2021/5521119.

Firmansyah, Taufik, Poningsih, and Sundari Retno Andani. 2022. “Analisis Clustering Algoritma K-Means Sebagai Rekomendasi Penambahan Koleksi Buku Di Perpustakaan Madrasah Tsanawiyah Negeri 2 Simalungun.” ZAHRA: Bulletin Big Data, Data Science, and Artificial Intelligence 1(1).

Fransiska, Andien. 2023. “Penataan Koleksi Bahan Pustaka Di Perpustakaan Politeknik Sriwijaya Sebagai Upaya Mempermudah Menemukan Buku Yang Diperlukan Oleh Pemustaka.” Jurnal Multidisipliner Bharasumba 2(3).

Haryani, Dicku Nofriansyah, and Ita Mariami. 2021. “Implementasi Data Mining Untuk Pengelempokan Buku Di Perpustakaan Yayasan Nurul Islam Indonesia Baru Dengan Metode K-Means Clustering.” Jurnal CyberTech 1(1):1–12.

Hasanah, Nisriina Nuur, and Agus Sidiq Purnomo. 2022. “Implementasi Data Mining Untuk Pengelompokan Buku Menggunakan Algoritma K-Means Clustering (Studi Kasus : Perpustakaan Politeknik LPP Yogyakarta).” Jurnal Teknologi Dan Sistem Informasi Bisnis 4(2):300–311. doi: 10.47233/jteksis.v4i2.499.

Kenger, Omer N., Zulal Diri Kenger, Eren Ozceylan, and Beata Mrugalska. 2023. “Clustering of Cities Based on Their Smart Performances: A Comparative Approach of Fuzzy C-Means, K-Means, and K-Medoids.” IEEE Access 11:134446–59. doi: 10.1109/ACCESS.2023.3333753.

Lan, Fei. 2022. “Research on Text Similarity Measurement Hybrid Algorithm with Term Semantic Information and TF-IDF Method.” Advances in Multimedia 2022:1–11. doi: 10.1155/2022/7923262.

Manning, Christopher D., Prabhakar Raghavan, and Hinrich Schütze. 2008. Introduction to Information Retrieval. Cambridge: Cambridge University Press.

Nasir, Januardi. 2021. “Penerapan Data Mining Clustering Dalam Mengelompokan Buku Dengan Metode K-Means.” Simetris: Jurnal Teknik Mesin, Elektro Dan Ilmu Komputer 11(2):690–703. doi: 10.24176/simet.v11i2.5482.

Nur Afifah, Inas Ajeng, and Heri Nurdiyanto. 2023. “Data Mining Clustering Dalam Pengelompokan Buku Perpustakaan Mengunakan Algoritma K-Means.” JIPI (Jurnal Ilmiah Penelitian Dan Pembelajaran Informatika) 8(3):802–14. doi: 10.29100/jipi.v8i3.3891.

Pamput, Jessicha Putrianingsih, Salsa Dillah, Aindri Rizky Muthmainnah, and Dewi Fatmarani Surianto. 2024. “Analysis of Fuzzy C-Means In Personality Clustering Based On The Ocean Model.” JIKO (Jurnal Informatika Dan Komputer) 7(3):158–64. doi: 10.33387/jiko.v7i3.8369.

Siburian, Daud, Sundari Retno Andani, Ika Purnama Sari, and Genesis Artikel. 2022. “Implementasi Algoritma K-Means Untuk Pengelompokkan Peminjaman Buku Pada Perpustakaan Sekolah Implementation of K-Means Algorithm for Clustering Books Borrowing in School Libraries.” JOMLAI: Journal of Machine Learning and Artificial Intelligence 1(2):2828–9099. doi: 10.55123/jomlai.v1i2.725.

Widaningrum, Ida, Dyah Mustikasari, Rizal Arifin, Siti Lathifah Tsaqila, and Dwiyunia Fatmawati. 2022. “Algoritma Term Frequency-Inverse Document Frequency (TF-IDF) Dan K-Means Clustering Untuk Menentukan Kategori Dokumen.” SISFOTEK: Sistem Informasi Dan Teknologi.

Xu, Xiaobo, and Jin Shang. 2024. “Research on the Construction Scheme of Smart Library Based on Blockchain Technology.” Measurement: Sensors 31. doi: 10.1016/j.measen.2023.100943.

Zhao, Huiling. 2022. “Design and Implementation of an Improved K-Means Clustering Algorithm.” Mobile Information Systems 2022:1–10. doi: 10.1155/2022/6041484.