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All Journal Paradigma Jurnal Informatika: Jurnal Pengembangan IT
Muthmainnah, Aindri Rizky
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Computer Science & IT

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Perbandingan Cosine Similarity dan Weighted Jaccard Similarity dalam Pengembangan Mesin Pencari Perpustakaan Digital Pamput, Jessicha Putrianingsih; Muthmainnah, Aindri Rizky; Surianto, Dewi Fatmarani; Fadilah, Nur
Jurnal Informatika: Jurnal Pengembangan IT Vol 10, No 4 (2025)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v10i4.8773

Abstract

This study addressed the problem of low relevance in search results within the digital library system of the Department of Informatics and Computer Engineering (JTIK), Universitas Negeri Makassar. The purpose of this research was to improve the accuracy and relevance of search outcomes, enabling users, particularly students, to access academic materials and research references more efficiently. A search engine system was developed using a term-weighting method based on term frequency and document distribution. The system incorporated similarity measurement techniques to evaluate the degree of match between user queries and document content. An experimental approach was applied, which involved observation, data collection, text preprocessing, implementation of term weighting, and the comparison of cosine similarity and Weighted Jaccard similarity for ranking search results. The The evaluation was conducted using the Precision@K metric and a paired t-test to measure the significance of performance differences between methods. The test results showed that Weighted Jaccard obtained an average Precision@K value of 0.933, slightly higher than Cosine Similarity with an average of 0.9. However, Cosine Similarity produced a higher average similarity value. In addition, system testing was conducted in two stages, namely assessing user satisfaction with search results and assessing system performance. These findings confirmed that the combination of term-weighting and cosine similarity effectively enhanced the relevance and performance of digital library search systems.
K-Means++ and TF-IDF for Grouping Library Books by Topic Pamput, Jessicha Putrianingsih; Muthmainnah, Aindri Rizky; Risal, Andi Akram Nur; Surianto, Dewi Fatmarani
Paradigma - Jurnal Komputer dan Informatika Vol. 27 No. 2 (2025): September 2025 Period
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/p.v27i2.8272

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.
Co-Authors Andi Akram Nur Risal Dewi Fatmarani Surianto NUR FADILAH Pamput, Jessicha Putrianingsih