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All Journal Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Indonesian Journal of Education And Computer Science
Nurbeti Sinulingga
Universitas Pembangunan Panca Budi
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Engineering

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Penerapan Algoritma K-Means Clustering untuk Mengetahui Pola Peminjaman Buku di Perpustakaan Universitas Imelda Medan Nurbeti Sinulingga; Muhammad Irfan Sarif; Nuzul Aini Ramadhani; Karina Nurfebia; Febri Yalda Sulistia
Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Vol. 4 No. 3 (2026): Februari 2026
Publisher : LKP KARYA PRIMA KURSUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62712/juktisi.v4i3.707

Abstract

The use of a modern library management system is an important aspect in improving the quality of library services. The Imelda University Library in Medan has been using SLiMS 7 as its collection management and book borrowing transaction information system. This study aims to analyze borrowing data from SLiMS 7 and identify book borrowing patterns using the K-Means Clustering algorithm. The research data includes book categories, borrowing frequency, borrower study programs, and borrowing periods extracted directly from the SLiMS 7 database. The research stages include data pre-processing, normalization, determining the optimal number of clusters using the Elbow method, and the clustering process using the K-Means algorithm. The results of the study show the formation of several clusters that describe different borrowing patterns, such as clusters of collections with high, medium, and low borrowing rates. These findings can assist libraries in making decisions related to collection development, service organization, and book acquisition strategy planning. Thus, the integration of SLiMS 7 data with the K-Means algorithm has proven effective in generating information that supports the improvement of data-based library services.
Performa Multinomial Naive Bayes dalam Klasifikasi Misinformasi Megathrust pada Komentar TikTok Febri Yalda Sulistia; Karina Nurfebia; Nurbeti Sinulingga; Nuzul Aini Ramadhani; Khairul Khairul
Indonesian Journal of Education And Computer Science Vol. 4 No. 2 (2026): INDOTECH - August 2026
Publisher : PT. INOVASI TEKNOLOGI KOMPUTER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60076/indotech.v4i2.2090

Abstract

Penyebaran misinformasi terkait bencana megathrust di media sosial TikTok berpotensi memicu kepanikan publik dan mengganggu efektivitas komunikasi risiko bencana. Penelitian ini bertujuan menganalisis model klasifikasi informasi valid dan misinformasi terkait megathrust menggunakan pendekatan Natural Language Processing (NLP), algoritma Multinomial Naive Bayes, dan TF-IDF. Data penelitian berupa 2.000 komentar TikTok berbahasa Indonesia yang dikumpulkan secara purposive sampling dari konten relevan dan dibagi ke dalam dua kelas: informasi valid dan misinformasi. Preprocessing meliputi case folding, cleaning, tokenizing, stopword removal, dan stemming menggunakan library Sastrawi. Evaluasi pada 400 data uji menghasilkan akurasi 52%, macro-precision 50,24%, macro-recall 50,20%, dan macro-F1 48,93%. Akurasi tersebut lebih rendah daripada majority-class baseline 54,5%, sehingga model belum memberikan peningkatan dibanding prediksi sederhana yang selalu memilih kelas mayoritas. Kesalahan paling banyak terjadi pada komentar valid yang diprediksi sebagai misinformasi. Hasil ini menunjukkan bahwa variasi bahasa informal dan keterbatasan TF-IDF dalam menangkap konteks masih menjadi tantangan utama pada klasifikasi komentar TikTok
Co-Authors Febri Yalda Sulistia Karina Nurfebia Khairul Muhammad Irfan Sarif Nuzul Aini Ramadhani