Implemetasi TF-IDF N-Gram dan Algoritma Nearest Centroid untuk Klasifikasi Topik Tugas Akhir

Rohima Choirul Hana; Defri Kurniawan

doi:10.47065/bits.v7i3.8859

Rohima Choirul Hana Universitas Dian Nuswantoro, Semarang, Indonesia
Defri Kurniawan * Universitas Dian Nuswantoro, Semarang, Indonesia

(*) Corresponding Author

DOI: https://doi.org/10.47065/bits.v7i3.8859

Keywords: TF-IDF; Nearest Centroid; Thesis Title Classification; Cosine Similarity; Streamlit

Abstract

This study presents a lightweight and explainable workflow for curating undergraduate thesis titles in the Informatics Engineering Study Program by combining TF-IDF n-gram (1–2) features with a cosine based Nearest Centroid classifier. Titles are grouped into three internal research area classes, RPLD, SC, and SKKKD, to support topic grouping and supervisor assignment. The approach is implemented as a Streamlit web application that supports Excel upload with preview and persistent saving, column standardization, text normalization, duplicate rejection using normalized titles, rapid training on labeled data, topic prediction for new titles, and retrieval of the most similar titles to assist curation. A key operational contribution is the direct linkage from predicted classes to the program maintained lecturer list for each area, enabling students to identify suitable supervisors and helping coordinators run a consistent and auditable workflow. On a multi semester corpus of 1,057 titles, stratified 5-fold cross-validation achieved 92.43 percent average accuracy, Macro F1 of 0.875, Micro F1 of 0.924, and Weighted F1 of 0.925, indicating a balance between accuracy, efficiency, and interpretability for short text. Decision inspection is supported by class specific top terms and nearest neighbor title lists. Limitations mainly stem from the minority class, therefore future work will expand labeled corpora, add character level n grams, and explore lightweight hybrid representations.

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