Klasifikasi Abstrak Tugas Akhir Mahasiswa Berbasis Web dengan Algoritma K-Nearest Neighbor - Moch. Anang Ardiansyah; Raka Tegar Wicaksono; Rino Raihan Gumilang; Solikhul Mauludin; Muhammad Hamdan Fuadi; Ersha Aisyah Elfaiz (2026)
Abstrak - Penelitian ini mengembangkan sistem klasifikasi abstrak tugas akhir mahasiswa Program Studi Pendidikan Teknologi Informasi (PTI) berbasis web menggunakan algoritma K-Nearest Neighbor (KNN). Data dikumpulkan melalui web scraping dari jurnal IT-Edu pada rentang tahun 2017–2025, kemudian melalui tahap preprocessing meliputi case folding, tokenizing, stopword removal, dan stemming sebelum dilakukan ekstraksi fitur menggunakan TF-IDF dan pengukuran kemiripan dengan cosine similarity. Dataset berjumlah 312 abstrak dibagi menjadi 249 data latih dan 63 data uji. Evaluasi performa dilakukan menggunakan beberapa nilai K dengan metrik accuracy, precision, recall, dan F1-score. Hasil penelitian menunjukkan bahwa nilai K= 17 memberikan kinerja terbaik dengan F-Measures sebesar 0.539. Sistem berbasis web yang dihasilkan mampu melakukan klasifikasi otomatis abstrak tugas akhir ke dalam kategori Rekayasa Perangkat Lunak (RPL) dan Teknik Komputer dan Jaringan (TKJ), sehingga dapat mendukung pengelolaan repositori akademik secara lebih efisien dan objektif.Kata kunci: K-Nearest Neighbor; TF-IDF; Cosine Similarity; Klasifikasi Dokumen; Sistem Berbasis Web; Abstract - This study develops a web-based classification system for undergraduate thesis abstracts in the Information Technology Education (PTI) program using the K-Nearest Neighbor (KNN) algorithm. Data were collected through web scraping from the IT-Edu journal (2017–2025) and preprocessed through case folding, tokenizing, stopword removal, and stemming prior to feature extraction using TF-IDF and similarity measurement with cosine similarity. A total of 312 abstracts were obtained and divided into 249 training data and 63 testing data. System performance was evaluated using several K values and measured with accuracy, precision, recall, and F1-score metrics. The results indicate that K = 17 provides the best performance with an F-measure of 0.539. The developed web-based system can automatically classify thesis abstracts into Software Engineering (RPL) and Computer and Network Engineering (TKJ), supporting more efficient and objective management of academic repositories.Keywords: K-Nearest Neighbor; TF-IDF; Cosine Similarity; Text Classification; Web-based System;