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All Journal JSAI (Journal Scientific and Applied Informatics)
Siska Mailana
Fakultas Teknik dan Informatika, Universitas Dian Nusantara, Indonesia
Computer Science & IT

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Klasifikasi Teks Umpan Balik Kompetensi Kepribadian di Perguruan Tinggi Menggunakan Ekstraksi Fitur TF-IDF dan Algoritma Logistic Regression Vina Ayumi; Mariana Purba; Siska Mailana
JSAI (Journal Scientific and Applied Informatics) Vol 8 No 2 (2025): Juni
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v8i2.8764

Abstract

This study aimed to develop and evaluate a text classification model to identify sentiment in feedback on lecturers’ personality competencies at a university using TF-IDF feature extraction and Logistic Regression (LR) algorithms. The data originated from student evaluations of lecturers’ personality competencies at Universitas Sjakhyakirti, consisting of a total of 6,112 texts labeled as positive sentiment (3,700) and negative sentiment (2,412). The dataset was then divided into three parts: training (70%), validation (10%), and testing (20%). The research stages included text preprocessing, which involved data cleaning, letter normalization, and the removal of common words, followed by term weighting using the TF-IDF method and classification using the LR model to categorize texts as positive or negative sentiment. The model was evaluated using accuracy, precision, recall metrics, and a confusion matrix. Experimental results showed that at the 50th epoch, the model achieved a training accuracy of 81.90% and a validation accuracy of 78.30%, while on the testing data, the TF-IDF-LR model reached an accuracy of 75.1%.
Co-Authors Mariana Purba Vina Ayumi