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Mailana, Siska
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Computer Science & IT

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Klasifikasi Teks Umpan Balik Kompetensi Sosial Dosen di Perguruan Tinggi Menggunakan Word2Vec dan CNN-1D Ayumi, Vina; Purba, Mariana; Mailana, Siska
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.v8i2.8763

Abstract

The advancement of artificial intelligence technology supported the development of automatic sentiment classification. This study aimed to develop a deep learning model based on Word2Vec and one-dimensional Convolutional Neural Networks (CNN-1D) to classify the sentiment of textual feedback regarding lecturers’ social competence in higher education. The dataset consisted of 6,124 feedback texts collected from student questionnaires at Universitas Sjakhyakirti. The data were proportionally divided into 70% for training, 10% for validation, and 20% for testing. The developed Word2Vec-CNN-1D model demonstrated performance with a training accuracy of 85.10% and a validation accuracy of 79.10%. During the testing phase, the model achieved an accuracy of 76.2% in classifying the feedback texts into positive and negative classes. Evaluation metric analysis showed that for the positive class, the model attained a precision of 0.827, recall of 0.760, and F1-score of 0.792, while for the negative class, it obtained a precision of 0.679, recall of 0.761, and F1-score of 0.717. The results indicated that the Word2Vec and CNN-1D model was more effective at identifying positive sentiments, whereas the performance for the negative class could still be improved in the classification of textual feedback on lecturers’ social competence.
Klasifikasi Teks Umpan Balik Kompetensi Kepribadian di Perguruan Tinggi Menggunakan Ekstraksi Fitur TF-IDF dan Algoritma Logistic Regression Ayumi, Vina; Purba, Mariana; Mailana, Siska
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 Ayumi, Vina Purba, Mariana