Albiyona, Rofiq Nur (2025) SISTEM IDENTIFIKASI EMOSI DALAM TEKS MENGGUNAKAN ALGORITMA SUPPORT VECTOR MACHINE (SVM). S1 thesis, UNIVERSITAS PGRI MADIUN.
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Abstract
Emotion is a fundamental aspect of human communication that influences thinking, behavior, and interaction. In the digital era, the increasing frequency of text-based interactions on social media and online platforms creates a need for automated systems that can accurately recognize emotions. This study aims to develop a text based emotion identification system using the Support Vector Machine (SVM) algorithm. The system is implemented as an interactive web application built with Python and the Streamlit framework, using a labeled emotion dataset from Kaggle that has been translated into Bahasa Indonesia. The data processing involves several stages: preprocessing (lowercasing, stopword removal, and stemming), feature extraction using the TF-IDF method, and emotion classification with SVM. The model evaluation is conducted using precision, recall, F1-score, and confusion matrix metrics, while system testing applies the System Usability Scale (SUS) method involving 10 respondents. The results show that the system can identify emotions in text with an accuracy of 68% and achieved an average SUS score of 81, indicating that the system’s usability is categorized as "good." This research contributes to the development of adaptive and practical text-based emotion classification systems with potential applications in customer service, chatbots, and public opinion analysis.
| Item Type: | Thesis/Skripsi/Tugas Akhir (S1) |
|---|---|
| Kata Kunci: | Emotion; Text Classification; Support Vector Machine (SVM); TF-IDF; Preprocessing; System Usability Scale (SUS); Streamlit; Natural Language Processing (NLP). |
| Subjects: | Q Science > QA Mathematics > QA76 Computer software |
| Divisions: | Fakultas Teknik > Teknik Informatika |
| Depositing User: | ALBIYONA NUR ROFIQ |
| Date Deposited: | 25 Aug 2025 05:48 |
| Last Modified: | 25 Aug 2025 05:48 |
| URI: | http://eprint.unipma.ac.id/id/eprint/4593 |
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