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All Journal JOIN (Jurnal Online Informatika) PROCESSOR Jurnal Ilmiah Sistem Informasi, Teknologi Informasi dan Sistem Komputer Jurnal Tekno Insentif Jurnal Sistem Cerdas Jurnal Teknologi Terapan JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Tekinkom (Teknik Informasi dan Komputer) Jurnal Teknik Industri Terintegrasi (JUTIN) Competitive Jurnal Sains, Nalar, dan Aplikasi Teknologi Informasi The Indonesian Journal of Computer Science
Habibi, Roni
Unknown Affiliation
Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Mechanical Engineering Transportation

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Journal : Jurnal Teknologi Terapan

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DETEKSI EMOSI PADA TEKS BERBAHASA INDONESIA MENGGUNAKAN PENDEKATAN ENSEMBLE Pane, Syafrial Fachri; Abdullah, Faisal; Habibi, Roni
Jurnal Teknologi Terapan Vol 10, No 2 (2024): Jurnal Teknologi Terapan
Publisher : P3M Politeknik Negeri Indramayu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31884/jtt.v10i2.551

Abstract

Emotion in written text is often difficult to recognize due to the absence of visual cues such as facial expressions or vocal intonation, which typically aid in understanding a person's feelings. This research aims to address this challenge by developing an emotion detection model for Indonesian text. The approach used is Ensemble Learning, combining three Machine Learning models: SVM, KNN, and XGBoost, to optimize emotion detection results. The main contribution of this research is the implementation of the Ensemble method for detecting emotions in Indonesian text, with performance evaluated using metrics such as accuracy, precision, recall, F1 score, and ROC AUC. The evaluation results show that the Ensemble model outperforms previous models, achieving an accuracy, precision, recall, and F1 score of 87.14%, and a ROC AUC score of 97.90%. To further enhance performance, this study utilizes GridSearchCV for hyperparameter tuning of the SVM and XGBoost models and employs the Automated Machine Learning (AutoML) tool TPOT to generate the KNN model.
Co-Authors Andarsyah, Roni Annisah, Wulan Nur Atmojo, Ariyo Azzahra, Fathya Fathimah Darfial Guslan Dwiyanti, Zian Asti Ekra Sanggala Fadilla Musdalifa, Deriska Faisal Abdullah Fatonah, Rd. Nuraini Siti Febrisa Sidabutar, Audry Harani, Nisa Hanum Hayati, Kurnia Maulana, Jaka Nugraha, Fikri Aldi Rahayu, Woro Isti Rahmawan, Fadhel Rosa Sekamayang, Balqis Salsabila, Helmi Setyawan, M. Yusril Helmi Syafrial Fachri Pane, Syafrial Fachri Widana, Naufal Dekha