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Rochman, Eka Mala Sari
Program Studi Teknik Informatika, Fakultas Teknik, Universitas Trunojoyo
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Physics

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Klasifikasi Emosi Pada Teks Berbahasa Indonesia Dengan Attention-Based BiLSTM Purwanti, Endah; Namara Akbar, Gamas; Mala Sari Rochman, Eka
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 13 No 3: Juni 2026
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2026133

Abstract

Media sosial telah menjadi bagian integral dari komunikasi digital modern yang memungkinkan pengguna berbagi informasi dan mengekspresikan berbagai emosi melalui konten digital. Pemahaman emosi pengguna menjadi penting karena berperan dalam mendukung pengambilan keputusan di berbagai domain, seperti analisis respons konsumen, pengukuran sentimen publik, serta peningkatan kualitas dan personalisasi pengalaman pengguna pada layanan digital. Namun, klasifikasi emosi pada teks berbahasa Indonesia masih menjadi tantangan karena karakteristik linguistik dan kompleksitas ekspresi emosional yang tinggi. Penelitian ini bertujuan untuk mengembangkan dan mengevaluasi model attention-based Bidirectional Long Short-Term Memory (BiLSTM) guna meningkatkan pemahaman kontekstual dan menangkap nuansa emosional dalam klasifikasi emosi teks berbahasa Indonesia. Dataset yang digunakan terdiri dari 7.080 tweet berbahasa Indonesia yang tersedia secara publik. Tahapan preprocessing meliputi tokenisasi, normalisasi, serta penghapusan stopword. Model yang diusulkan dibandingkan dengan dua model pembanding, yaitu BiLSTM tanpa attention dan attention-based LSTM. Hasil penelitian menunjukkan bahwa attention-based BiLSTM mencapai akurasi sebesar 72,5% dan macro F1-score sebesar 73,8%. Kinerja ini menunjukkan peningkatan performa hingga 3,54% pada akurasi dan 3,03% pada macro F1-score. Performa terbaik diperoleh pada kelas fear (F1 = 0,80) dan love (F1 = 0,76), sementara kelas neutral masih menunjukkan kinerja yang relatif lebih rendah (F1 = 0,62). Secara keseluruhan, penelitian ini menyimpulkan bahwa attention-based BiLSTM merupakan pendekatan yang efektif untuk klasifikasi emosi pada teks media sosial berbahasa Indonesia. Integrasi attention mechanism pada arsitektur BiLSTM terbukti meningkatkan kemampuan ekstraksi fitur kontekstual dan akurasi klasifikasi lintas kelas emosi.   Abstract   Social media has become integral to modern digital communication, enabling users to share information and express a range of emotions through digital content. Understanding user emotions is increasingly important because it supports decision-making across multiple domains, including consumer response analysis, public sentiment measurement, and the enhancement and personalization of user experiences in digital services. However, emotion classification in Indonesian-language text remains challenging due to linguistic characteristics and the complexity of emotional expressions. This study aims to develop and evaluate an attention-based Bidirectional Long Short-Term Memory (BiLSTM) model to improve contextual understanding and capture emotional nuances in emotion classification for Indonesian text. The dataset consists of 7,080 publicly available Indonesian tweets. Preprocessing includes tokenization, normalization, and stopword removal. The proposed model is compared with two baseline models: a BiLSTM without attention and an attention-based LSTM. The experimental results show that the attention-based BiLSTM achieves an accuracy of 72.5% and a macro F1-score of 73.8%, representing performance improvements of up to 3.54% in accuracy and 3.03% in macro F1-score. The best performance is observed in the fear class (F1 = 0.80) and the love class (F1 = 0.76), while the neutral class still exhibits relatively lower performance (F1 = 0.62). Overall, this study concludes that the attention-based BiLSTM is an effective approach for emotion classification in Indonesian social media text. The integration of an attention mechanism into the BiLSTM architecture has been shown to enhance contextual feature extraction and improve classification accuracy across emotion classes.
Improving RoBERTa Performance through Hyperparameter Optimization for Sentiment Analysis of Indonesian Tourism Reviews Imamah, Imamah; Thida, Myo; Rachman, Fika Hastarita; Satoto, Budi Dwi; Herawati, Sri; Kustiyahningsih, Yeni; Rochman, Eka Mala Sari; Zakiyah, Meita Lailatuz
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 3 (2026): JUTIF Volume 7, Number 3, June 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.3.5672

Abstract

The performance of transformer models such as RoBERTa in sentiment classification is influenced by hyperparameter settings, especially the epoch and batch sizes. However, no previous study has examined the impact of changes in the number of epochs and batch sizes on the performance of each class in classification tasks, especially in Indonesian-language sentiment analysis of tourism reviews. Therefore, this study aims to fill this gap by analyzing the performance of RoBERTa and the impact of various hyperparameter settings on sentiment for each class. The dataset consists of 3,875 reviews from visitors to Lake Sarangan on Google Maps. The batch sizes used in this study are 8 and 16, and the epoch range is 2 to 4. There are three classes of sentiment: negative, neutral, and positive. The results demonstrate that increasing the batch size from 8 to 16 does not linearly improve model performance. The optimal combination of epoch=4 and batch size=8 achieved 91% accuracy, with significant improvements in recall and F1-score across all classes, especially in positive sentiment classification. This research offers valuable insights into fine-tuning RoBERTa for sentiment analysis in Indonesian contexts, providing recommendations for future sentiment analysis tasks in natural language processing.
Co-Authors -, Husni Aeri Rachmad Alfa Faridh Suni Bain Khusnul Khotimah Budi Dwi Satoto Dyah S Rahayu Endah Purwanti Fika Hastarita Rachman, Fika Hastarita Firdaus Solihin Fitri Damayanti Husni - Huzain Azis Ifan Pratama Ika Oktavia Suzanti Imamah Imamah Imamah Imamah Kamil, Fajrul Ihsan Khozaimi, Ach. Mohammad Syarief, Mohammad Muhammad Syarief, Muhammad Nabil Dzul Afkar, Ahmad Namara Akbar, Gamas Pramudita, Yoga Dwitya Pratama, Ifan Putro, Sigit Susanto Ratna Nur Tiara Shanty Rima Tri Wahyuningrum Sri Herawati Syakur, Muhammad Ali Thida, Myo Yeni Kustiyahningsih Yudha Dwi Putra Negara, Yudha Zakiyah, Meita Lailatuz