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All Journal International Journal of Engineering, Science and Information Technology Jurnal Ilmiah Teknik Mesin, Elektro dan Komputer (JURITEK) Jurnal Ilmu Komputer Journal of International Multidisciplinary Research
Handayani, Tri Pratiwi
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Comparative Analysis of CNN-RNN Models for Hatespeech Detection Incorporating L2 regularization Handayani, Tri Pratiwi; Hasyim, Wahyudin
International Journal of Engineering, Science and Information Technology Vol 4, No 1 (2024)
Publisher : Department of Information Technology, Universitas Malikussaleh, Aceh Utara, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v4i1.491

Abstract

This study aims to address the challenge of detecting hate speech in text data by comparing two experimental CNN-RNN models. The primary issue is achieving a balance between precision and recall in hate speech detection while preventing overfitting and ensuring good generalization. Two different approaches were applied: the first model used standard training techniques, while the second model incorporated L2 regularization and early stopping. The research involved using Keras Tokenizer for text tokenization, layering with CNN and LSTM for feature extraction and temporal context capturing, and applying dropout to prevent overfitting. L2 regularization and early stopping were added to the second model to enhance generalization. The findings reveal that the first model, although exhibiting some overfitting, attained a higher overall accuracy of 78% and more balanced F1-scores for both the "Not Hate Speech" and "Hate Speech" categories. The second model, although achieving higher precision for hate speech (0.81), had lower recall (0.58), resulting in an overall accuracy of 75%. This suggests that regularization and early stopping need careful tuning to avoid reducing sensitivity to hate speech detection.
Preliminary Evaluation of Gaussian Naive Bayes for Multi-Label Hate Speech and Abusive Language Detection on Indonesian Twitter Handayani, Tri Pratiwi; Hasyim, Wahyudin; Wati, Nursetia
Journal of International Multidisciplinary Research Vol. 1 No. 1 (2023): November 2023
Publisher : PT. Banjarese Pacific Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62504/jimr532

Abstract

Automatic detection of hate speech and abusive language is crucial for combating online toxicity. This study explores Gaussian Naive Bayes for multi-label classification of hate speech on Indonesian Twitter, including target, category, and level. We combined TF-IDF features with contextual BERT embeddings. The model achieved balanced performance for general hate speech and good non-abusive language detection. However, it exhibited limitations with imbalanced data and specific hate speech types. The classifier consistently favored the majority class (non-hateful/non-abusive) across labels, particularly struggling with HS_Gender, HS_Physical, etc. This suggests difficulty detecting less frequent but potentially severe hate speech, likely due to limited training data. Overall accuracy and F1-scores confirm that while Gaussian Naive Bayes is efficient, it lacks robustness for nuanced multi-label classification with imbalanced datasets. This necessitates exploring alternative approaches for effectively detecting specific and less frequent hate speech.
SISTEM INFORMASI PENCARIAN JASA TUKANG BANGUNAN Polapa, Risman; Abas, Mohamad Ilyas; Handayani, Tri Pratiwi; Lasarudin, Alter
Jurnal Ilmu Komputer (JUIK) Vol 4, No 3 (2024): October 2024
Publisher : Universitas Muhammadiyah Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31314/juik.v4i3.3477

Abstract

This research aims to design a Web-based Building Services Search Information System and implement a Web-Based Building Builder Services Search Information System. This research was developed using the Rapid Application Development (RAD) method. In RAD, users and analysts participate in three stages. The three stages are implementation, RAD design and requirements planning. With these stages, the application of the RAD method is very appropriate and suitable for developing website-based systems. The results of this research were that the researcher had previously conducted an interview with one of the head builders. Craftsmen in their profession as builders are to get work that suits their skills, only certain people know and understand the workman's performance, what field the craftsman is skilled in, so if there is a special job that must be done by a craftsman that suits his skills. So the employer must meet the craftsman directly and ask about the craftsman's skills. Conclusion: We have succeeded in designing a web-based information system for searching for construction services, able to make it easier for customers (employers) to search for construction services according to the skills required by customers (employers), as well as making it easier for builders to find work that suits the craftsman's skills. With this information system, it can help builders and customers (employers) in the process of searching for builders according to their expertise in the form of the Web or other web browsers.
PENERAPAN METODE MULTI OBJECTIVE OPTIMIZATION ON THE BASIC OF RATIO ANALYSIS (MOORA) UNTUK PEMILIHAN PENERIMA BANTUAN LANGSUNG TUNAI DI DESA ILOMANGGA Handayani, Tri Pratiwi; Pratiwi I Wantu; Irawan Ibrahim; Hilmansyah Gani
Jurnal Ilmiah Teknik Mesin, Elektro dan Komputer Vol. 3 No. 2 (2023): Juli: Jurnal Ilmiah Teknik Mesin, Elektro dan Komputer
Publisher : Pusat Riset dan Inovasi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/juritek.v3i2.1724

Abstract

Penelitian ini bertujuan untuk mengimplementasikan algoritma Multi-Objective Optimization on the Basis of Ratio Analysis (MOORA) sebagai Pendukung Keputusan dalam memilih penerima Bantuan Tunai Langsung di Desa Ilomangga, Gorontalo. Dengan dataset sebanyak 169 calon penerima, penelitian ini berfokus pada pengembangan pendekatan yang efisien untuk membantu kepala desa dalam proses pemilihan penerima manfaat. Dengan menggabungkan optimisasi multi-obyektif dan analisis rasio, algoritma MOORA secara objektif mengevaluasi dan mengurutkan penerima berdasarkan kelayakan dan kesesuaian. Temuan penelitian ini menunjukkan efektivitas MOORA dalam menyederhanakan proses seleksi, memastikan transparansi, dan mengoptimalkan alokasi sumber daya bagi mereka yang paling membutuhkan. Penelitian ini memberikan kontribusi pada sistem pendukung keputusan dengan memperlihatkan implementasi praktis MOORA.
Enhancing Multi-Label Hate Speech and Abusive Language Detection on Indonesian Twitter Using Recurrent Neural Networks with Hyperparameter Tuning Handayani, Tri Pratiwi; Hilmansyah Gani
Jurnal Ilmiah Teknik Mesin, Elektro dan Komputer Vol. 3 No. 3 (2023): November: Jurnal Ilmiah Teknik Mesin, Elektro dan Komputer
Publisher : Pusat Riset dan Inovasi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/juritek.v3i3.3022

Abstract

This study investigates enhancing multi-label hate speech and abusive language detection on Indonesian Twitter using Recurrent Neural Networks (RNNs) with hyperparameter tuning. A dataset of Indonesian tweets labeled for various hate speech and abusive language categories was preprocessed through text cleaning, tokenization, and sequence padding. A baseline RNN model was initially constructed and evaluated. Hyperparameter tuning was then performed using Keras Tuner to optimize performance. The best hyperparameters identified were an embedding dimension of 32, 32 LSTM units, and a dropout rate of 0.2. The tuned model was trained and compared with the baseline. Results indicated improved precision for labels like Abusive, HS_Group, HS_Moderate, and HS_Strong, but a decline in recall and F1-scores for labels like HS_Religion and HS_Race. Overall performance metrics showed a slight decline, highlighting trade-offs in the tuning process. In conclusion, while hyperparameter tuning can enhance certain performance aspects, it also introduces complexities and trade-offs. It is recommended to use hyperparameter tuning in model optimization with careful consideration of application requirements. Further research will explore different model architectures and additional tuning strategies for better overall performance.
Comparative Analysis of CNN-LSTM and LSTM Models for Cyberbullying Detection with Increasing Dataset Sizes Handayani, Tri Pratiwi; Abas, Mohamad Ilyas
Jurnal Ilmu Komputer (JUIK) Vol 4, No 2 (2024): JUNE 2024
Publisher : Universitas Muhammadiyah Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31314/juik.v4i2.3185

Abstract

This study compares the performance of two deep learning models, CNN-LSTM and LSTM, for identifying cyberbullying in social media text. Three distinct dataset sizes are used for our evaluation and comparison: 1,000, 5,000, and 10,000 samples. The results indicate that the CNN-LSTM model outperforms the LSTM-only model (Ablation model) for the largest dataset size, exhibiting substantial enhancements in accuracy, precision, recall, and F1-Score as the dataset size increases. The Ablation model exhibits competitive performance and slightly superior results on the mid-sized dataset. However, it inevitably falls behind the CNN-LSTM model when trained on 10,000 samples. These findings imply that increasing the complexity of the CNN layer in the CNN-LSTM model improves its ability to collect significant features in bigger datasets, making it more successful for cyberbullying detection. 
Co-Authors Gani, Hilmansyah Irawan Ibrahim Lasarudin, Alter Mohamad Ilyas Abas Nursetiawati Nursetiawati Polapa, Risman Pratiwi I Wantu Wahyudin Hasyim