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.

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.