Meningkatkan Deteksi Ujaran Kebencian dan Bahasa

KLASIFIKASI SENTIMEN MASYARAKAT TERHADAP AKSI 17+8 DI MEDIA SOSIAL MENGGUNAKAN LSTM DAN BERT

This study aims to compare the performance of Long Short-Term Memory (LSTM) and Bidirectional Encoder Representations from Transformers (BERT) models in classifying public sentiment on platform X (Twitter) regarding the "17+8 Tuntutan Rakyat" movement. The dataset consists of 1,000 Indonesian tweets collected between A...

Flauschgummi at GermEval 2025 Shared Task on Candy Speech Detection: Sentiment Analysis and Classification of Online Comments

This work examines the effectiveness of stacking traditional machine learning techniques like Logistic Regression, k-Nearest Neighbors, Random Forest, Multi-Layer Perceptron, Support Vector Classifier and simple word counting and compares them to BERT, a powerful pre-trained language model.

Analisis Sentimen Masyarakat Terhadap Program Makan Bergizi Gratis (MBG) di Jawa Tengah Menggunakan Model Bidirectional Encoder Representations From Transformers For Indonesian (Indobert)

This study analyzes public sentiment toward the Free Nutritious Meal (Makan Bergizi Gratis/MBG) program in Central Java using data from the social media platform X. Data were collected automatically with Python and Selenium WebDriver, yielding 2,000 tweets containing username, date, location, and comment text. The comm...

Perbandingan Algoritma Support Vector Machine dan Random Forest untuk Analisis Sentimen terkait Makan Bergizi Gratis (MBG)

Advances in digital technology have made social media the primary platform for the public to voice their opinions on government policies, including the Free Nutritious Meals (MBG) program. This study aims to compare the performance of the Support Vector Machine (SVM) and Random Forest algorithms in conducting sentiment...

KLASIFIKASI SENTIMEN HEADLINE BERITA PASAR FINANSIAL CNBC INDONESIA MENGGUNAKAN MULTINOMIAL LOGISTIC REGRESSION DAN TF-IDF

Klasifikasi Sentimen Ulasan Pengguna Aplikasi Alfagift Menggunakan Metode Support Vector Machine Berbasis TF-IDF

This study aims to classify the sentiment of Alfagift user reviews into three classes: Positive, Negative, and Neutral, using the Support Vector Machine (SVM) method with Term Frequency-Inverse Document Frequency (TF-IDF) feature representation.