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Wirdayanti
Universitas Tadulako
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Journal : Jurnal Algoritma

 1 
Analisis Sentimen Terhadap Kinerja Awal Pemerintahan Menggunakan IndoBERT Dan SMOTE Pada Media Sosial X Ihalauw, Sahron Angelina; Trezandy Lapatta, Nouval; Wiria Nugraha, Deny; Wirdayanti; Ar Lamasitudju, Chairunnisa
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.2957

Abstract

Social media platform X has become a key channel for expressing public opinion on political issues, including evaluating the early performance of the government. The first 100 days of an administration are a strategic period to assess policy direction and public perception. This study aims to apply and evaluate the IndoBERT model for sentiment analysis of Indonesian-language tweets discussing the 100-day performance of the Prabowo–Gibran administration, as well as to assess the impact of using the Synthetic Minority Oversampling Technique (SMOTE) to address data imbalance. A total of 15,027 tweets were collected through API crawling and processed through several stages: preprocessing, labeling using the InSet Lexicon, data splitting, and fine-tuning IndoBERT. Two scenarios were tested — without SMOTE and with SMOTE oversampling. The results show that both models achieved the same overall accuracy of 87%, but performance varied across sentiment classes. The model without SMOTE performed better in the positive class with 93% precision, whereas the SMOTE-applied model improved performance in the neutral class (F1-score increased from 70% to 71%; recall from 69% to 71%) and in the negative class (precision increased from 88% to 90%). Considering the balance across classes, the SMOTE-based model was selected as the final model and implemented into a Streamlit application for interactive sentiment analysis. This study expands the application of IndoBERT in the Indonesian political domain by combining the lexical InSet approach with SMOTE oversampling — a combination rarely applied in Indonesian political sentiment analysis. The findings highlight the importance of data balancing strategies in improving transformer-based model performance on imbalanced datasets. Future research is encouraged to explore alternative balancing methods, expand training data, and test other transformer variants to enhance accuracy and generalization.
Implementation of Long Short-Term Memory Algorithms on Cryptocurrency Price Prediction with High Accuracy on Volatile Assets Nursiana Zasqia, Andi Nirina; Laila, Rahmah; Trezandy Lapatta, Nouval; Yazdi Pusadan, Mohammad; Santi, Dessy; Wirdayanti
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.2422

Abstract

Cryptocurrencies have emerged as one of the most popular digital assets, characterized by high volatility, which presents a significant challenge in forecasting their price movements accurately. This study aims to implement the Long Short-Term Memory (LSTM) algorithm to predict the prices of selected cryptocurrencies, including Bitcoin (BTC), Binance Coin (BNB), Ethereum (ETH), Dogecoin (DOGE), Solana (SOL), and Shiba Inu (SHIB). The LSTM model is trained using the Adam optimizer and employs early stopping to mitigate overfitting. Model performance is evaluated using Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and the coefficient of determination (R²). The results indicate that the LSTM model achieves strong predictive accuracy for relatively low-volatility assets such as Dogecoin and Solana, with R² scores of 0.9795 and 0.9523, respectively. In contrast, its performance declines when applied to highly volatile assets like Bitcoin and Binance Coin. The findings also suggest that LSTM performs best in short-to-medium-term forecasts (7 to 30 days), but shows limitations in long-term predictions. This study contributes to the field by demonstrating the applicability of LSTM in financial forecasting and highlighting its strengths and constraints across different volatility profiles. Practically, the findings can assist traders and financial analysts in making data-driven decisions by applying LSTM models for more reliable short-term predictions, while emphasizing the need to integrate external market factors to enhance long-term forecast accuracy.
Co-Authors ., Rezki A. Masse, Burhannudin Abul Azis, Muhammad Dzaky Aisyah Warsid Aldy, Moh.Fajrin Sigit Amriana Amriana Amriana Amriana Andi Hendra Anita Ahmad Kasim ARDIANSYAH ARDIANSYAH Ardiansyah, R Ar Lamasitudju, Chairunnisa Asriani Asriani, Asriani Bakri Budiman, Wahyu Chairunnisa Ar. Lamasitudju Deni Irawan, Deni Deni Luvi Jayanto Deny Wiria Nugraha Dessy Santi Dwi Shinta Angreni Hajra Rasmita Ngemba Ihalauw, Sahron Angelina Indah Safitri Kade Dwi Arsana, I Gusti Ngurah Agung Laala, Jonathan Zebina Laila, Rahma Masykur, M Miftah Miftah Miftah, Miftah Mohammad Yazdi Pusadan Mufthi, Mufthi Muhammad Akbar MUHAMMAD BAKRI Nadhar, Muhammad Ngurah, Gusti Nirwan Nirwan Nouval Trezandy Lapatta Noviantika, Noviantika Nursalim, Moh. Agung Nursiana Zasqia, Andi Nirina Qofifa, Sitti Nurlaili Rafika Sari Rahmah Laila Reinaldy Mansa, Jeremy Rinianty, Rinianty Rizka Ardiansyah Rizqa Raaiqa Bintana Ryfial Azhar, Ryfial Sabarudin Saputra Sahril Sahril Saragih, Linda Hertaty Semmagga, Nuraeni Septiano Anggun Pratama Sri Khaerawati Nur Suryani Putri, Fadiah Syahrullah Syahrullah Syahrullah Syaiful Hendra Tria Meisya Aziti Wahyu Setiawan Wongkar, Noel Marcell Jonathan Yanti, Alda Yudhaswana, Yuri Yuri Yudhaswana Joefrie Yusuf Anshori