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All Journal Journal of Technology and Informatics (JoTI) Router : Jurnal Teknik Informatika dan Terapan Router : Jurnal Teknik Informatika dan Terapan
Anna Baita
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Mechanical Engineering

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Analisis Sentimen Review Film Avatar 2 pada Platform IMDb Menggunakan LSTM dan GRU Rani Saputri; Anna Baita
Router : Jurnal Teknik Informatika dan Terapan Vol. 3 No. 1 (2025): Maret: Router : Jurnal Teknik Informatika dan Terapan
Publisher : Asosiasi Profesi Telekomunikasi dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/router.v3i1.395

Abstract

This research uses a deep learning-based sentiment analysis approach with several main stages, namely data collection, preprocessing, model preparation, and model building. In addition, this research also evaluates the impact of data splitting techniques on the model's performance during the training process.The evaluation results show that the LSTM-GRU model achieved the best performance on the character aspect, with an F1-score of 0.72 in the 90:10 splitting scenario. Meanwhile, the lowest F1-score was found in the editing aspect, with a value of 0.51 in the 80:20 splitting scenario. These findings indicate that the model is more effective in recognizing sentiment in narrative aspects compared to technical aspects. Furthermore, the data splitting technique significantly influences model performance, both in determining accuracy levels and in optimizing the model's effectiveness in identifying sentiment patterns more accurately.
Analisis Sentimen Review Film Avatar 2 pada Platform IMDb Menggunakan LSTM dan GRU Rani Saputri; Anna Baita
Router : Jurnal Teknik Informatika dan Terapan Vol. 3 No. 1 (2025): Maret: Router : Jurnal Teknik Informatika dan Terapan
Publisher : Asosiasi Profesi Telekomunikasi dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/router.v3i1.395

Abstract

This research uses a deep learning-based sentiment analysis approach with several main stages, namely data collection, preprocessing, model preparation, and model building. In addition, this research also evaluates the impact of data splitting techniques on the model's performance during the training process.The evaluation results show that the LSTM-GRU model achieved the best performance on the character aspect, with an F1-score of 0.72 in the 90:10 splitting scenario. Meanwhile, the lowest F1-score was found in the editing aspect, with a value of 0.51 in the 80:20 splitting scenario. These findings indicate that the model is more effective in recognizing sentiment in narrative aspects compared to technical aspects. Furthermore, the data splitting technique significantly influences model performance, both in determining accuracy levels and in optimizing the model's effectiveness in identifying sentiment patterns more accurately.
Prediksi Stunting pada Anak Balita Menggunakan Algoritma Extreme Gradient Boosting dan Bayesian Optimization Rangga Yoga Pratama; Anna Baita
Journal of Technology and Informatics (JoTI) Vol. 7 No. 2 (2025): Vol. 7 N. 2 (2025)
Publisher : Universitas Dinamika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37802/joti.v7i2.1174

Abstract

Stunting is a chronic malnutrition condition affecting children under five years that impairs cognitive development, physical growth, and future productivity. This study develops a stunting risk prediction model using the Extreme Gradient Boosting (XGBoost) algorithm with hyperparameter tuning and data balancing techniques. The dataset from Kaggle contains 120,998 records with variables including age, gender, height, and nutritional status. The methodology encompasses data preprocessing for outlier handling, categorical encoding, and feature extraction based on height thresholds. Feature selection utilized ANOVA F-test, while Exploratory Data Analysis identified height as the most influential attribute. To address class imbalance, Synthetic Minority Over-sampling Technique (SMOTE) was implemented, followed by Bayesian Optimization for hyperparameter tuning. Model evaluation was conducted using various data splits (80:20, 70:30, 60:40, 50:50) with metrics including accuracy, precision, recall, and F1-score. Results demonstrate that the optimized XGBoost model achieved exceptional performance with 0,982% accuracy, 0,973% precision, 0.979% recall, and 0,976% F1-score, consistently across all data configurations. The combination of XGBoost with Bayesian Optimization and SMOTE proves highly effective in handling imbalanced classification tasks. These findings highlight machine learning's potential in supporting public health initiatives through accurate early identification and targeted intervention for stunting prevention.
Co-Authors Rangga Yoga Pratama Rani Saputri