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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) JOURNAL OF APPLIED INFORMATICS AND COMPUTING ILKOM Jurnal Ilmiah KOMPUTIKA - Jurnal Sistem Komputer JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Pengabdian kepada Masyarakat Nusantara Journal of Technology and Informatics (JoTI) LOGIC : Jurnal Ilmu Komputer dan Pendidikan
Baita, Anna
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Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Mechanical Engineering Social Sciences Other

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Comparative Study of Support Vector Regression and Long Short-Term Memory for Stock Price Prediction Aviva Pradasyah; Baita, Anna
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.9425

Abstract

This study aims to compare the performance of two machine learning algorithms, Long Short-Term Memory (LSTM) and Support Vector Regression (SVR), in predicting the stock prices of PT Bank Rakyat Indonesia (BBRI) using daily historical data from January 1, 2020, to January 10, 2025. The data were processed using a 60-day sliding window technique and normalized with MinMaxScaler. Model performance was evaluated using Mean Absolute Error (MAE), Mean Squared Error (MSE), and the coefficient of determination (R²) across five independent trials (5-fold trials). The evaluation results show that SVR outperforms in short-term prediction, with an average MAE of 0.0281, MSE of 0.0014, and R² of 0.9072. Meanwhile, LSTM records an average MAE of 0.0312, MSE of 0.0015, and R² of 0.8962, but achieves better performance in medium-term predictions, with a smaller average error of Rp228.02 compared to Rp242.52 from SVR. Both models demonstrate strong generalization capabilities on test data without signs of overfitting. Based on these findings, SVR is recommended for stable short-term forecasts, while LSTM is better suited for medium-term predictions involving complex trend patterns.
Machine Learning-Based Sentiment Analysis on Twitter (X): A Case Study of the “Kabur Aja Dulu” Issue Using SVM Rohmatun, Lina; Baita, Anna
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.9991

Abstract

This study aims to analyze public sentiment toward the phenomenon of “Kabur Aja Dulu” on Twitter (X) using the Support Vector Machine (SVM) method. The data used consists of 4,768 Indonesian-language tweets collected through web scraping. The pre-processing process includes data cleaning, tokenization, stemming, and translation into English for automatic sentiment labeling using TextBlob. The data is then classified into three sentiment categories: positive, negative, and neutral. To address class imbalance, the SMOTE method is applied to the training data, along with TF-IDF techniques for feature extraction. The model was evaluated using the K-Fold Cross Validation method and Grid Search for hyperparameter tuning. The results of the study show that the SVM model with a linear kernel and parameter C=10 provides the best performance with an accuracy value of 85.56%, precision of 845.19%, recall of 85.56%, and F1-score of 85.30%. The main finding of this study is that the linear SVM method is capable of classifying sentiment well, particularly for neutral sentiment data, and has proven effective as an approach to sentiment analysis in the context of social media using the Indonesian language.
Prediksi Stunting pada Anak Balita Menggunakan Algoritma Extreme Gradient Boosting dan Bayesian Optimization Pratama, Rangga Yoga; Baita, Anna
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.
Stroke prediction using data balancing method and extreme gradient boosting Rahim, Abd Mizwar A.; Baita, Anna; Asharudin, Firman; Ashari, Wahid Miftahul; Hakim, Walidy Rahman; Putra, Andriyan Dwi; Supriatin, Supriatin; Pramono, Eko
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i1.pp655-671

Abstract

Stroke is one of the leading causes of death worldwide, creating an urgent need for effective early detection systems, particularly because conventional methods often struggle with class imbalance and produce biased evaluations. Previous studies have primarily focused on accuracy while overlooking model consistency, data pre-processing quality, and probability-based evaluation. This study evaluates model performance under three conditions: original data using extreme gradient boosting (XGBoost) with scale_pos_weight, original data using the easy ensemble classifier, and class-balanced data generated using random oversampling (ROS), adaptive synthetic sampling (ADASYN), and synthetic minority over-sampling technique (SMOTE). Each model underwent missing value handling, normalization, feature preparation, and hyperparameter optimization using grid search. Performance was assessed using area under the receiver operating characteristic curve (AUROC), area under the precision-recall curve (AUPRC), confidence intervals, calibration curves, Shapley additive explanations (SHAP), decision curve analysis (DCA), and external validation. The results demonstrate that data resampling significantly improves performance, with the XGBoost-SMOTE combination achieving the best results, including an accuracy of 0.99, AUROC of 0.998, and AUPRC of 0.986, outperforming the other approaches. This method provides more consistent and balanced predictions, supporting the application of artificial intelligence for early stroke risk identification.
OPTIMASI NILAI IMPERCEPTIBILITY PADA WATERMARKING CITRA WARNA BERBASIS DCT-DWT Baita, Anna; Firmansyah, Rohmatullah Batik; Anggita, Sharazita Dyah
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 10, No 1 (2025)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v10i1.6878

Abstract

Teknik penyisipan watermark telah banyak digunakan untuk melindungi hak cipta, proses authentikasi maupun tamper detection. Terdapat dua jenis watermark berdasarkan tingkat persepsi visualnya, yakni visible watermark dan invisible watermark. Tantangan terbesar dari invisible watermark adalah mempertahankan tingkat imperceptibility namun tetap menjamin keamanan watermark dari berbagai serangan. Tujuan dari penelitian ini adalah untuk menghasilkan skema watermarking citra warna yang memiliki imperceptibility yang tinggi pada basis DCT DWT. Metode DWT dikenal memilki performa yang baik dalam invisible watermark. Untuk itu Chanel blue dipilih sebagai area penyisipan watermark karena mata manusia kurang sensitive terhadap warna ini.  Untuk meningkatkan keamanan, skema yang diusulkan menggunakan transformasi Arnold untuk mengacak watermark. Skema watermark yang diusulkan dapat menghasilkan imperceptibility yang cukup tinggi, yakni dengan nilai PSNR sebesar 43.786 dB. Nilai NC yang dihasilkan dalam skema ini sebesar 0.985 menunjukkan bahwa skema watermark mampu bertahan dari beberapa serangan. Akan tetapi skema ini kurang tahan terhadap serangan salt pepper serta cropping.
Co-Authors Andika Joni, Gusti Andriyan Dwi Putra Anggita, Sharazita Dyah Ashari, Wahid Miftahul Asharudin, Firman Aulia Fitri, Laili Aviva Pradasyah Damayanti, Annisa Eko Pramono Febriyanti, Gusti Ayu Putu Firmansyah, Rohmatullah Batik Fremmuzar, Pangestu Ghaisani, Mauhiba Salmaa Hakim, Walidy Rahman Karimah, Nur Nur Rachman, Ali Nuri Cahyono Pradnya Dhuhita, Windha Mega Pramudya Hidayat, Muhammad Rifqi Pratama, Rangga Yoga Rahim, Abd Mizwar A. Rohmatun, Lina Siswahyudianto Supriatin Supriatin, Supriatin Ubaid, Muhammad Yahya