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All Journal RABIT: Jurnal Teknologi dan Sistem Informasi Univrab JOURNAL OF APPLIED INFORMATICS AND COMPUTING Abdimasku : Jurnal Pengabdian Masyarakat Aiti: Jurnal Teknologi Informasi Jurnal Informatika: Jurnal Pengembangan IT Jurnal Teknik Informatika dan Teknologi Informasi
Sindhu Rakasiwi
Universitas Dian Nuswantoro
Computer Science & IT Economics, Econometrics & Finance Engineering Health Professions Industrial & Manufacturing Engineering Social Sciences

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Journal : journal of applied informatics and computing

 1 
Flood Status Prediction Based on Water Level Data Using Machine Learning Models Aisyah Putri Widyastuti; Sindhu Rakasiwi
Journal of Applied Informatics and Computing Vol. 10 No. 3 (2026): June 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i3.12809

Abstract

Flooding is one of the hydrometeorological disasters that frequently occurs in Indonesia and causes various social and economic losses. This study aims to compare the performance of five machine learning algorithms, namely Random Forest, Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Logistic Regression, as well as one Long Short-Term Memory (LSTM) deep learning model in predicting flood status based on water level data from seven observation posts in the DKI Jakarta area and its surroundings. The research stages include data preprocessing, handling unbalanced data using ADASYN, hyperparameter tuning, and evaluation using accuracy, precision, recall, and F1-score. To avoid data leakage, the data division process is carried out before preprocessing and oversampling. The results show that XGBoost produces the best performance with 96.0% accuracy, 95.5% precision, 96.9% recall, and 96.2% F1-score after hyperparameter tuning. The LSTM model also demonstrated competitive performance with an accuracy of 94.5% and an F1-score of 94.5%. Learning curve analysis showed that all models exhibited normal learning patterns with no indication of data leakage. The results indicate that XGBoost and LSTM have good potential for application in flood early warning systems based on water level data.
Anti-Data Leakage Pipeline for Differentiated Thyroid Cancer Recurrence Prediction: Integrating SMOTE, Optuna-based Optimization, and Bootstrap BCa Validation Deri Rosadi; Sindhu Rakasiwi
Journal of Applied Informatics and Computing Vol. 10 No. 3 (2026): June 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i3.12922

Abstract

Thyroid cancer recurrence prediction remains a critical clinical challenge, as early identification of high-risk patients enables targeted monitoring and intervention. This study presents a comparative evaluation of six machine learning classifiers (XGBoost, LightGBM, CatBoost, Logistic Regression, Random Forest, and Decision Tree) using the UCI Differentiated Thyroid Cancer Recurrence dataset which consists of 383 patient records and 16 clinical features. To prevent performance overestimation, a rigorous anti-data leakage pipeline was implemented, encapsulating SMOTE, Optuna-based hyperparameter optimization, and Isotonic Calibration within the cross-validation process. Furthermore, model stability was assessed using Bias-Corrected and accelerated (BCa) Bootstrap validation with 2,000 iterations. Experimental results demonstrate that XGBoost achieved the best overall performance with an F1-score of 0.9545, an AUC-ROC of 0.9967, and the lowest Brier Score of 0.0183. Bootstrap BCa analysis confirmed XGBoost as the most stable model, with a 95% CI F1-score width of 0.1429 and unbiased estimation. These findings suggest that XGBoost, integrated within a zero-leakage pipeline and validated through Bootstrap BCa, is a promising candidate for post-treatment clinical decision support in differentiated thyroid cancer management.
Evaluating LSB and MSB Steganography in Retinal Fundus Images Through Image Quality Assessment and VGG19-Based Classification Gilang Faturrahman; Muhammad Naufal; Wahyu Aji Eko Prabowo; Sindhu Rakasiwi
Journal of Applied Informatics and Computing Vol. 10 No. 3 (2026): June 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i3.13198

Abstract

The security of medical image data within electronic medical record systems has become a critical issue due to the increasing threat of health data breaches. Steganography is a promising technique for protecting patient information by concealing secret data within medical images without significantly altering their visual appearance. However, the application of steganography to retinal fundus images, which carry high diagnostic value, has never been comprehensively evaluated in terms of image quality or its impact on artificial intelligence-based diagnostic model performance. This study compares Least Significant Bit (LSB) and Most Significant Bit (MSB) steganography methods applied to 3,200 retinal fundus images from the Retinal Fundus Multi-disease Image Dataset (RFMiD) dataset across four payload levels (0.1-0.4 bpp), evaluated using PSNR, SNR, SSIM, and FSIM for image quality, and VGG19 classification accuracy and AUC for diagnostic impact. Results show LSB achieves substantially superior image quality (PSNR: 59.97-65.93 dB; SNR: 49.34-55.30 dB; SSIM: 0.9981-0.9997; FSIM: 0.9999-1.0000) compared to MSB (PSNR: 12.98-18.99 dB; SNR: 2.35-8.37 dB; SSIM: 0.5979-0.9003; FSIM: 0.5342-0.7500), while VGG19 classification accuracy remains stable for both methods (LSB: 0.8938-0.9000; MSB: 0.8953-0.9031) with a maximum difference of 0.62% from baseline. This study demonstrates that LSB is the more appropriate steganography method for retinal fundus images, delivering superior visual quality while preserving VGG19 diagnostic capability.
Co-Authors Abu Salam Aisyah Putri Widyastuti Almira Zuhrotus Safira Daiyan Akbar Setiyadi Deri Rosadi Dhita Aulia Octaviani Edy Mulyanto Egia Rosi Subhiyakto Endri Mujiono Erlin Dolphina Etika Kartikadarma Gilang Faturrahman Ika Novita Dewi Ilham Maulana Hadinanda Junta Zeniarja Muhammad Naufal Raid Alvaro Fathin Lie Shelomita Fitriyani Wahyu Aji Eko Prabowo Yani Parti Astuti