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Nyayu Latifah Husni, Nyayu Latifah
State Polytechnic of Sriwijaya
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Comparative Analysis of LSTM and GRU for River Water Level Prediction Faris, Fakhri Al; Taqwa, Ahmad; Handayani, Ade Silvia; Husni, Nyayu Latifah; Caesarendra, Wahyu; Asriyadi, Asriyadi; Novianti, Leni; Rahman, M. Arief
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.5.5054

Abstract

Accurate river water level prediction is essential for flood management, especially in tropical areas like Palembang. This study systematically analyzes the performance of two deep learning models, Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), for real-time water level forecasting using hourly rainfall and water level data collected from automatic sensors. A series of experiments were conducted by varying window sizes (10, 20, 30) and the number of layers (1, 2, 3) for both models, with model performance assessed using RMSE, MAE, MAPE, and NSE. The results demonstrate that both window size and network depth significantly influence prediction accuracy and computational efficiency. The LSTM model achieved its highest accuracy with a window size of 30 and a single layer, while the GRU model performed best with a window size of 20 and two layers. This work contributes by systematically analyzing hyperparameter configurations of LSTM and GRU models on hourly rainfall and water level time series for flood-prone regions, offering empirical insight into parameter tuning in recurrent neural architectures for hydrological forecasting. These findings highlight the importance of careful parameter selection in developing reliable early warning systems for flood risk management.
The Application of the Adaptive Neuro Fuzzy Inference System (ANFIS) Method in Estimating State of Charge (SOC) and State of Health (SOH) of Lithium-Ion Batteries Muslimin, Selamat; Prihatini, Ekawati; Husni, Nyayu Latifah; Caesandra, Wahyu
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 10, No. 4, November 2025
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v10i4.2357

Abstract

The increasing reliance on lithium-ion batteries (LIBs) for electric vehicles and portable electronics demands accurate monitoring of battery performance, particularly the State of Charge (SOC) and State of Health (SOH). Conventional estimation methods—such as Coulomb counting, Kalman filtering, and equivalent circuit modeling—face challenges under dynamic conditions due to drift and limited adaptability. Recent studies have explored machine learning and neuro-fuzzy approaches to enhance prediction accuracy, yet many lack integration of real-time hybrid learning or struggle with high estimation error in noisy data environments. This research aims to apply the Adaptive Neuro-Fuzzy Inference System (ANFIS) to estimate SOC and SOH using experimental data from a 48V lithium-ion battery. The novelty lies in combining voltage, current, and capacity data within a MATLAB-based ANFIS framework that employs a hybrid learning algorithm integrating backpropagation and Recursive Least Squares Estimation (RLSE). Training data for SOC estimation used charging voltage and current, while SOH estimation incorporated discharging data and capacity. Results show that ANFIS achieved high accuracy with RMSE of 0.1466 and MAE of 0.021 for SOC, and RMSE of 0.012 and MAE of 0.0017 for SOH. The estimated SOH was 33.61%, closely aligned with actual values. These findings confirm ANFIS as a robust and adaptive method for real-time battery diagnostics. Future work will explore multi-input hybrid models, the integration of IoT-based BMS telemetry, and testing across diverse battery chemistries to generalize the model's performance and extend its application in smart energy systems.
Multisensor monitoring system for detecting changes in weather conditions and air quality in agricultural environments Ramadhani, Dwi; Taqwa, Ahmad; Handayani, Ade Silvia; Caesarendra, Wahyu; Husni, Nyayu Latifah; Sitompul, Carlos R
Journal of Environment and Sustainability Education Vol. 3 No. 2 (2025)
Publisher : Education and Development Research

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62672/joease.v3i2.103

Abstract

The increasing impact of climate change and the need for precision agriculture demand reliable environmental monitoring solutions.This study aims to develop a real-time, multisensor-based environmental monitoring system that displays data via an I2C LCD and a user-friendly web interface. The system utilizes an ESP32 microcontroller connected to a range of sensors, including the DHT22 (for temperature and humidity), MQ-7 and MQ-135 (for CO and COâ‚‚), LDR (for light intensity), a rain sensor, and an anemometer (for wind speed). Testing was conducted over eight hours under various environmental conditions, both indoors and outdoors. Validation was performed by comparing the sensor readings with those from standard measuring instruments. The results showed that the DHT22 sensor had a low error rate of 0.62% for temperature and 0.38% for humidity. Other sensors demonstrated low standard deviation values, indicating stable and consistent measurements. The system also exhibited responsive and accurate performance in detecting changes in environmental parameters. Therefore, this system is effective as an environmental monitoring tool for agricultural applications and can support early decision-making based on environmental condition changes.
Real-Time Access Control System with YOLOv11-Based Face and Blink Detection Rifani, Namira Nur; Kusumanto, RD.; Husni, Nyayu Latifah
Indonesian Journal of Artificial Intelligence and Data Mining Vol 8, No 3 (2025): November 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v8i3.36812

Abstract

This study presents a real-time smart access control system that combines facial recognition with blink-based liveness detection to strengthen security and reduce spoofing risks. The main purpose is to provide a lightweight and efficient method that verifies both identity and physical presence in real time. The system employs two YOLOv11 models: one for detecting facial regions and another for distinguishing eye states through “open” and “closed” transitions. Identity verification is carried out by comparing facial embeddings using Euclidean distance. A private dataset was collected for facial images, while blink data was obtained from a public source, both annotated in YOLO format. After 100 epochs, the face detection model achieved 0.999 precision, 1.000 recall, 0.995 mAP50, and 0.868 mAP50–90, while the blink detection model recorded 0.959 precision, 0.962 recall, 0.967 mAP50, and 0.678 mAP50–90. These outcomes confirm that the objectives were achieved, demonstrating a practical and reliable biometric authentication solution with integrated liveness verification. The system also offers scalability for future multi-modal applications.
Comparative Evaluation of Optuna-Optimized Radial Basis Function and Sigmoid Kernels in Support Vector Machine for Smart Air Quality Classification Galea, Nanda; Rahman, A; Maulidda, Renny; Husni, Nyayu Latifah
Indonesian Journal of Artificial Intelligence and Data Mining Vol 8, No 3 (2025): November 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v8i3.37563

Abstract

Poor air quality can have a serious impact on human health, so a classification system capable of accurately identifying air conditions is needed. This research proposes an air quality classification method using the Support Vector Machine (SVM) algorithm with two types of non-linear kernels, namely Radial Basis Function (RBF) and Sigmoid. The data used is obtained from various environmental sensors that record parameters such as CO, smoke, HC, TVOC, eCO₂, temperature, and humidity, and then collected in the form of historical datasets. To enhance the accuracy and efficiency of the model, hyperparameter optimization was performed automatically using Optuna. The evaluation results showed that SVM with RBF kernel performed better than Sigmoid kernel, achieving an accuracy value of 96.67% and F1-score of 96.80%. In addition, RBF also showed higher stability in 5-fold cross validation. This research shows that the combination of SVM and Optuna is effective in building an accurate air quality classification system, and has the potential to be further developed as a sensor based in air monitoring system and IoT.
Co-Authors A Rahman A. Rahman Abdurahman Abdurrahman Ade Silvia Handayani Aditya, M Rizky Vira Ahmad Taqwa Al Rasyid, Johansyah Alfarizal, Niksen Amperawan Amperawan, Amperawan Andika, Destra Angguna, Welan Mauli Asriyadi Asriyadi Caesandra, Wahyu DWI RAMADHANI Evelina Evelina Faisal Damsi, Faisal Faris, Fakhri Al Felia, Okta Galea, Nanda Ganza Handayani, Ade S Silvia Hetty Meileni Irawan Hadi Irdayanti, Yeni Irsyadi Yani Kinasih, Ayu Antika Sekar Kusumanto, Kusumanto Leni Novianti Markinashella, Deva Marniati, Yessi Meranda, Arganda Moch Hikmal Abrar1 Muslimin, Selamat Niksen Alfarizal Permatasari, Rosmalinda Plowerita, Sanyyah Pola Risma Pratama, Destra Andika Prihatini, Ekawati Putra, Yogie Dwi Rahman, M. Arief Ramadhan, M. Daffa Rasyad, Sabilal RD Kusumanto Renny Maulidda, Renny Riansyah, Rici Rifani, Namira Nur Rizkiyanti, Shally Rossi Passarella Sepulau, Ifroh Intan Siti Chodijah Siti Nurmaini Sitompul, Carlos R Sobri, M. Ulandari, Monica Vira, Rizky Wahyu Caesarendra