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Moh. Diqi
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International Journal of Informatics Engineering and Computing
Published by ASTEEC Publisher
ISSN : -     EISSN : 30909112     DOI : https://doi.org/10.70687/ijimatic
Core Subject : Science,
Computer Science & IT
International Journal of Informatics Engineering and Computing (IJIMATIC) is an international, peer-reviewed, open-access journal that publishes original theoretical and empirical work on the science of informatics and its application in multiple fields. Our concept of informatics encompasses technologies of information and communication, as well as the social, linguistic, and cultural changes that initiate, accompany, and complicate their development. IJIMATIC aims to be an international platform to exchange novel research results in simulation-based science across all computer science disciplines.
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 22 Documents
 1   2   3 
Predictive Maintenance for Al Sabiya Power Plant Using Machine Learning Algorithms Adel Sayed, Ahmed; Shalaby, Yasmeen Ali
International Journal of Informatics Engineering and Computing Vol. 2 No. 1 (2025): International Journal of Informatics Engineering and Computing
Publisher : ASTEEC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70687/ijimatic.v2i1.49

Abstract

This study develops a predictive maintenance framework for the Al Sabia steam power plant in Kuwait, employing Support Vector Machine (SVM) and K-nearest Neighbor (KNN) algorithms. This research focuses on anticipating maintenance needs based on critical operational parameters, including temperature, pressure, flow rate, operational hours, and alert signals. Experimental results indicate that SVM outperforms KNN, achieving an accuracy of 0.95 compared to 0.93 for KNN, along with superior precision, recall, and F1-score, suggesting its suitability for this application. Furthermore, an ensemble model SVM and KNN achieves an accuracy of 0.93. The adoption of this model is expected to markedly reduce downtime, improve storage quality, and enhance overall power plant reliability. Additionally, this paper provides a comparative analysis of a neural network model developed in TensorFlow and its equivalent model implemented in TensorFlow Lite. The analysis evaluates both models on three key performance metrics: accuracy, sample size, and latency. Both the TensorFlow and TensorFlow Lite models attain an accuracy of 0.95, affirming TensorFlow Lite's efficacy in facilitating high-performance machine learning on resource-constrained hardware.
Efficient Flood Prediction with SVM and RF Algorithm Juwita Sampe Ruru
International Journal of Informatics Engineering and Computing Vol. 2 No. 1 (2025): International Journal of Informatics Engineering and Computing
Publisher : ASTEEC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70687/ijimatic.v2i1.85

Abstract

Flood is a high risk of natural disasters such as floods due to its geological location at the intersection of four major tectonic plates. This study aims to predict flood risks using the Support Vector Machine (SVM) and Random Forest (RF) algorithms, utilizing rainfall, topography, and land use data. Historical rainfall data were obtained from BMKG, topographic data from GIS, and land use data from satellite imagery. The evaluation results show that the RF algorithm outperforms SVM, achieving 92.1% accuracy and an F1-score of 91.8%. RF has proven effective in capturing non-linear relationships between features influencing flood risk. This predictive system is expected to aid disaster mitigation, spatial planning, and the development of an early flood warning system.
Enhancing Rainfall Prediction Using LSTM Algorithm Selamet Riadi; Jamil, Trisna
International Journal of Informatics Engineering and Computing Vol. 2 No. 1 (2025): International Journal of Informatics Engineering and Computing
Publisher : ASTEEC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70687/ijimatic.v2i1.86

Abstract

Rainfall is an important factor that influences various aspects of human life, including agriculture, transportation, and urban planning. With climate change, the need for accurate rainfall prediction systems is becoming increasingly urgent. Traditional methods, such as statistical or physical models, often struggle to deal with the complex and nonlinear nature of weather data. This research proposes the use of Long Short-Term Memory (LSTM), a deep learning model capable of processing sequential data, to predict rainfall based on historical data. The model can capture long-term dependencies, making it suitable for analyzing meteorological data such as temperature, humidity, wind speed and rainfall intensity. This paper investigates the performance of an LSTM-based rainfall prediction system, and compares it with traditional forecasting methods. Evaluation metrics such as Root Mean Square Error (RMSE) are used to assess the accuracy of predictions. These findings indicate that LSTM-based models provide a more reliable solution for rainfall prediction, especially in detecting extreme weather events early.
Stepping up Support Vector Machine Algorithm for Flood Prediction Muhammad Fahrurrozi
International Journal of Informatics Engineering and Computing Vol. 2 No. 1 (2025): International Journal of Informatics Engineering and Computing
Publisher : ASTEEC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70687/ijimatic.v2i1.91

Abstract

Flooding is one of the natural disasters that often occurs in Dompu Regency, especially around the Rabalaju River. To anticipate the adverse impacts caused, an accurate prediction system is needed to detect the potential for flooding. This research aims to apply the machine learning method Support Vector Machine (SVM) as a flood prediction model in Rabalaju River. The data used in this research includes historical data on rainfall, water level, soil moisture, and river flow discharge. The research stages include data collection, data preprocessing, SVM model building, and model performance evaluation using accuracy, precision, recall, and F1-score metrics. The results showed that the SVM method was able to provide accurate predictions with an accuracy rate of 92%. The implementation of this method is expected to help related parties, such as local governments and local communities, in mitigating flood disasters more effectively. This research also provides further development recommendations, such as model integration with the Python programming language for real-time data monitoring.
Forest Fire Detection Model Using Dense Net Architecture Rike Pradila; Akhyar Bintang
International Journal of Informatics Engineering and Computing Vol. 2 No. 1 (2025): International Journal of Informatics Engineering and Computing
Publisher : ASTEEC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70687/ijimatic.v2i1.93

Abstract

Forest and land fires in Indonesia are frequent events and cause significant losses in the health, ecological and social sectors. Human and natural factors play a role in triggering these fires. However, handling forest and land fires still faces obstacles in accurately predicting the location of hot spots, making optimal control difficult. Therefore, it is necessary to develop an intelligent system to detect forest and land fires more effectively. This research aims to create a model that is capable of detecting forest and land fires using a transfer learning approach, utilizing the DenseNet201 architecture to increase detection accuracy. The dataset used in this research comes from the Fire Forest Dataset on the Kaggle site. The feature extraction process was carried out using the DenseNet201 architecture, and the resulting model was tested using the confusion matrix method to classify images into two classes, namely fire and non-fire classes. Through training using the DenseNet201 architecture, an effective model was obtained in detecting forest and land fires. Test results using 380 test data show an accuracy level of 99% in recognizing images of forest and land fires. It is hoped that this research can provide a basis for the development of smart systems that are more sophisticated and effective in overcoming the problem of forest and land fires, as well as protecting the environment and public health in Indonesia.
Optimizing Sunspot Forecasts: An In-Depth Analysis of the ConcaveLSTM Model Ordiyasa, I Wayan; Diqi, Mohammad; Hiswati, Marselina Endah; Wandani, Aulia Fadillah Wani
International Journal of Informatics Engineering and Computing Vol. 2 No. 1 (2025): International Journal of Informatics Engineering and Computing
Publisher : ASTEEC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70687/ijimatic.v2i1.103

Abstract

This work examines how effectively the ConcaveLSTM model can forecast sunspot numbers, recognizing their importance in space weather. The model addresses the complex and changing sunspot characteristics to improve forecasting accuracy. By comparing different model variations, this research identifies optimal combinations of input steps and LSTM units that enhance forecast performance while avoiding overfitting. The study showcases the capability of specific architectures concerning detail versus computational cost, using evaluation metrics such as RMSE, MAE, MAPE, and R2. Considering factors like limited data availability and the complexity of solar phenomena, the ConcaveLSTM model could be a valuable tool for predicting solar activity. This research advances understanding of space weather forecasting through machine learning and offers guidance for further model development and future investigations.
Monitoring System for Sugar Storage using DHT22, Ultrasonic, and Light Sensors Izzurohman, Moh.; Mulyani, Sri Hasta; Ordiyasa, I Wayan
International Journal of Informatics Engineering and Computing Vol. 2 No. 2 (2025): International Journal of Informatics Engineering and Computing
Publisher : ASTEEC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70687/c3d6kr84

Abstract

This study develops an Internet of Things (IoT)-based monitoring system designed to maintain stable environmental conditions in palm sugar storage warehouses. The system integrates a NodeMCU ESP8266 microcontroller, a DHT22 temperature and humidity sensor, an OLED display, and a relay-controlled exhaust fan to monitor and regulate environmental parameters. Experimental evaluation was conducted using 30 measurement samples collected at 15-minute intervals in a simulated warehouse environment. The accuracy of the DHT22 sensor was assessed by comparing its readings with calibrated digital instruments. The results show that the average temperature measurement error was 0.3923°C, while the humidity error reached approximately 2.1%. The monitoring system successfully displayed real-time environmental conditions and automatically activated the exhaust fan when the temperature exceeded 30°C or the humidity surpassed 67.89%. Telegram notifications were delivered with an average latency of approximately 1–2 seconds after threshold detection, demonstrating near real-time system responsiveness. Overall, the proposed IoT-based monitoring system demonstrates reliable performance in monitoring and managing environmental conditions in palm sugar storage facilities. The integration of automated control, remote notification, and web-based data visualization provides a practical and cost-effective solution for warehouse monitoring.
Weather Forecasting Using Stacked-LSTM Hidayatulloh, M. Riyan; Anjani, Sarah
International Journal of Informatics Engineering and Computing Vol. 2 No. 2 (2025): International Journal of Informatics Engineering and Computing
Publisher : ASTEEC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70687/408j8q02

Abstract

This study proposes a Stacked Long Short-Term Memory (Stacked LSTM) model for multivariate weather forecasting using historical meteorological data from Denpasar City. The dataset consists of 264,924 records collected between 1990 and 2020, including four key weather variables: temperature, humidity, pressure, and wind speed. The model is designed to capture temporal dependencies in time-series weather data through multiple LSTM layers. A sliding window technique is used to construct input sequences, and the model is trained for 50 epochs with a batch size of 64, incorporating dropout regularization to improve generalization. The dataset is divided using a train–test split, where 20% of the data is reserved for performance evaluation. Experimental results demonstrate that the proposed model achieves strong predictive performance across all weather variables. The evaluation on the test dataset yields an average Mean Absolute Error (MAE) of 1.08, Mean Absolute Percentage Error (MAPE) of 10.22%, Root Mean Squared Error (RMSE) of 1.93, and a Coefficient of Determination (R²) of 0.86. Among the predicted variables, humidity and temperature show the highest accuracy with R² values of 0.9537 and 0.9031, respectively. The findings indicate that the Stacked LSTM architecture successfully captures both short-term and long-term temporal relationships within multivariate weather datasets. The proposed approach demonstrates strong potential for improving automated weather forecasting systems, particularly in tropical urban environments characterized by complex climatic dynamics. Future work may focus on integrating real-time weather data sources and adaptive retraining mechanisms to further enhance prediction accuracy and operational applicability.
Comparison of Hybrid CNN-LSTM Models for Stock Price Prediction Rike Pradila; Rafitajudin, Rafitajudin
International Journal of Informatics Engineering and Computing Vol. 2 No. 2 (2025): International Journal of Informatics Engineering and Computing
Publisher : ASTEEC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70687/sdts5v08

Abstract

This study explores the application of deep learning techniques for stock price prediction by comparing Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and hybrid CNN–LSTM architectures. We propose a hybrid deep learning model that integrates convolutional layers for local feature extraction with LSTM layers for capturing long-term temporal dependencies in financial time-series data. Historical stock price data of INDF.JK obtained from Yahoo Finance were used to train and evaluate the models. The dataset was preprocessed and transformed into sequential input using a sliding window approach to enable effective time-series learning. Model performance was evaluated using several regression metrics, including Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and the coefficient of determination (R²). Experimental results demonstrate that the proposed hybrid CNN–LSTM model achieves superior prediction performance compared with standalone CNN and LSTM models. The hybrid model records an RMSE of 87.77, MAE of 63.97, and MAPE of 1.02%, while achieving the highest R² score of 0.9759. In comparison, the CNN model produces an RMSE of 96.18 and an R² score of 0.9711, whereas the LSTM model achieves an RMSE of 89.13 with an R² score of 0.9752. These results indicate that the hybrid architecture provides more accurate predictions and better captures the complex patterns in stock price movements. The findings confirm that combining CNN and LSTM architectures enables the model to learn both spatial and temporal representations of financial time-series data. CNN layers effectively identify local patterns within historical price sequences, while LSTM layers capture long-term dependencies that influence future stock prices. Consequently, the hybrid CNN–LSTM framework offers a reliable approach for financial forecasting and has strong potential for practical applications in stock market prediction systems. Future work may incorporate additional technical indicators, sentiment data, or attention-based mechanisms to further enhance prediction accuracy and robustness.
Detection of DDoS Attacks Using Hybrid LSTM and SVM Algorithm Nahak, Ivansius; M. Hizbul Wathan
International Journal of Informatics Engineering and Computing Vol. 2 No. 2 (2025): International Journal of Informatics Engineering and Computing
Publisher : ASTEEC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70687/vd7kk061

Abstract

Distributed Denial of Service (DDoS) attacks pose serious threats to network infrastructures by disrupting services through massive malicious traffic. This study proposes a hybrid detection model that integrates Long Short-Term Memory (LSTM) with a Support Vector Machine (SVM) classifier to improve the accuracy of DDoS detection in network traffic. The LSTM model captures temporal patterns within sequential traffic data, while the SVM performs the final classification to distinguish between normal and anomalous traffic. The experiment uses a dataset containing 104,345 records with 23 features that undergo preprocessing, encoding, scaling, and class balancing before model training. Experimental results demonstrate that the proposed hybrid model achieves stable learning performance with training accuracy reaching approximately 93% and validation accuracy around 94%. The loss curves show consistent decreases across 50 training epochs, indicating effective convergence and minimal overfitting. Confusion matrix analysis shows that the model correctly classifies the majority of normal and anomalous traffic samples, with relatively low false positive and false negative rates. Overall evaluation results show that the hybrid LSTM–SVM model achieves 95% accuracy with balanced classification performance. The model records strong precision, recall, and F1-score values for both normal and anomalous traffic classes.

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