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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 16 Documents
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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.

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