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p-Index From 2021 - 2026
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This Author published in this journals
All Journal International Journal of Informatics Engineering and Computing
Muhammad Fahrurrozi
UIN Sunan Kalijaga Yogyakarta, Indonesia
Computer Science & IT

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Journal : international journal of informatics engineering and computing

 1 
Decision of Student's Dormitory Accommodation Using SAW Method Muhammad Fahrurrozi; Arita Witanti
International Journal of Informatics Engineering and Computing Vol. 1 No. 1 (2024): International Journal of Informatics Engineering and Computing
Publisher : ASTEEC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70687/ijimatic.v1.i1.28

Abstract

This study presents the development and evaluation of a Decision Support System (DSS) designed to streamline the student selection process for dormitory administrators. The system leverages the Simple Additive Weighting (SAW) method to evaluate applicants based on predefined criteria, including academic performance, financial need, and extracurricular involvement. Testing conducted on five applicants demonstrated a 100% accuracy rate, validating the system's effectiveness in identifying the most suitable candidates. The SAW method's ability to aggregate and normalize multiple criteria into a single quantitative score ensures a robust, transparent, and fair decision-making framework. This approach significantly enhances the precision of candidate selection while reducing manual effort and minimizing biases. The system's adaptability allows for the incorporation of additional criteria or adjustments to weighting schemes, ensuring its applicability across diverse institutional contexts. The successful implementation of this system underscores the value of integrating multi-criteria decision-making techniques into administrative workflows, offering a scalable and accountable solution for optimizing resource allocation in dormitory management.
Optimizing Machine Learning Algorithms to Accelerate Smoking Detection Muhammad Fahrurrozi; Germanus Naru
International Journal of Informatics Engineering and Computing Vol. 1 No. 2 (2024): International Journal of Informatics Engineering and Computing
Publisher : ASTEEC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70687/ijimatic.v1.i2.42

Abstract

This study evaluates the performance of various classification algorithms, including Convolutional Neural Networks (CNN), Support Vector Machines (SVM), Decision Trees, K-Nearest Neighbors (KNN), Gaussian Naive Bayes (GNB), and Gradient Boosting (Gboost), on a binary classification task. The results reveal that CNN achieves perfect performance, with an accuracy of 1.00 and precision, recall, and F1-scores of 1.00 for both classes. Similarly, SVM, Decision Tree, KNN, and Gboost also demonstrate flawless performance across all metrics. In contrast, GNB underperforms significantly, with an accuracy of 0.78 and lower precision, recall, and F1-scores, particularly for the "no" class. These findings highlight CNN's robustness and reliability, positioning it as a top-performing algorithm for this classification task. The study underscores the effectiveness of CNN and other high-performing algorithms while identifying limitations in GNB. Future research could focus on optimizing computational efficiency and scalability for real-world applications.
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.
Co-Authors Arita Witanti Germanus Naru