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All Journal International Journal Of Science, Technology & Management (IJSTM) Journal of Artificial Intelligence and Engineering Applications (JAIEA)
Ibnu Alfarobi
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Aerospace Engineering Agriculture, Biological Sciences & Forestry Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Mechanical Engineering Transportation

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Implementation of Logistic Regression Algorithm in Predicting Tsunami Potential on Earthquake Data Parameters Sofian Wira Hadi; Ibnu Alfarobi; Irmawati
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 2 (2025): February 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i2.871

Abstract

This study presents the evaluation and testing of a logistic regression model for predicting earthquake-related features, including earthquake depth, magnitude, and tsunami potential. The model achieved high accuracy in predicting earthquake depth categories (99.82%) and earthquake magnitude (99.84%), but faced challenges with low recall for tsunami prediction (50%) due to class imbalance. Evaluation results showed that the model struggled to predict tsunami occurrence accurately, as the dataset contained a disproportionate number of 'no tsunami' instances. Despite these limitations, the model displayed high accuracy for earthquake depth and magnitude predictions. The testing phase revealed a series of prediction errors, particularly for the tsunami category, influenced by the imbalance in training data. The results emphasize the need for improved handling of imbalanced datasets and the potential for exploring other machine learning algorithms and techniques for better performance in multiclass classification problems. Future research could further refine these models by incorporating additional criteria and exploring other earthquake and tsunami prediction methodologies.
Decision Support System for Cloud Computing Service Selection Using the Weighted Product Method (Case Study: PT. Deptech Digital Indonesia) saputra, dedi; Kudiantoro Widianto; Tyas Setiyorini; Ibnu Alfarobi
International Journal of Science, Technology & Management Vol. 2 No. 1 (2021): January 2021
Publisher : Publisher Cv. Inara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46729/ijstm.v2i1.103

Abstract

The selection of cloud computing services requires careful consideration and review. Some aspects of the criteria that must be considered such as direct selection between services that take a long time and need to be done repeatedly. Decision Support System with Weighted Product (WP) method is an effective method because the time needed for calculation is much shorter. The purpose of this research is to apply Weighted Product (WP) method in the decision support system to choose cloud computing services where as a case study is PT Deptech Digital Indonesia, so that it can make it easier for companies to make decisions according to their needs. The calculation results using the WP method give preference to the top 3 services: Google Cloud, Amazon Web Services and Microsoft Azure. This proves that research with the WP method can be applied to various services that will be used in the future according to predetermined criteria. Based on these results, the system can recommend cloud computing services according to the needs and a good level of accuracy.
Co-Authors Dedi Saputra Irmawati Kudiantoro Widianto, Kudiantoro Sofian Wira Hadi Tyas Setiyorini