Indonesian Journal of Electrical Engineering and Computer Science Vol. 32, No. 3, December 2023, pp. 1825~1836 ISSN: 2502-4752, DOI: 10.11591/ijeecs.v32.i3.pp1825-1836  1825 Journal homepage: http://ijeecs.iaescore.com Forecast earthquake precursor in the Flores Sea Adi Jufriansah 1 , Ade Anggraini 2 , Zulfakriza 3 , Azmi Khusnani 1 , Yudhiakto Pramudya 4 1 Physics Education, Faculty of Mathematics and Science Education, Universitas Muhammadiyah Maumere, Maumere, Indonesia 2 Geoscience Research Group, Faculty of Engineering, Gadjah Mada University, Yogyakarta, Indonesia 3 Global Geophysics Research Group, Faculty of Earth Sciences and Technology, Bandung Institute of Technology, Bandung, Indonesia 4 Master of Physics Education, Ahmad Dahlan University, Yogyakarta, Indonesia Article Info ABSTRACT Article history: Received Oct 6, 2023 Revised Oct 12, 2023 Accepted Oct 21, 2023 Artificial intelligence (AI) can use seismic training data to discover relationships between inputs and outcomes in real-world applications. Despite this, particularly when using geographical data, it has not been used to predict earthquakes in the Flores Sea. The algorithm will read the seismic data as a pattern of iterations throughout the operation. The output data is created by grouping based on clusters using the most effective WCSS analysis, while the input features are derived from the original international resource information system (IRIS) web service data. Given that earthquake prediction is an effort to reduce seismic disasters, this research is essential. By generating predictions, it can reduce the devastation caused by earthquakes. Using the support vector machine (SVM), hyperparameter support vector machine (HP-SVM), and particle swarm optimization support vector machine (PSO-SVM) algorithms, this study seeks to forecast the Flores Sea earthquake. According to the estimation results, the SVM algorithm’s evaluation value is less precise than that of the HP-SVM, especially the linear HP-SVM and HP-SVM Polynomial models. However, the HP-SVM RBF model’s accuracy rating is identical to that of the traditional SVM model. The improvement of the PSO-SVM model, which has the finest gamma position and a value of 9. Keywords: Artificial intelligence Earthquake HP-SVM algorithm PSO-SVM algorithm SVM algorithm This is an open access article under the CC BY-SA license. Corresponding Author: Adi Jufriansah Physics Education, Faculty of Mathematics and Science Education, Universitas Muhammadiyah Maumere Maumere, East Nusa Tenggara, Indonesia Email: saompu@gmail.com 1. INTRODUCTION The Flores back arc thrust is the cause of the Flores Sea’s seismic activity [1], [2]. The Kalaotoa fault, which was discovered by the December 14, 2021, earthquake, is a new fault [3]–[5]. According to the Meteorological, Climatological, and Geophysical Agency (BMKG) study, East Nusa Tenggara will experience an increase in the number of earthquakes in 2022, with a total of 3,621 occurrences. This data pertains to the Flores Sea’s geographic location, with water depths ranging from 300 metres (in the centre) to 5,500 metres (in the south) and a precipitous and undulating morphological structure in the southeast see Figure 1. Therefore, geological structure may govern the Flores Sea [6], [7]. The study of earthquake forecasting is one of the numerous endeavours scientists make to mitigate the effects of earthquake disasters [8]. Marhain et al. [9], claims that an artificial intelligence (AI) method can be used to predict earthquakes. Using earthquake data compiled and recorded in a database, it is possible to calculate algorithm parameters [10]. As a first step in mitigation, it is necessary to take steps to reduce earthquake damage [11], [12]. One of them is using algorithms to forecast the future.