Ali Ridho Barakbah
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Published : 51 Documents
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Journal : JOIV : International Journal on Informatics Visualization

Neural Network for Earthquake Prediction Based on Automatic Clustering in Indonesia Shodiq, Mohammad Nur; Kusuma, Dedy Hidayat; Rifqi, Mirza Ghulam; Barakbah, Ali Ridho; Harsono, Tri
JOIV : International Journal on Informatics Visualization Vol 2, No 1 (2018)
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1115.541 KB) | DOI: 10.30630/joiv.2.1.106

Abstract

A model of artificial neural networks (ANNs) is presented in this paper to predict aftershock during the next five days after an earthquake occurrence in selected cluster of Indonesia with magnitude equal or larger than given threshold. The data were obtained from Indonesian Agency for Meteorological, Climatological and Geophysics (BMKG) and United States Geological Survey’s (USGS). Six clusters was an optimal number of cluster base-on cluster analysis implementing Valley Tracing and Hill Climbing algorithm, while Hierarchical K-means was applied for datasets clustering. A quality evaluation was then conducted to measure the proposed model performance for two different thresholds. The experimental result shows that the model gave better performance for predicting an aftershock occurrence that equal or larger than 6 Richter’s scale magnitude.
Adaptive Neural Fuzzy Inference System and Automatic Clustering for Earthquake Prediction in Indonesia Shodiq, Mohammad Nur; Kusuma, Dedy Hidayat; Rifqi, Mirza Ghulam; Barakbah, Ali Ridho; Harsono, Tri
JOIV : International Journal on Informatics Visualization Vol 3, No 1 (2019)
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1452.798 KB) | DOI: 10.30630/joiv.3.1.204

Abstract

Earthquake is a type of natural disaster. The Indonesian archipelago located in the world's three mega plates; they are Australian plate, Eurasian plate, and Pacific plate. Therefore, it is possible for applied of earthquake risk of mitigation. One of them is to provide information about earthquake occurrences. This information used for spatiotemporal analysis of earthquakes. This paper presented Spatial Analysis of Magnitude Distribution for Earthquake Prediction using adaptive neural fuzzy inference system (ANFIS) based on automatic clustering in Indonesia. This system has three main sections: (1) Data preprocessing, (2) Automatic Clustering, (3) Adaptive Neural Fuzzy Inference System. For experimental study, earthquake data obtained Indonesian Agency for Meteorological, Climatological, and Geophysics (BMKG) and the United States Geological Survey’s (USGS), the year 2010-2017 in the location of Indonesia. Automatic clustering process produces The optimal number of cluster, that is 7 clusters. Each cluster will be analyzed based on earthquake distribution. Its calculate the b value of earthquake to get the seven seismicity indicators. Then, implementation for ANFIS uses 100 training epochs, Number of membership function (MFs) is 2, MFs type input is gaussian membership function (gaussmf). The ANFIS result showed that the system can predict the non-occurrence of aftershocks with the average performance of 70%.
Big Data Environment for Realtime Earthquake Data Acquisition and Visualization Arif, Louis Nashih Uluwan; Barakbah, Ali Ridho; Sudarsono, Amang; Edelani, Renovita
JOIV : International Journal on Informatics Visualization Vol 3, No 4 (2019)
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (3512.439 KB) | DOI: 10.30630/joiv.3.4.320

Abstract

Indonesia is a country that has the highest level of earthquake risk in the world. In the past 10 years, there have been ± 90,000 earthquake events recorded and always increasing along with the explosion of earthquake data occurs at any time. The process of collecting and analyzing earthquake data requires more effort and takes a long computational time. In this paper, we propose a new system to acquire, store, manage and process earthquake data in Indonesia in real-time, fast and dynamic by utilizing features in the Big Data Environment. This system improves computational performance in the process of managing and analyzing earthquake data in Indonesia by combining and integrating earthquake data from several providers to form a complete unity of earthquake data. An additional function is the existence of an API (Application Programming Interface) embedded in this system to provide access to the results of earthquake data analysis such as density, probability density function and seismic data association between provinces in Indonesia. The process in this system has been carried out in parallel and improved computing performance. This is evidenced by the computational time in the preprocessing process on a single-core master node, which requires 55.6 minutes, but a distributed computing process using 15 cores can speeds up with only 4.82 minutes.
Incremental Associative Mining based Risk-Mapping System for Earthquake Analysis in Indonesia Edelani, Renovita; Barakbah, Ali Ridho; Harsono, Tri; Arif, Louis Nashih Uluwan
JOIV : International Journal on Informatics Visualization Vol 3, No 4 (2019)
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1760.156 KB) | DOI: 10.30630/joiv.3.4.319

Abstract

Indonesia is one of the largest archipelagic countries in the world that has the highest risk of an earthquake. The major causes of earthquakes in this country are plate movements and volcanic activity. Earthquakes in Indonesia has a cause and effect relationship between each province. This disaster caused severe damage including a lot of people to get killed, injured and lose their money and property. We must minimize the impact of the earthquake by forming earthquake risk mapping. The risk of seismicity in Indonesia can vary each year, so it needs to be analyzed how the changes in risk are each addition of earthquake data. This paper proposes an earthquake risk mapping system with Associative Mining based on incremental earthquake data that have the highest values of confidence rates from the seismic association between provinces in Indonesia. The system uses the Incremental Association rule method to see the trend in the value of changes in confidence for each addition of earthquake data every 5 years. This system proposes 3 main features, which are (1) Data Retrieval and Preprocessing, (2) Association Rule Mining, (3) Incremental Associative Mining based risk mapping. For the experimental study, the system used data from 1963-2018. The results show that the provinces of Maluku, North Maluku, Nusa Tenggara Timur, North Sulawesi, and Papua have an incremental association risk of an earthquake.