Article Per Year (5 Year)
p-Index From 2020 - 2025
0.408
P-Index
This Author published in this journals
All Journal IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Reconfigurable and Embedded Systems (IJRES) Civil Engineering Forum Teknik Sipil
Agus Budhie Wijatna
Sekolah Pasca Sarjana/Fakultas Teknik UGM
Civil Engineering, Building, Construction & Architecture Computer Science & IT Economics, Econometrics & Finance Engineering

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
PENGGUNAAN PASIR BESI DARI KULON PROGO DENGAN BERAT JENIS 4,311 UNTUK MORTAR PERISAI RADIASI SINAR GAMMA Putra, Hendra; Satyarno, Iman; Wijatna, Agus Budhie
Civil Engineering Forum Teknik Sipil Vol 18, No 3 (2008): SEPTEMBER 2008
Publisher : Civil Engineering Forum Teknik Sipil

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (202.613 KB)

Abstract

The radiation effects of radiology and x-rays equipments do not only give excellent benefit for human, but also harmful effect at the same time. Protecting people form the radiation is an important aspect to control such harmful effect. Hence every nuclear installation and radiology unit must pay attention on protecting surrounding people from the radiation. Lead is commonly used as component of shield, but the use of lead requires special work and energy. In economical aspects, the cost of such effort is relatively expensive, but in workability aspects, the application of iron sand mortar can be used as alternative material to protect from radiation. This study assessed gamma radiation absorption on mortar cube sample with dimension of 15 x 15 cm and thickness variation of 1 cm to 15 cm. Mortar ingredient consisted of iron sand, cements and water, with cements - iron sand volume ratio of 1 : 6. Water cement ratio was determined at 0,4 and the gamma radiant energy applied were Iodine-131 (131I) denergi 284,00 keVs, 364,00 keVs, 637,00 keVs and Caesium-137 (137Cs) dissociation energy of diatomic 662,00 keV. Physical test gradation conducted to iron sand from Congot beach Kulonprogo regency of Daerah Istimewa Yogyakarta Province, showed specific gravity of 4,331 with, Ssd specific gravity of 4,330, unit weight of 2,554 gr/cm³, water absorbency 0,442%, and grain finest modulus of 1,33, which was categorized as zone IV (smooth gradation). Compressive strength and specific gravity of Iron sand mortar at 28 days reached 7,92 MPa and 2,59 respectively. Especially, specific gravity was heavier than ordinary cements mortar with average value ranged from 1,80 - 2,20. Coefficient linear magnitude attenuation (μ) of iron sand mortar at radiation energy 284 keVs, 364 keVs, 637 keVs and 662 keVs were 0,2816 cm-1, 0,2253 cm-1, 0,1297 cm-1 and 0,1003 cm-1 respectively. Based on these relation, the line equation obtained was y = 0,5631e(-0025X).
Earthquake magnitude prediction in Indonesia using a supervised method based on cloud radon data Pratama, Thomas Oka; Sunarno, Sunarno; Wijatna, Agus Budhie; Haryono, Eko
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 13, No 3: November 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v13.i3.pp577-585

Abstract

In the challenging realm of earthquake prediction, the reliability of forecasting systems has remained a persistent obstacle. This study focuses on earthquake magnitude prediction in Indonesia, leveraging supervised machine learning techniques and cloud radon data. We present an analysis of the tele-monitoring system, data collection methods, and the application of regression-based machine learning algorithms. Utilizing a comprehensive dataset spanning 30 training instances and 105 test instances, the study evaluates multiple metrics to ascertain the efficacy of the prediction models. Our findings reveal that the linear regression approach yields the best earthquake magnitude prediction method, with the lowest values across multiple evaluation metrics: standard deviation 0.40, mean absolute error (MAE) 0.30, mean absolute percentage error (MAPE) 6%, root mean square error (RMSE) 0.52, mean squared error (MSE) 0.28, symmetric mean absolute percentage error (SMAPE) 0.06, and conformal normalized mean absolute percentage error (cnSMAPE) 0.97. Additionally, we discuss the implications of the research results and the potential applications in enhancing existing earthquake prediction methodologies.
Grindulu fault cloud radon data for earthquake magnitude prediction using machine learning Pratama, Thomas Oka; Sunarno, Sunarno; Wijatna, Agus Budhie; Haryono, Eko
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i4.pp4572-4582

Abstract

The study investigates the potential of integrating radon gas concentration telemonitoring systems with machine learning techniques to enhance earthquake magnitude prediction. Conducted in Pacitan, East Java, Indonesia, where the stations are near the active Grindulu fault, the research employs random forest (RF), extreme gradient boosting (XGB), neural network (NN), AdaBoost (AB), and support vector machine (SVM) methods. The study aims to refine earthquake magnitude prediction, utilizing real-time radon gas concentration measurements, crucial for disaster preparedness. The evaluation involves multiple metrics like mean absolute error (MAE), mean absolute percentage error (MAPE), root mean square error (RMSE), mean squared error (MSE), symmetric mean absolute percentage error (SMAPE), and conformal normalized mean absolute percentage error (cnSMAPE). XGB and SVM emerge as top performers, showcasing superior predictive accuracy with minimal errors across various metrics. XGB achieved MAE (0.33), MAPE (6.03%), RMSE (0.51), MSE (0.26), SMAPE (0.06), and cnMAPE (0.97), while SVM recorded MAE (0.34), MAPE (6.20%), RMSE (0.51), MSE (0.26), SMAPE (0.06), and cnSMAPE (0.97). The analysis reveals XGB as the most effective method, boasting the lowest error values. The study underscores the importance of expanding data availability to enhance predictive models, ultimately contributing to more precise earthquake magnitude predictions and effective mitigation strategies.
Co-Authors Eko Haryono Hendra Putra Heru Hendrayana Iman Satyarno Pratama, Thomas Oka Sudarmadji Sudarmadji Sunarno Sunarno