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All Journal Journal of Information Systems Engineering and Business Intelligence Majalah Ilmiah Pengkajian Industri Jurnal Sains dan Teknologi Mitigasi Bencana
Riza, Hammam
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Aerospace Engineering Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Education Engineering Industrial & Manufacturing Engineering Transportation

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UTILIZATION OF ARTIFICIAL INTELLIGENCE TO IMPROVE FLOOD DISASTER MITIGATION Riza, Hammam; Santoso, Eko Widi; Tejakusuma, Iwan Gunawan; Prawiradisastra, Firman; Prihartanto, Prihartanto
Jurnal Sains dan Teknologi Mitigasi Bencana Vol 15, No 1 (2020): JURNAL SAINS DAN TEKNOLOGI MITIGASI BENCANA
Publisher : Badan Pengkajian dan Penerapan Teknologi (BPPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1516.032 KB) | DOI: 10.29122/jstmb.v15i1.4145

Abstract

Flood disaster is one of predominant disaster event in Indonesia. The frequency and intensity of this disaster tend to increase from year to year as well as the losses caused thereby. To reduce the risks and losses due to flood disasters, innovation in disaster mitigation is needed. Artificial intelligence and machine learning are technological innovations that have been widely applied in various fields of life and can also be used to improve flood disaster mitigation. A literature study conducted in this research shows that the use of artificial intelligence and machine learning has proven to be able, and succeed to fastly and accurately perform flood prediction, flood risk mapping, flood emergency response and, flood damage mapping. ANNs, SVM, SVR, ANFIS, WNN and DTs are popular methods used for flood mitigation in the pre-disaster phase and it is recommended to use a combination or hybrid of these methods. During the flood disaster response phase, the application of artificial intelligence and machine learning are still not much has been done and need to be developed. Examples of the application are the use of big data from social media Twitter and machine learning both supervised learning with Random Forest and unsupervised learning with CNN which have shown good results and have a good prospect to be applied. For the use of artificial intelligence in post-disaster flood phase, are still also rare, because it requires actual data from the field. However, in the future, it will become a promising program for the assessment and application of artificial intelligence in the flood disaster mitigation.
MACHINE LEARNING APPLICATION IN RESPONSE TO DISASTER RISK REDUCTION OF FOREST AND PEATLAND FIRE: Impact-Based Learning of DRR for Forest, Land Fire and Peat Smouldering Riza, Hammam; Santoso, Eko Widi; Kristijono, Agus; Melati, Dian Nuraini; Prawiradisastra, Firman
Majalah Ilmiah Pengkajian Industri Vol. 14 No. 3 (2020): Majalah Ilmiah Pengkajian Industri
Publisher : Deputi TIRBR-BPPT

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29122/mipi.v14i3.4426

Abstract

Peat forest is a natural swamp ecosystem containing buried biomass from biomass deposits originating from past tropical swamp vegetation that has not been decomposed. Once it burns, smoldering peat fires consume huge biomass. Peat smoldering fires are challenging to extinguish. These will continuously occur for weeks to months. Experts and practitioners of peat smoldering fires are the most recommended effort to prevent them before they occur with the strategy: 'detect early, locate the fire, deliver the most appropriate technology.' Monitoring methods and early detection of forest and land fires or 'wildfire' have been highly developed and applied in Indonesia, for example, monitoring with hotspot data, FWI (Fire Weather Index), and FDRS (Fire Danger Rating System). These 'physical simulator' based methods have some weaknesses, and soon such methods will be replaced by the Machine Learning method as it is developing recently. What about the potential application of Machine Learning in the forest and land fires, particularly smoldering peat fires in Indonesia? This paper tries to answer this question. This paper recommends a conceptual design: impact-based Learning for Disaster Risk Reduction (DRR) of Forest, Land Fire, and Peat Smouldering. Keywords: Artificial Intelligence; Machine Learning; Wildfire; Peat Smouldering; DRR impact-based
Incorporation of IndoBERT and Machine Learning Features to Improve the Performance of Indonesian Textual Entailment Recognition Tandi, Teuku Yusransyah; Abidin, Taufik Fuadi; Riza, Hammam
Journal of Information Systems Engineering and Business Intelligence Vol. 11 No. 2 (2025): June
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.11.2.173-186

Abstract

Background: Recognizing Textual Entailment (RTE) is a task in Natural Language Processing (NLP), used for question-answering, information retrieval, and fact-checking. The problem faced by Indonesian NLP is based on how to build an effective and computationally efficient RTE model. In line with the discussion, deep learning models such as IndoBERT-large-p1 can obtain high F1-score values but require large GPU memory and very long training times, making it difficult to apply in environments with limited computing resources. On the other hand, machine learning method requires less computing power and provide lower performance. The lack of good datasets in Indonesian is also a problem in RTE study.  Objective: This study aimed to develop Indonesian RTE model called Hybrid-IndoBERT-RTE, which can improve the F1-Score while significantly increasing computational efficiency.  Methods: This study used the Wiki Revisions Edits Textual Entailment (WRETE) dataset consisting of 450 data, 300 for training, 50 for validation, and 100 for testing, respectively. During the process, the output vector generated by IndoBERT-large-p1 was combined with feature-rich classifier that allowed the model to capture more important features to enrich the information obtained. The classification head consisted of 1 input, 3 hidden, and 1 output layer.  Results: Hybrid-IndoBERT-RTE had an F1-score of 85% and consumed 4.2 times less GPU VRAM. Its training time was up to 44.44 times more efficient than IndoBERT-large-p1, showing an increase in efficiency.  Conclusion: Hybrid-IndoBERT-RTE improved the F1-score and computational efficiency for Indonesian RTE task. These results showed that the proposed model had achieved the aims of the study. Future studies would be expected to focus on adding and increasing the variety of datasets.  Keywords: Textual Entailment, IndoBERT-large-p1, Feature-rich classifiers, Hybrid-IndoBERT-RTE, Deep learning, Model efficiency
Co-Authors Kristijono, Agus Melati, Dian Nuraini Prawiradisastra, Firman Prihartanto, Prihartanto Santoso, Eko Widi Tandi, Teuku Yusransyah Taufik Fuadi Abidin Tejakusuma, Iwan Gunawan