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All Journal Jurnal Sains dan Teknologi Mitigasi Bencana Jurnal Sistem Cerdas
Hammam Riza
BPPT
Automotive Engineering Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering

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UTILIZATION OF ARTIFICIAL INTELLIGENCE TO IMPROVE FLOOD DISASTER MITIGATION Hammam Riza; Eko Widi Santoso; Iwan Gunawan Tejakusuma; Firman Prawiradisastra; 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 | 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.
Kaji Terap Kecerdasan Buatan di Badan Pengkajian dan Penerapan Teknologi Hammam Riza; Anto Satriyo Nugroho; Gunarso
Jurnal Sistem Cerdas Vol. 3 No. 1 (2020): Artificial Intelligence untuk Indonesia
Publisher : APIC

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (852.284 KB) | DOI: 10.37396/jsc.v3i1.60

Abstract

BPPT mulai melakukan penelitian dan pengembangan di bidang kecerdasan buatan sejak tahun 1987 yaitu dengan keterlibatannya dalam proyek sistem mesin penerjemah multi bahasa yang disponsori oleh pemerintah Jepang. Penelitian di bidang mesin penerjemah ini terus berlanjut seiring dengan keterlibatan BPPT dalam beberapa proyek sesudahnya, antara lain proyek UNL, PAN Localization, ASEAN-MT, dan U-STAR.Beberapa metode pun telah digunakan dalam pembuatan sistem mesin penerjemah, dari penggunaan metode Interlingua yang berbasis aturan, berbasis statistik, sampai dengan metode sequence-to-sequenceyang menggunakan deep learning. Di bidang pemrosesan bahasa alami lainnya, BPPT juga melakukan riset dalam bidang pengenalan wicara atau ASR (Automatic Speech Recognition) yang telah menghasilkan produk komersial Perisalah yang berfungsi untuk mencatat segala bentuk pembicaraan di dalam rapat dan membuat notulensi secara cepat. Di bidang pembangkit wicara atau TTS (Text-to-Speech) BPPT telah memulai risetnya sejak tahun 2001 yang saat itu masih menggunakan metode diphone concatenation hingga saat ini menggunakan metode end-to-end.Selain riset di bidang teknologi pemrosesan alami, BPPT juga melakukan penelitian aplikasi kecerdasan buatan dalam pengolahan citra. Antara lain pengembangan sistem diagnosis Malaria, identifikasi individu memakai sidik jari, selaput pelangi, maupun wajah. Penelitiandi bidang biometrik ini seiring dengan tugas BPPT melakukan pendampingan Kementrian Dalam Negeri dalam implementasi KTP elektronik. Selain itu BPPT juga melakukan layanan pengujian KTP-elektronik bagi industri dalam negeri dari sisi teknologi kartu cerdas dan teknologi biometrik. BPPT juga turut mempersiapkan perancangan standar nasional biometrik (SNI) untuk pertukaran data, misalnya format penyimpanan data sidik jari pada chip KTP elektronik. Pada tahun 2019 BPPT memiliki Pusat Unggulan Iptek Biometrik yang menggalang kegiatan litbangyasa maupun layanan teknologi di bidang biometrik untuk kemandirian bangsa.
Analisis Penyebaran dan Komparasi Skenario Kebijakan Penanggulangan Covid-19 berbasis Sistem Dinamik Adi Akhmadi Pamungkas; Hammam Riza; Arwanto; Sri Handoyo Mukti
Jurnal Sistem Cerdas Vol. 3 No. 3 (2020): Kecerdasan Artifisial pada Rekayasa Biomedis
Publisher : APIC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37396/jsc.v3i3.74

Abstract

The emergence of a new variant of the coronavirus, SARS-Cov-2, which causes the Corona Virus Disease (Covid-19) outbreak has really changed the world. First reported in Wuhan City, Hubei Province, China at the end of 2019, this virus has spread throughout the world. Apart from hitting the world economy, the Covid-19 pandemic has also changed the way humans interact. All over the world, people have changed their habits of work, worship, and social activities. This was done to reduce the risk of transmission of the massive new coronavirus. But the next question arises: when will conditions improve? when will this Covid-19 outbreak subside? To answer this question, this study seeks to model the spread of the new Corona Virus with a Dynamic Systems approach. In the modelling carried out, there are seven scenarios that describe the policies undertaken to mitigate the spread of Covid-19 which include WFH policies, office vacations, social distancing, implementation of PSBB, to PSBB relaxation. The resulting model is then validated with data from the Covid-19 Handling Acceleration Task Force which is released every day. Of the seven modelled scenarios, the fastest pandemic relief time is predicted to occur on September 25, 2020, as indicated by scenario 0 with a prediction of a total of 530,655 positive cases. The longest pandemic relief time is predicted to occur on July 17, 2021, with a prediction of a total of 269,115 positive cases.
Co-Authors Adi Akhmadi Pamungkas Anto Satriyo Nugroho Arwanto Eko Widi Santoso Firman Prawiradisastra Gunarso Iwan Gunawan Tejakusuma Prihartanto Prihartanto Sri Handoyo Mukti