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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Ilmu dan Teknologi Kelautan Tropis IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Jurnal Informatika Jurnal Simetris Elkom: Jurnal Elektronika dan Komputer Bulletin of Electrical Engineering and Informatics Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) JTSL (Jurnal Tanah dan Sumberdaya Lahan) Jurnal Transformatika Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika Sinkron : Jurnal dan Penelitian Teknik Informatika INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi JURNAL MEDIA INFORMATIKA BUDIDARMA Faktor Exacta Jurnal Ilmiah Matrik JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Indonesian Journal of Computing and Modeling J-SAKTI (Jurnal Sains Komputer dan Informatika) JURIKOM (Jurnal Riset Komputer) JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Building of Informatics, Technology and Science Journal Sensi: Strategic of Education in Information System JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) TIN: TERAPAN INFORMATIKA NUSANTARA Aiti: Jurnal Teknologi Informasi Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Jurnal Teknik Informatika (JUTIF) Journal of Information Technology (JIfoTech) J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Info Sains : Informatika dan Sains Jurnal Nasional Teknik Elektro dan Teknologi Informasi IT-Explore: Jurnal Penerapan Teknologi Informasi dan Komunikasi Jurnal Informatika: Jurnal Pengembangan IT Jurnal Indonesia : Manajemen Informatika dan Komunikasi Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Sri Yulianto Joko Prasetyo
Universitas Kristen Satya Wacana
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Journal : Bulletin of Electrical Engineering and Informatics

 1 
Satellite imagery and machine learning for aridity disaster classification using vegetation indices Sri Yulianto Joko Prasetyo; Kristoko Dwi Hartomo; Mila Chrismawati Paseleng; Dian Widiyanto Chandra; Edi Winarko
Bulletin of Electrical Engineering and Informatics Vol 9, No 3: June 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1593.071 KB) | DOI: 10.11591/eei.v9i3.1916

Abstract

Central Java Province is one of provinces in Indonesia that has a high aridity risk index. Aridity disaster risk monitoring and detection can be done more accurately in larger areas and with lower costs if the vegetation index is extracted from the remote sensing imagery. This study aims to provide accurate aridity risk index information using spectral vegetation index data obtained from LANDSAT 8 OLI satellite. The classification of drought risk areas was carried out using k-nn with the Spatial Autocorrelation method. The spectral vegetation indices used in the study are NDVI, SAVI, VHI, TCI and VCI. The results show a positive correlation and trend between the spectral vegetation index influenced by seasonal dynamics and the characteristics of the High R.A. and Middle R.A. drought risk areas. The highest correlation coefficient is SAVI with a High R.A. amounted to 0.967 and Middle R.A. amounted to 0.951. The results of the Kappa accuracy test comparison show that SVM and k-nn have the same accuracy of 88.30. The result of spatial prediction using the IDW method shows that spectral vegetation index data that initially as an outlier, using the k-nn method, the spectral vegetation index data can be identified as data in the aridity classification. The spatial connectivity test among sub-districts that experience drought was done using Moran’s I Analysis.
Computer model for tsunami vulnerability using sentinel 2A and SRTM images optimized by machine learning Sri Yulianto Joko Prasetyo; Bistok Hasiholan Simanjuntak; Kristoko Dwi Hartomo; Wiwin Sulistyo
Bulletin of Electrical Engineering and Informatics Vol 10, No 5: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i5.3100

Abstract

This study aims to develop a software framework for modeling of tsunami vulnerability using DEM and Sentinel 2 images. The stages of study, are: 1) extraction Sentinel 2 images using algorithms NDVI, NDBI, NDWI, MSAVI, and MNDWI; 2) prediction vegetation indices using machine learning algorithms. 3) accuracy testing using the MSE, ME, RMSE, MAE, MPE, and MAPE; 4) spatial prediction using Kriging function and 5) modeling tsunami vulnerability indicators. The results show that in 2021 the area was dominated by vegetation density between (-0.1-0.3) with moderate to high vulnerability and risk of land use tsunami as a result of the decreasing of vegetation. The prediction results for 2021 show a low canopy density of vegetation and a high degree of land surface slope. Based on the prediction results in 2021, the study area mostly shows the existence of built-up lands with a high tsunami vulnerability risk (more than 0.1). Vegetation population had decreased to 67% from the original areas in 2017 with an area of 135 km2. Forest vegetation had decreased by 45% from 116 km2 in 2017. Land use for fisheries had increased to the area of 86 km2 from 2017 with an area of 24 km2.
A machine learning-based computer model for the assessment of tsunami impact on built-up indices using 2A Sentinel imageries Joko Prasetyo, Sri Yulianto; Simanjuntak, Bistok Hasiholan; Susatyo, Yeremia Alfa; Sulistyo, Wiwin
Bulletin of Electrical Engineering and Informatics Vol 13, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i2.5910

Abstract

This study aims to build a computer model to detect built-up land in the identified tsunami hazard zone based on Sentinel 2A imagery using the normalized built up area index (NBI), urban index (UI), normalize difference build-up index (NDBI), a modified built-up index (MBI), index-based builtup index (IBI) algorithms, optimized with machine learning Random Forest (RF) and extreme gradient boosting (XGboost) algorithms and the spatial patterns are predicted using the ordinary kriging (OK) method. Testing of the accuracy of the classification and optimization results was performed using the Kohen Kappa and overall accuracy functions. The results of the study show that a built-up land consisting of open land and water, settlements, industry areas, and agriculture and tourism areas can be identified using the parameters of built-up indices. The accuracy testings that were performed using overall accuracy and Kohen Kappa methods show that classification and prediction are highly accurate using XGboost machine learning, namely 91%. This study produces a novelty of finding, namely a computer model to detect and predict the spatial distribution of built-up land in 4 scales, i.e., very low, low, high, and very high based on NBI, UI, NDBI, MBI, IBI data extracted from Sentinel 2A imagery.
Co-Authors Adenia Kusuma Dayanthi Anna Simatauw Antar Maramba Jawa Antonius Mbay Ndapamury Ardian Ariadi Ardito Laksono Suryoputro Arit Imanuel Meha Arvira Yuniar Isnaeni Ayuningtyas, Fajar Baali, Gabriel Megfaden Kenisa Baronio, Nodas Constantine Bintang Lazuardi Bistok Hasiholan Simanjuntak Brian Laurensz Brilliananta Radix Dewana Bunga, Alex Frianco Cahyaningtyas, Christian Charitas Fibriani Christanto, Erwien Christiana Ari Setyaningrum Daniel HF Manongga Danny Manongga Danny Sebastian Devianto, Yudo Dian Widiyanto Chandra Dwi Hayati Edwin Zusrony Eko Sediyono Elvira Umar Engles Marabangkit Yoesmarlan Erik Wahyu Abdi Nugroho Evan Bagus Kristianto Evan Geraldy Suryoto Evi Maria Fabian Valerian Feibe Lawalata Florentina Tatrin Kurniati Gallen cakra adhi wibowo Gideon Bartolomeus Kaligis Gilbert Yesaya Likumahua Gudiato, Candra Haikal Nur Rachmanrachim Achaqie Haikal Nur Rachmanrachim Achaqie Hindriyanto Dwi Purnomo Ida Ayu Putu Sri Widnyani Indra Yunanto Irdha Yunianto Irwan Sembiring Isnaeni, Arvira Yuniar Josua Josen Alexander Limbong Kase, Celomitha Putri Welhelmina Kristia Yuliawan Kristoko Dwi Hartomo Kurnia Latifatul Nazila Laurentius Kuncoro Probo Saputra Lobo, Murry Albert Agustin Lyonly Evany Tomasoa Maipauw, Musa Marsel Maya Sari Merryana Lestari Mikhael Dio Eclesi Mila Chrismawati Paseleng Mira Mira Muhamad Yusup Muhammad Rizky Pribadi Muhammad Sholikhan Nadia Renatha Yuwono Nadya Inarossy Novem Berlian Uly Nugroho, Ignatius Dion Nusantara, Bandhu Otniel, Marcelinus Vito Patrick Simbolon Permatasari, Aurilia Dinda Petty, Holbed Joshua Praditya, Al-Farrel Raka Prayitno, Gunawan Priyadi Priyadi Purwoko, Agus Qurotul Aini Ratu, Herman Huki Ravensca Matatula Raymond Elias Mauboy Riko Yudistira Rina Pratiwi Pudja I. A Rohmad Abidin, Rohmad Rony, Zahara Tussoleha Roy Rudolf Huizen Santoso, Nuke Puji Lestari Septian Silvianugroho Septio, Pius Aldi Solly Aryza Sri Hartati Stanny Dewanty Rehatta Stevanus Dwi Istiavan Mau Supit, Christanti Ekkelsia Suryasatria Trihadaru Suryasatriya Trihandaru Susatyo, Yeremia Alfa Sutarto Wijono Theopillus J. H. Wellem Tirsa Ninia Lina Triloka Mahesti Triloka Mahesti Untung Rahardja Valentino Kevin Sitanayah Que Vinsensius Aprila Kore Dima Wahani, Puteri Justia Kardia Momuat Wasis Pancoro Wicaksono, Muhammad Ryqo Jallu Winarko, Edi Wiwin Sulistyo Yansen Bagas Christianto Yerik Afrianto Singgalen Yesi Arumsari Yohanes Aji Priambodo