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All Journal International Journal of Electrical and Computer Engineering Jurnal Teknoin JURNAL SISTEM INFORMASI BISNIS Jurnal Buana Informatika Bulletin of Electrical Engineering and Informatics Journal of Education and Learning (EduLearn) Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) JUTI: Jurnal Ilmiah Teknologi Informasi Jurnal Algoritma Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Transformatika Journal of Information Systems Engineering and Business Intelligence Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika Register: Jurnal Ilmiah Teknologi Sistem Informasi InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan Sistemasi: Jurnal Sistem Informasi Journal of Applied Geospatial Information JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL MEDIA INFORMATIKA BUDIDARMA Information System for Educators and Professionals : Journal of Information System SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal Sisfokom (Sistem Informasi dan Komputer) GUIDENA: Jurnal Ilmu Pendidikan, Psikologi, Bimbingan dan Konseling Indonesian Journal of Computing and Modeling JURIKOM (Jurnal Riset Komputer) Jurnal Informatika JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Journal of Information Systems and Informatics Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Jurnal Abdi Insani Abdimasku : Jurnal Pengabdian Masyarakat Aiti: Jurnal Teknologi Informasi Budapest International Research and Critics Institute-Journal (BIRCI-Journal): Humanities and Social Sciences Jurnal Teknik Informatika (JUTIF) Journal of Applied Data Sciences JOINTER : Journal of Informatics Engineering IJECS: Indonesian Journal of Empowerment and Community Services International Journal of Engineering, Science and Information Technology International Journal of Community Service Jurnal Impresi Indonesia Jurnal Nasional Teknik Elektro dan Teknologi Informasi Jurnal Algoritma Magistrorum et Scholarium: Jurnal Pengabdian Masyarakat Jurnal Rekayasa elektrika Jurnal INFOTEL Scientific Journal of Informatics JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Kristoko Dwi Hartomo
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.
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.
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.
BiLSTM OptiFlow: an enhanced LSTM model for cooperative financial health forecasting Maria, Evi; Wahyono, Teguh; Dwi Hartomo, Kristoko; Purwanto, Purwanto; Arthur, Christian
Bulletin of Electrical Engineering and Informatics Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i3.8653

Abstract

This paper presents bidirectional long short-term memory (BiLSTM) OptiFlow, an optimized deep learning model designed to predict the financial health of cooperatives using key financial ratios: debt to equity ratio (DER), net profit margin (NPM), and return on equity (ROE). By leveraging a BiLSTM architecture combined with an optimal decayed learning rate, this model aims to enhance forecasting accuracy. The proposed model was tested against three established methods—recurrent neural network (RNN), long short-term memory (LSTM), and gated recurrent unit (GRU)—and evaluated using mean absolute error (MAE), mean absolute percentage error (MAPE), and mean squared error (MSE) metrics. Results indicate that BiLSTM OptiFlow outperforms the other models across all key indicators. This research offers a robust approach to cooperative financial forecasting, with significant implications for decision-making processes in cooperative management.
Co-Authors Ade Iriani Agata, Kristien Yuni Agus Bambang Nugraha Ahmad Ashifuddin Aqham Alexandra, Andrea Cellista Allu, Roy Armus Andeka Rocky Tanaamah Andriana, Myra Angelia Destriana Anggara Cahya Putra Anthony Y.M. Tumimomor April Firman Daru Ariany Mahastanti, Linda Ariel Kristianto Arthur, Christian Aruperes, Viveca Grivenda Aryanata Andipradana Baali, Gabriel Megfaden Kenisa Bagaskara, Adyatma Andhika Bambang Ismanto Brilliananta Radix Dewana Chandra Husada Danny Manongga Danny Sebastian Dearmelliani Tarigan Desyandri Desyandri Dewi, Stefani Fransisca Dian Widiyanto Chandra Diky Candra Muria Pratama Djoko Hartanto Dwi Anggono Winarso Suparjo Putra Dwi Hosanna Bangkalang Eko Sediyono Enik Muryanti Estie Grace Melisa Sinulingga Evangs Evi Maria Ezra Julang Prasetyo Faudisyah, Alfendio Alif Gerry Santos Lasatira Gladiola Lavinia Ambayu Gogo Krisatyo Hanna Arini Parhusip Hanna Prillysca Chernovita Hindriyanto Dwi Purnomo Hong, Hendry Indrajaya, Denny Irwan Sembiring Joanito Agili Lopo Joanito Agili Lopo Johan Jimmy Carter Tambotoh Joshua Rondonuwu Kamil, Muhammad Farhan Karina Bianca Lewerissa Kevin Benedictus Simarmata Kevin Hendra William Kevin Stevian Hermawan Kezia Sharent Kodoati Kho, Ardi Kuncoro, Wreda Agung Kurniawan, Timothy Arif Limbong, Josua Josen Alexander Linda Ariany Mahastanti Lobo, Murry Albert Agustin Magdalena Ariance Ineke Pakereng Martin Setyawan Martin Teddy Sihite Matheus Supriyanto Rumetna Mila Chrismawati Paseleng Mozad Timothy Waluyan Muflihanto, Ezar Juan Muhammad Rizky Ramadhan Muhammad Sholikhan Neilin Nikhlis Nicolas Evander Suhandi Nina Setiyawati Nining Fitriani nuranto, bogo Nurrokhman Nurrokhman Nuzhah Al Waaidhoh Penidas Fodinggo Tanaem Prakoso, Hendri Suryo Pramudhita Tunjung Seta Prasetyo, Sri Yulianto Prasianto, Kornelius Reinand Purnomo, Andreas Wisnu Adi Purwanto Purwanto Raditya Ditto Aryaputra Radius Tanone Radjawane, Samy Rahmawati, Lutfi Raymond Elias Mauboy Rizaldi, Alexander Sandy Pratama Saputro, Andreas Arga Rinjani Septian Silvianugroho Sinulingga, Yedija Sada Ukurta Sri Yulianto Sri Yulianto Joko Prasetyo Stevan Hamonangan Hardi Suhandi, Nicolas Evander Suharjo, Rahmat Abadi Sulistiawati, Anita Suryasatriya Trihandaru Sutarto Wijono Sutedja, Indrajani T. Arie Setiawan P Takakobi, Michael Richard Teguh Wahyono Theopillus J. H. Wellem Tri Harjani Tri Wahyuningsih Tridinatha, Zenitha Eunike Triloka Mahesti Tumbade, Marcho Oknivan Untung Rahardja Wahab, Nur Haliza Abdul Waliyuddin Rabbani, Imam Wattimena, Nalbraint Wibowo, Mars Caroline Winarko, Edi Wiwien Hadikurniawati Yessica Nataliani Yohan Maurits Indey