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All Journal International Journal of Electrical and Computer Engineering International Journal of Informatics and Communication Technology (IJ-ICT) Bulletin of Electrical Engineering and Informatics Seminar Nasional Informatika (SEMNASIF) JURNAL ISTEK Proceeding of the Electrical Engineering Computer Science and Informatics
Winarko, Edi
Computer Science And Electronics Department, Faculty Of Mathematics And Natural Sciences Universitas Gadjah Mada, Yogyakarta
Computer Science & IT Electrical & Electronics Engineering

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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.
A hybrid recommender system based on customer behavior and transaction data using generalized sequential pattern algorithm Ramos Somya; Edi Winarko; Sigit Priyanta
Bulletin of Electrical Engineering and Informatics Vol 11, No 6: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i6.4021

Abstract

In the future, the quality of product suggestions in online retailers will influence client purchasing decisions. Unqualified product suggestions can result in two sorts of errors: false negatives and false positives. Customers may not return to the online store as a result of this. By merging sales transaction data and consumer behavior data in clickstream data format, this work offers a hybrid recommender system in an online store utilizing sequential pattern mining (SPM). Based on the clickstream data components, the product data whose status is only observed by consumers is assessed using the simple additive weighting (SAW) approach. Products with the two highest-ranking values are then coupled with product data that has been purchased and examined in the SPM using the generalized sequential pattern (GSP) method. The GSP algorithm produces rules in a sequence pattern, which are then utilized to construct product suggestions. According to the test results, product suggestions derived from a mix of sales transaction data and consumer behavior data outperform product recommendations generated just from sales transaction data. Precision, recall, and F-measure metrics values rose by 185.46, 170.83, and 178.43%, respectively.
Co-Authors Agus Harjoko Agus Harjoko Anak Agung Istri Ngurah Eka Karyawati Ani Dijah Rahajoe Aniati Murni Arymurthy Arif Rahman Arif Rahman Azhari Azhari Azhari, Azhari Dian Widiyanto Chandra Edhy Sutanta (Jurusan Teknik Informatika IST AKPRIND Yogyakarta) Edy Winarno Jumadi Jumadi Khabib Mustofa Khabis Mustofa Kristoko Dwi Hartomo Mila Chrismawati Paseleng Moh. Edi Wibowo Muhammad Davi Ramos Somya Retantyo Wardoyo Sigit Priyanta Sri Yulianto Joko Prasetyo Wibowo, Moh. Edi Wiranto Wiranto