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Indra Ranggadara
Mercu Buana University
Computer Science & IT

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Augmented reality using features accelerated segment test for property catalogue Rudy Setyadi; Indra Ranggadara
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 1: February 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v18i1.13039

Abstract

Promotional media used in the marketing of housing using catalogs that display 2D images of houses from one side of the house make potential customers unable to imagine the design of all parts of the house. Augmented Reality can be used as an interactive marketing media so that it can be used to display homes in 3D so that they appear more real from all sides so that prospective customers can consider the type of house to be chosen. Development of this application using the Multimedia Development Life Cycle. Application development uses the FAST algorithm as detection of home catalog markers to define how well images can be detected and tracked. The FAST algorithm will calculate every pixel on the target image in determining the corner when scanning the home catalog then it will produce a 3D object home to see the real shape design of the house.
Comparative study of extraction features and regression algorithms for predicting drought rates Irza Hartiantio Rahmana; Amalia Rizki Febriyani; Indra Ranggadara; Suhendra Suhendra; Inna Sabily Karima
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 20, No 3: June 2022
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v20i3.23156

Abstract

Rice is the primary staple food source for Indonesian people, with consumption increasing so that rice production needs to be increased. Rice drought is one of the problems that can hamper rice production. This research aims to determine the best extraction feature between the normalized difference vegetation index (NDVI) and the normalized difference water index (NDWI) in describing rice fields’ dryness. Moreover, using the random forest regression algorithm. This research compares NDVI with NDWI using data originating from Sentinel-2A and retrieved via the google earth engine. Regression algorithms are used in research to predict drought in paddy fields. This research shows that NDVI is better than NDWI in predicting drought using random forest regression algorithms and logistic regression algorithms. The random forest regression algorithm based on the results obtained shows that the average root mean square error (RMSE) on NDVI is 0.018, and NDWI is 0.012. Based on the logistic regression algorithm results, it was found that the average value of RMSE on NDVI was 0.346, and NDWI was 0.336. Based on the results of the RMSE, it shows that the forecasting ability of the random forest regression algorithm is better than the logistic regression.
Co-Authors Amalia Rizki Febriyani Inna Sabily Karima Irza Hartiantio Rahmana Rudy Setyadi Suhendra Suhendra