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All Journal JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI Bulletin of Electrical Engineering and Informatics JAMBURA JOURNAL OF PROBABILITY AND STATISTICS
Raditya Novidianto
Institut Teknologi Sepuluh Nopember
Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Environmental Science Mathematics Social Sciences

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3D Reconstruction using convolution smooth method Sofyan Arifianto; Hardianto Wibowo; Wildan Suharso; Raditya Novidianto; Dani Harmanto
Bulletin of Electrical Engineering and Informatics Vol 10, No 3: June 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i3.1991

Abstract

3D imagery is an image with depth data. The use of depth information in 3D images still has many drawbacks, especially in the image results. Raw data on the 3D camera even does not look smooth, and there is too much noise. Noise in the 3D image is in the form of imprecise data, which results in a rough image. This research will use the convolution smooth methods to improve the 3D image. Will smooth noise in the 3D image, so the resulting image will be better. This smoothing system is called the blurring effect. This research has been tested on flat objects and objects with a circle contour. The test results on the flat surface obtained a distance of 1.3177, the test in the object with a flat surface obtained a distance of 0.4937, and the test in circle contour obtained a distance of 0.3986. This research found that the 3D image will be better after applying the convolution smooth method.
MODEL HYBRID NONLINEAR REGRESSION LOGISTIC (NLR) –DOUBLE EXPONENSIAL SMOOTHING (DES) DAN PENERAPANNYA PADA JUMLAH KASUS KUMULATIF COVID-19 DI INDONESIA DAN BELANDA RADITYA NOVIDIANTO
Jambura Journal of Probability and Statistics Vol 2, No 1 (2021): Jambura Journal Of Probability and Statistics
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34312/jjps.v2i1.7757

Abstract

The economic relationship between Indonesia and the Netherlands is a good trade relationship, but the spread of COVID-19 disrupts the two countries' economies. Both countries need to have an explanation regarding the condition of COVID-19 to raise economic market sentiment. Based on this, Hybrid and non-hybrid models are used to predict the dispersion conditions and compare them through the MAPE value. The double-exponential nonlinear logistic regression hybrid model on the cumulative number of COVID-19 is not suitable for use in the Netherlands COVID-19 cases but is suitable for use in the cumulative number of COVID-19 cases Indonesia. The hybrid nonlinear regression logistic-double exponential model is one way to optimize MAPE, especially in training data. Based on the hybrid non-client regression logistic model, the peak incidence of Covid-19 in the Netherlands is estimated at 22 November 2020, and the hybrid nonlinear regression logistic-Double exponential model predicts that the peak of Covid-19 occurs in Indonesia on 28 November 2020. the Netherlands wave is around 2.83 percent and Indonesia 1.62 percent. Therefore the decline in Indonesia is predicted to be faster, but the Netherlands will reach the peak of the Indonesian news wave.
Analisis Bicluster Algoritma CC untuk Mengidentifikasi Pola Rawan Pangan di Indonesia Raditya Novidianto; Rini Irfani
Jurnal Matematika, Statistika dan Komputasi Vol. 17 No. 2 (2021): JANUARY 2021
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/jmsk.v17i2.12057

Abstract

Indonesia is known as an agricultural country. This means that most of the population work in the agricultural sector related to food. However, food insecurity still occurs in Indonesia. With the COVID-19 pandemic, the Food and Agriculture Organization (FAO) stated that there was a threat of food scarcity which had an impact on food insecurity conditions. This would undermine the second goal of the SDGs, which is to end hunger and create sustainable agriculture. The purpose of this study was to determine the spatial pattern of food insecurity in each province in Indonesia using the bicluster method. The data used are data from Susenas and Sakernas by BPS in 2019. Several studies show that the bicluster method with the CC algorithm shows that each province group has a different characteristic pattern. In the bicluster approach, the researcher runs parameter tuning to select the best parameter based on the Mean Square Residual in Volume (MSR / V). The CC algorithm tries to get a bicluster with a low MSR value, therefore the best parameter is the one that produces the smallest MSR / V value, in this study the smallest MSR / V is 0,01737 with δ = 0,01. The application of the CC biclustering algorithm to the food insecurity structure in Indonesia results in 5 bicluster. Bicluster 1 consists of 15 provinces with 8 variables, Bicluster 2 consists of 10 provinces with 5 variables, Bicluster 3 consists of 3 provinces with 7 variables, Bicluster 4 consists of 4 provinces with 4 variables and Bicluster 5 consists of 2 provinces with 5 variables. Biculster 4 represents a cluster of food insecurity areas with the characteristics of the bicluster P0, P1, P2 and calorie consumption of less than 1400 KKAL.
Co-Authors Dani Harmanto Hardianto Wibowo Rini Irfani Sofyan Arifianto Wildan Suharso