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All Journal Prosiding Seminar Nasional Official Statistics International Journal of Informatics Engineering and Computing
Nur Silviyah Rahmi
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Humanities Computer Science & IT Economics, Econometrics & Finance Social Sciences

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MAPPING TINGKAT KERAWANAN COVID-19 DAN FAKTOR AKSELERATOR PENYEBARAN VIRUS ANTAR DAERAH DI INDONESIA DENGAN MENGGUNAKAN ANALISIS CLUSTER HIERARKI Nur Silviyah Rahmi
Seminar Nasional Official Statistics Vol 2020 No 1 (2020): Seminar Nasional Official Statistics 2020
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (277.824 KB) | DOI: 10.34123/semnasoffstat.v2020i1.512

Abstract

Currently the corona virus pandemic is forcing the government to formulate and implement unusual policies. Moreover, the impact of this pandemic was not only felt by the health sector, but also hit other sectors such as the economy, education, social and tourism. To prevent a bigger impact, it is necessary to group the regions based on the factors that accelerate the spread of the corona virus so that appropriate policies can be formulated for each region. It is because the demographic and cultural conditions between regions in Indonesia are different. Based on this background, the purpose of this research is to find out a map of the level of pandemic vulnerability in each area accompanied by the accelerator factor of the spread of the corona virus using the hierarchical cluster analysis method. Hierarchical cluster analysis is a multivariate technique for grouping objects based on their characteristics. Cluster analysis classifies objects so that each object that has similar properties will be grouped into one cluster. The accelerator factors referred are age, hand washing kebiasaans, work activities, and aspects of traveling. The results show that the 4 variables accelerating of the virus spread, are need attention. The provinces of Papua, West Papua, Riau, and Riau Islands are in the safe category, but from the age factor of the population, these areas are still classified as alert. This means that if the age factor is not given special attention, the four provinces have the potential to increase their status to be alert or even vulnerable. The results of this analysis are expected to provide information regarding the state of vulnerability of each region and can serve as suggestions for the government in making appropriate policies.
Automatic Detection of Cabbage Pest Attacks Based on Leaf Images with Machine Learning Approach Ni Wayan Surya Wardhani; Prayudi Lestantyo; Atiek Iriany; Nur Silviyah Rahmi
International Journal of Informatics Engineering and Computing Vol. 2 No. 2 (2025): International Journal of Informatics Engineering and Computing [Preview]
Publisher : ASTEEC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70687/3szcd282

Abstract

Farmers in cabbage farming face many problems, one of which is pest attack. Plutella xylostella L. is a major pest on cabbage (known as cabbage leaf caterpillar) which can cause a decrease in production of up to 100 percent. Decision Support System (DSS) was developed to classify the attack rate of Plutella to reduce the negative effects of using various types of high doses of pesticides and short spraying intervals but causing residual effects and killing natural enemies. DSS has a role in helping farmers to make decisions regarding the time of pesticide treatment needed to minimize negative effects and increase productivity. In this study, DSS was developed to detect damage to cabbage (Brassica oleracea L) crops so that farmers can determine pesticide doses and spraying intervals based on a website. The results of the system is presented in the form of images and the percentage of damage to cabbage plants. Therefore, the CART method can be used to analyze the level of damage to plants that are attacked by pests.
Co-Authors Atiek Iriany NI WAYAN SURYA WARDHANI Prayudi Lestantyo