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All Journal Jurnal Ilmiah Universitas Batanghari Jambi Antivirus : Jurnal Ilmiah Teknik Informatika
M Shochibul Burhan
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Agriculture, Biological Sciences & Forestry Civil Engineering, Building, Construction & Architecture Computer Science & IT Economics, Econometrics & Finance Education Law, Crime, Criminology & Criminal Justice

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Pemodelan Shift Kerja dalam Proyek Konstruksi menggunakan NetLogo dalam meminimalkan Penyebaran Covid-19 Sandi Wahyudiono; Azhar Adi Darmawan; M Shochibul Burhan
Jurnal Ilmiah Universitas Batanghari Jambi Vol 22, No 2 (2022): Juli
Publisher : Universitas Batanghari Jambi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33087/jiubj.v22i2.2465

Abstract

Due to a shift in habits from face-to-face to virtual activities, the Covid-19 pandemic in Indonesia has affected the way people interact with each other and the environment. However, other activities, such as in the construction industry, are not digital. In Indonesia, the outbreak has had a significant impact on construction projects. Due to the pandemic, construction projects may involve local workers mixed with migrant workers, which could potentially result in new clusters of Covid-19 spread. This study presents an Agent Based Modeling (ABM) by using NetLogo approach to assess the impact of using multiple shifts to reduce the spread of Covid-19. ABM is a simulation model that describes individuals (agents) in a complex and dynamic system. This model is based on literature data to replicate worker behavior across shifts and simulate scenarios using various alternatives during a pandemic. Therefore, estimates and scenarios are needed to realize the Covid-19 simulation among construction projects. From the results of modeling using Netlogo, it can be seen that when shifts are divided into several scenarios, the number of hecalthy workers can increase compared to only one shift. The best scenario is to distribute 30% of project workers into night shift. By implementing various alternative work shifts, it is expected that they can contribute to minimizing the spread of Covid-19 among construction workers which can be implemented by the Project Manager.
Prediksi Turunnya Hujan Menggunakan Algoritma Naïve Bayes Dan K-Means Di Wilayah Malang M Shochibul Burhan; Mohamad Nur Cholis; Achmad Latifudin; Mahendra Widikara Tri Nugroho
Antivirus : Jurnal Ilmiah Teknik Informatika Vol 17 No 2 (2023): November 2023
Publisher : Universitas Islam Balitar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35457/antivirus.v17i2.3205

Abstract

Rain is beneficial for agriculture and also for water reserves, but rain can also have negative impacts such as floods, disease outbreaks and other disasters if not handled properly, therefore to overcome or avoid the problem of excessive rainwater falling It is better to make predictions as an effort to prevent disasters that will occur. Rain will be able to be handled well if you are able to predict when the rain will fall, so that early mitigation can be carried out. With a prediction method using the Naïve Bayes algorithm, rainfall can be predicted because this method uses a probability approach for each data. The result of this method is that it is able to predict the fall of rain by 80%, so the Naïve Bayes method is able to predict when it will rain accurately, where this value comes from 10 test data results for the Malang Raya area. The incorrect value is 20% which is relatively small and this value is quite accurate as a basis for the probability of rain in the Malang Raya area.
Co-Authors Achmad Latifudin Azhar Adi Darmawan Mahendra Widikara Tri Nugroho Mohamad Nur Cholis Sandi Wahyudiono