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All Journal Jurnal Informatika dan Rekayasa Perangkat Lunak i-Latinnotary Journal: Internasional Journal of Latin Notary
Darwis Darwis
Universitas Prima Indonesia
Humanities Astronomy Computer Science & IT Education Law, Crime, Criminology & Criminal Justice Social Sciences Other

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IMPLEMENTATION OF THE IMPOSITION OF RESTRICTIONS ON EMERGENCY COMMUNITY ACTIVITIES FOR CORONA VIRUS DISEASE (PPKM) IN RELATION TO POSITIVE LAW IN INDONESIA Elvira Fitriani Pakpahan; Tommy Leonard; Darwis Darwis
International Journal of Latin Notary Vol. 2 No. 1 (2021): Internasional Journal of Latin Notary, Vol. 2, No. 1, September 2021
Publisher : Magister Kenotariatan Universitas Pasundan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55904/journal.v2i1.31

Abstract

This study aims to analyze the legal basis for implementing Emergency PPKM and sanctions if the community violates Emergency PPKM. This research is a type of normative juridical law research. To obtain the data needed in this study, literature research was used and then analyzed qualitatively by collecting primary, secondary and tertiary legal materials related to research. Based on the results of data analysis, the results obtained: The legal basis for implementing Emergency PPKM based on the perspective of the Government of the Republic of Indonesia is the Law of the Republic of Indonesia Number: 4 of 1984 concerning Outbreaks of Infectious Diseases in Article 1 to Article 6 while sanctions are regulated in Article 14 paragraph (1) to paragraph (3) and the Law of the Republic of Indonesia Number: 6 of 2018 concerning Health Quarantine in Article 9 while sanctions are only given to the captain, pilot captain and person in charge of land transportation with the overall implementation regulated in government regulations where government regulations are statutory regulations determined by the President to carry out the law properly, The benefits of the Emergency PPKM being implemented are to suppress the spread of the covid-19 virus in certain activities or in non-essential activities as a form of follow-up to the direction of the President of the Republic of Indonesia because people do not understand the importance of implementing health protocols so that there is an increase in people exposed to the corona virus every day, The sanctions given if the public violates the Emergency PPKM do not have a strong legal basis and the issuance of the Instruction of the Minister of Home Affairs is not appropriate because Indonesia is still in a state of emergency based on Presidential Decree Number: 11 of 2020 concerning the Determination of the Covid-19 Public Health Emergency so that emergency handling must refer to Presidential Decree Number: 11 of 2020 concerning the Determination of the Covid-19 Public Health Emergency or through a Government Regulation.
Classification Model Analysis of ICU Mortality Level using Random Forest and Neural Network Lymin Lymin; Alvin Alvin; Bodhi Lhoardi; Darwis Darwis; Joseph Siahaan; Abdi Dharma
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol 5, No 2 (2023): September
Publisher : Universitas Wahid Hasyim

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36499/jinrpl.v5i2.8749

Abstract

Based on the results of previous studies, research on machine learning for predicting ICU patients is crucial as it can aid doctors in identifying high-risk individuals. A high accuracy in machine learning models is necessary for assisting doctors in making informed decisions. In this study, machine learning models were developed using two models, namely Random Forest and Artificial Neural Network (ANN), to predict patient mortality in the ICU. Patient data was obtained from The Global Open Source Severity of Illness Score (GOSSIS) and underwent preprocessing to address issues of missing values and imbalanced data. The data was then divided into training, validation, and testing sets for model training and evaluation. The results of the study indicate that the Random Forest model performs better with an accuracy of 93% on the testing data compared to the ANN which only achieved an accuracy of 86% on the testing data. Consequently, the Random Forest model can be utilized as a solution for predicting patient mortality in the ICU.
Co-Authors Abdi Dharma Alvin Alvin Bodhi Lhoardi Elvira Fitriani Pakpahan Joseph Siahaan Lymin Lymin Tommy Leonard