Lockdown Policy As A Corona Desease (COVID-19) Management Efforts Asked From The Environmental Aspect Of Life Based On Law Act No. 32 Of 2009 Concerning Protection And Management Of Environment

The government has formed the COVID-19 (Task Force) Acceleration Countermeasures Group to discuss strategies to deal with the Corona Virus outbreak. One of Covid-19's coping strategies, namely: Social restrictions in the form of Lock Down with modifications or rules that are clarified and clear in priority areas as of now, but proposals in the form of Lock Down in priority areas such as DKI are not approved by the government. Although in the end the DKI Jakarta Government issued a policy after approval from the central government through the Minister of Health in the form of Governor's Regulation Number 33 Year 2020 concerning the Implementation of Large-Scale Social Debate in Handling Corona Disease 2019 (COVID-19) in the Special Capital Province of Jakarta and Governor Decree Number 380 Year 2020 concerning the Imposition of the Implementation of Large-Scale Social Restrictions in Handling Corona Disease 2019 (COVID-19) in the Special Capital Province of Jakarta. If the lockdown is really implemented, then this effort will indirectly have an impact on the environment, because the policy will relate to space that includes all objects, power, conditions, and living things, including humans and their behavior, which affect nature itself, continuity of life, and the welfare of humans and other living things. Therefore it is necessary to examine the relationship between the lockdown policy and COVID-19 countermeasures in the perspective of the Environmental Protection and Management Law. The author intends to find a connection point between the lockdown policy by looking at the impact it has on the environment by referring to the Law Act No. 32 Of 2009 Concerning Protection And Management Of Environment

Twitter user sentiment analysis for RUU Omnibus Law using convolutional neural network

The general function of social media is for online interaction with many people. Moreover, social media have functions for sharing information, discussion, and giving an opinion media about some topics that a lot of people talk about, one of that media is Twitter. An atopic will show many opinions and different responses from everyone. This study was for making an analysis opinion from social media Twitter user about Rancangan Undang-Undang (RUU) Omnibuslaw topic using a Convolutional Neural Network method wich one of Deep Learning method. This study has been done a sentiment analysis with opinion data from many different people through the tweet they making, Preprocessing and weighting are done using Word2vec which give 84% result accuracy of an algorithm from 10-time testing. Based on 2.820 tweet data, the result is 1.320 data of positive sentiment, and 1.500 data of negative response for the RUU Omnibuslaw topic in Indonesia

Expert system for predicting the early pregnancy with disorders using artificial neural network

Pregnancy is an important moment of growth of the human being. In many case women do not know that she is being pregnant, this is one of the causes of miscarriage. For healthy pregnancy also need to be guarded by knowing abnormalities early in pregnancy. There are several early pregnancy disorder among others hyperemesis gravidarum, pre-eclampsia and eclampsia, hydatidiform mole, and ectopic pregnancy. In this research, we propose an expert system using Artificial Neural Network (ANN) and Back Propagation algorithm for predicting the pregnancy with disorders early. We used 172 medical records of patient with 17 input parameters and 5 output classes, among others normal early pregnancy, and 4 classes for pregnancy disorders. The experiment with training and testing process showed that ANN could be applied to predict the disorders pregnancy with percentage of accuracy around 78,248%. The percentage is got by 0,1 of learning rate value, 17 of neuron input layers, 50 of neuron hidden layers, 5 of neuron output layers, and 0,01 of error value

Sentiment Analysis of the Use of Telecommunication Providers on Twitter Social Media using Convolutional Neural Network

Telecommunication technology continues to develop starting from 1G, 2G, 3G, 4G, and currently entering the 5G era. The Global System for Mobile Communications (GSM) based telecommunication industry in Indonesia consists of three big names: Telkomsel, XL, and Indosat. During the Covid-19 pandemic, activities carried out outside the home should be done online. People hope that the internet network can work properly. However, the reality is not as expected, because many networks are experiencing slow internet problems and many complaints are delivered through social media. Therefore, this research aims to find the insight opinions that have been conveyed to the telecommunications operator in social media. This research used the Convolutional Neural Network (CNN) algorithm to classify text sentiment (negative or positive) about telecommunication providers. The experiment with text data from Twitter is conducted after preprocessing and weighting of the Word2Vec process. The confusion matrix experiment shows that the CNN algorithm's performance reaches an average accuracy value of around 86.22%. The experiment was carried out by dividing the training data and testing the data 5 times in 10 times. The study results indicated that disruption of cellular telecommunications operators provided many sentiments, especially negative sentiment at the beginning of the COVID-19 pandemic

Design of expert system for train operational feasibility with Tsukamoto fuzzy inference system

Train is one of the most favorite mass transportation in the world. In 2017 train in Indonesia can bring about 341.605 passengers to many destinations. PT. Kereta Api Indonesia is a state owned enterprise that has responsibility to make sure that train is safe and works well. As we know that a train has several components to check. It is very difficult to identify whether a train is in a good condition or needs repairing. The purpose of this work is to proposes a model of operational feasibility by several main criteria: bogie, breaking system, boffer, electric coupler, and safety kit. In its experiment phase, this model uses Tsukamoto Fuzzy Inference System to decide that a train is in good condition or need repairing. In evaluation phase we compare this model with traditional method and this model shows exactly 99% of the same result. It is suggested for further work to include several methods such as Tsugeno and Mamdani fuzzy inference system

Design of expert system for train operational feasibility with Tsukamoto fuzzy inference system

Train is one of the most favorite mass transportation in the world. In 2017 train in Indonesia can bring about 341.605 passengers to many destinations. PT. Kereta Api Indonesia is a state owned enterprise that has responsibility to make sure that train is safe and works well. As we know that a train has several components to check. It is very difficult to identify whether a train is in a good condition or needs repairing. The purpose of this work is to proposes a model of operational feasibility by several main criteria: bogie, breaking system, boffer, electric coupler, and safety kit. In its experiment phase, this model uses Tsukamoto Fuzzy Inference System to decide that a train is in good condition or need repairing. In evaluation phase we compare this model with traditional method and this model shows exactly 99% of the same result. It is suggested for further work to include several methods such as Tsugeno and Mamdani fuzzy inference system

Decision support system for football player's position with tsukamoto fuzzy inference system

Nowadays, football is one of the most famous sports in the world. Many football clubs and football academies have been established in Indonesia. In football academy, each player will be trained and selected to get the best positon in the team formation. In fact, each player has a different ability and skill. If a player gets a correct position, he can open the opportunity for his team to win a competition. This condition absolutely gives a good impact for the team. However, it will be a serious problem if a player plays in an incorrect position. The player's best position can be deciding by his own ability and skill. This study proposes the selection model of player's position by understanding a player's speed, stamina, strength, and other skills that covering, shooting, passing, dribble, and header with Tsukamoto Fuzzy Inference System. A player may have the following positions: central forward, midfielder, winger, goal keeper. In evaluation phase, this model exactly shows 52.17% accurate value. This means that the model decreases the misplacement of player's position. It is recommended for further study to make some additional criteria such as player's emotion, attitude, etc in order to increase accurate

Decision support system for football player's position with tsukamoto fuzzy inference system

Nowadays, football is one of the most famous sports in the world. Many football clubs and football academies have been established in Indonesia. In football academy, each player will be trained and selected to get the best positon in the team formation. In fact, each player has a different ability and skill. If a player gets a correct position, he can open the opportunity for his team to win a competition. This condition absolutely gives a good impact for the team. However, it will be a serious problem if a player plays in an incorrect position. The player's best position can be deciding by his own ability and skill. This study proposes the selection model of player's position by understanding a player's speed, stamina, strength, and other skills that covering, shooting, passing, dribble, and header with Tsukamoto Fuzzy Inference System. A player may have the following positions: central forward, midfielder, winger, goal keeper. In evaluation phase, this model exactly shows 52.17% accurate value. This means that the model decreases the misplacement of player's position. It is recommended for further study to make some additional criteria such as player's emotion, attitude, etc in order to increase accurate