ARE YOU SURE THAT WE NEED RECLAMATION?

Muhammad Abdul Rohman (26)

A. Background 

Reclamation is the process of claiming something back or of a right. (oxford dictionary). In the context of urban development, reclamation is an effort to make efforts to rebuild the urban area to expand the size of the city. The city structuring efforts were also carried out to reduce the number of slum areas by building a new world above the sea. In the agglomeration theory that occurs when the size of the city is expanded it will increase the social welfare of workers as illustrated in the figure below: 

Many efforts can be made to reduce the slowness of the city by natural and artificial methods. In this natural way, the community itself is built to improve the environment, one of which is through agglomeration in the city. According to data from the world development indicator, urban aglomerations have the effect of reducing the slum area. Seen in the plot shows that high urban agglomeration tends to have low population living in slums . Meanwhile there are further efforts made by humans to build and rearrange cities, one of which is to use the city's sea reclamation. this road which should organize the city becomes better and able to improve the welfare of the community, but in reality this road raises many problems such as environmental mitigation problems, corruption, unclear AMDAL, damage to coral reefs, and other social problems. 

This paper tries to analyze how people perceive reclamation in Indonesia by using data from social media twitter. This twitter data is one form of unstructured data that is part of big data, with twitter data we can see sentiment analysis of a government policy and it's easy to get it. We can get public perceptions about reclamation from the data. So that we can draw conclusions and be taken into consideration for based on existing data. One of the interesting findings about the perception of the people in the world about reclamation is that reclamation is so widely accepted abroad, and that the response to reclamation is relatively more positive. Here is a picture that can explain sentiment about reclamation on twitter that uses English: 

The picture above gives us an idea that some of the perceptions of Twitter users have a positive sentiment towards reclamation itself. Reflected on images that tend towards the right approaching things that are positive and preferred. But this perception situation is different in Indonesia. Since the existence of cases such as being caught in corruption due to reclamation, poor, slum in near,  labor sanitation and low mitigation of natural disasters made the image of reclamation in Indonesia to be bad. And this paper tries to explain this by using existing data.

By using 300 data tweets, get the conversation pattern on twitter. The analysis used analysis graph (in mathematics) which consists of 328 nodes and 310 edges in the conversation. the data is then processed through text processing such as text prepositions, tokenisation, morphology with normalisations, stemming and lemma, part of speech and others in data.

B. Agglomeration in twitter conversation 

1) Agglomeration Level individuals 

Before explaining about this agglomeration the author tries to analyze the important accounts in the discussion of this reclamation. The account that is the center of discussion about reclamation, the analysis used is using social network analysis. This analysis uses the theory graph to explain important accounts. The following is a description of the leaves.

In centrality analysis, showing influencial users in the reclamation conversation are as follows: ['ripper_tolol', 'JRX', 'mpuanon', 'KPK', 'antuyyay', 'rilopunklima', 'aniesbaswedan', 'QBAY_suqi', 'duniamanji' , 'sandiuno']. the scores obtained for each account were [0.05198777, 0.05198777, 0.03975535, 0.03669725, 0.02752294, 0.02446483, 0.02446483, 0.02140673, 0.02140673, 0.02140673, 0.02140673]. This score shows the greater the more important the account is in the context of the existing conversation. Then, analysis of closeness centrality shows that the Influencial Users are the following: ['sandiuno', 'abifauzancs', 'binsaragih', 'IsnanZulfida', 'prabowo', 'masifaturohmi', 'AndiArief', 'umarwoto', 'Sutrisn54472450', 'pa_ndu']. Then Influencers, Users, Scores :, [0.09957623, 0.09651627, 0.09422401, 0.09422401, 0.09410637, 0.09363876, 0.092946, 0.09114777, 0.0902745,, 0.09005879]. This number shows that the larger the score, the account user has the ability to spread the conversation about reclamation faster. Because the distance between accounts at the time of conversation is very close.

In the analysis, the picture beside shows the opposite account on analysis of closesness centrality. This account has the ability to slow down the viral reclamation case. The accounts are Influencial Users: ['JRX', 'sandiuno', 'abifauzancs', 'ripper_tolol', 'AndiArief', 'pa_ndu', 'aniesbaswedan', 'addiems', 'jokowi', 'prabowo'] while the score obtained is Influencial, Users, Scores :, [0.10803829, 0.09575798, 0.09430979, 0.08925945, 0.07265916, 0.07229466, 0.06516064, 0.05429542, 0.05149736, 0.05068829] Then the accounts that hold conversations about reclamation are drawn using the theory graph so that obtain the agglomeration results as follows:

In the picture, show the conversation pattern at the level of existing accounts. the more blackened the account becomes the center of the conversation on twitter. But in the picture the location of coordinates does not give meaning to the position of the account on Twitter. Not only is agglomeration in economist theory caused by three things, namely sharing, learning and matching. Sharing activities have been very common for Twitter sharing users about experiences that are not wearing from the impact of reclamation. Then learning activities are also carried out by Twitter users, Twitter users who learn about good reclamation from abroad so that it will affect their view of reclamation itself. Matching activities on Twitter also occur when arguments resemble and match their opinions. 

2) Agglomeration at the community level 

Twitter accounts that have the topic of reclamation form a community that we can detect its existence using the theory graph which is clustered, namely the theory put forward by Kernighan Lin:

Based on the data above shows that the existing community formed 83 communities, with a modularity network of 0.9. the data shows community patterns that occur in conversations about reclamation in Indonesian. The data has limited meaning limitations that occur. From the facts of the data above, it is shown that Twitter media experience also agglomerates naturally. The origin of them talking about reclamation is starting from mentions and retweets, this fact is to give a description of the behavior carried out by twitter users in Indonesia. Then the same thing was done by several users and formed a network in the end they grouped and formed agglomerations on twitter. 

One of the effects of this agglomeration is discussion, topics of perceptions and opinions generated on Twitter. To analyze how people's perceptions of these accounts are about reclamation, the analysis used uses keyword detection. The following are the keywords that are on social media about reclamation as follows:

Most Twitter users discuss recurrence towards rejection, cases due to reclamation such as corruption, and political discussion about reclamation, the Draft Bill on Data Reclamation is very interesting to do more analysis further, about what the public opinion about reclamation? Is it still needed?

to answer the response to the community about us using analysis sentiment. Based on the analysis shows that the reclamation of cases in Indonesia has a bad reputation, in the data shows 60% stated that reclamation in Indonesia has a negative impact on the environment, social, and culture. In contrast to relations abroad, there is a positive sentiment for our lives. So the recommendations put forward are for Indonesia to learn how to make this reclamation not damage the environment and be able to improve the welfare of society. Then we can also see trends in the conversations of reclamation sentiments in Indonesia. The following is the sentiment trend in the reclamation case: 

Interesting finding in this trend, that in the positive trend there is a break on a certain date, this happens all the centers of sentiment in the negative and neutral direction. In general, the trend in sentiment has decreased until now. Another finding is that negative sentiments continue to experience continuity from beginning to end. This indicates that the community consistently provides negative perceptions of reclamation, it will be difficult for the government to educate how to change the main story about reclamation, thus giving us a challenge, how to clean up the reputation of reclamation development. 

to see the code stata and python can visit in https://github.com/studentrohman/slum_in_stata