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Analisis Sentimen Dampak Covid-19 Terhadap Pembatalan Keberangkatan Ibadah Haji Pada Tahun 2020 Mila Kartika; Sudin Saepudin; Dudih Gustian
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 5, No 2 (2021): EDISI SEPTEMBER
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v5i2.392

Abstract

The government through the Ministry of Religious Affairs canceled the departure of pilgrims from Indonesia in 2020. The decision was taken, considering the Covid-19 pandemic still plagues almost all parts of the world, including Indonesia and Saudi Arabia. "Saudi Arabia has never opened access for pilgrims from any country. As a result, the government could no longer have enough time to make its main preparations in the service and protection of Jemaah. Based on this fact, the government decided not to send pilgrims in 2020. Judging from the charts and data recorded from April to December 2020 there was a sharp increase. There were 743,198 confirmed cases of infection, 109,963 cases treated, 22,138 cases died and 611,091 were declared cured. This is a consideration of the government in taking the decision to lower the letter of cancellation of hajj departure in 2020. To retrieve this sentiment data the author took the data methodology from Twitter by using the data retrieval step using orange anaconda tools with the amount of data obtained as many as 670 tweets that have 3 variables that are positive by 37%, negative 12% and neutral 51%. To analyze sentiment data from Twitter the author used 3 classification methods and produced an accuracy value of KnN of 0.507, Random forest of 0.531 and Naïve bayes of 0.532. Based on the results of the analysis conducted by the author that the response or comments of the public to the delay / cancellation of the hajj departure was reaped a neutral response and support the government's move. Of the 3 methods used, the most superior method is the Naïve bayes method because it uses probability and statistics.
Analisis Sentimen Dampak Covid-19 Terhadap Pembatalan Keberangkatan Ibadah Haji Pada Tahun 2020 Mila Kartika; Sudin Saepudin; Dudih Gustian
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 5, No 2 (2021): EDISI SEPTEMBER
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v5i2.392

Abstract

The government through the Ministry of Religious Affairs canceled the departure of pilgrims from Indonesia in 2020. The decision was taken, considering the Covid-19 pandemic still plagues almost all parts of the world, including Indonesia and Saudi Arabia. "Saudi Arabia has never opened access for pilgrims from any country. As a result, the government could no longer have enough time to make its main preparations in the service and protection of Jemaah. Based on this fact, the government decided not to send pilgrims in 2020. Judging from the charts and data recorded from April to December 2020 there was a sharp increase. There were 743,198 confirmed cases of infection, 109,963 cases treated, 22,138 cases died and 611,091 were declared cured. This is a consideration of the government in taking the decision to lower the letter of cancellation of hajj departure in 2020. To retrieve this sentiment data the author took the data methodology from Twitter by using the data retrieval step using orange anaconda tools with the amount of data obtained as many as 670 tweets that have 3 variables that are positive by 37%, negative 12% and neutral 51%. To analyze sentiment data from Twitter the author used 3 classification methods and produced an accuracy value of KnN of 0.507, Random forest of 0.531 and Naïve bayes of 0.532. Based on the results of the analysis conducted by the author that the response or comments of the public to the delay / cancellation of the hajj departure was reaped a neutral response and support the government's move. Of the 3 methods used, the most superior method is the Naïve bayes method because it uses probability and statistics.